A newly published paper quantifies the extent to which automation has contributed to income inequality in the U.S., simply by replacing workers with technology — whether self-checkout machines, call-center systems, assembly-line technology, or other devices. Image: Jose-Luis Olivares, MIT

By Peter Dizikes

When you use self-checkout machines in supermarkets and drugstores, you are probably not — with all due respect — doing a better job of bagging your purchases than checkout clerks once did. Automation just makes bagging less expensive for large retail chains.

“If you introduce self-checkout kiosks, it’s not going to change productivity all that much,” says MIT economist Daron Acemoglu. However, in terms of lost wages for employees, he adds, “It’s going to have fairly large distributional effects, especially for low-skill service workers. It’s a labor-shifting device, rather than a productivity-increasing device.”

A newly published study co-authored by Acemoglu quantifies the extent to which automation has contributed to income inequality in the U.S., simply by replacing workers with technology — whether self-checkout machines, call-center systems, assembly-line technology, or other devices. Over the last four decades, the income gap between more- and less-educated workers has grown significantly; the study finds that automation accounts for more than half of that increase.

“This single one variable … explains 50 to 70 percent of the changes or variation between group inequality from 1980 to about 2016,” Acemoglu says.

The paper, “Tasks, Automation, and the Rise in U.S. Wage Inequality,” is being published in Econometrica. The authors are Acemoglu, who is an Institute Professor at MIT, and Pascual Restrepo PhD ’16, an assistant professor of economics at Boston University.

So much “so-so automation”

Since 1980 in the U.S., inflation-adjusted incomes of those with college and postgraduate degrees have risen substantially, while inflation-adjusted earnings of men without high school degrees has dropped by 15 percent.

How much of this change is due to automation? Growing income inequality could also stem from, among other things, the declining prevalence of labor unions, market concentration begetting a lack of competition for labor, or other types of technological change.

To conduct the study, Acemoglu and Restrepo used U.S. Bureau of Economic Analysis statistics on the extent to which human labor was used in 49 industries from 1987 to 2016, as well as data on machinery and software adopted in that time. The scholars also used data they had previously compiled about the adoption of robots in the U.S. from 1993 to 2014. In previous studies, Acemoglu and Restrepo have found that robots have by themselves replaced a substantial number of workers in the U.S., helped some firms dominate their industries, and contributed to inequality.

At the same time, the scholars used U.S. Census Bureau metrics, including its American Community Survey data, to track worker outcomes during this time for roughly 500 demographic subgroups, broken out by gender, education, age, race and ethnicity, and immigration status, while looking at employment, inflation-adjusted hourly wages, and more, from 1980 to 2016. By examining the links between changes in business practices alongside changes in labor market outcomes, the study can estimate what impact automation has had on workers.

Ultimately, Acemoglu and Restrepo conclude that the effects have been profound. Since 1980, for instance, they estimate that automation has reduced the wages of men without a high school degree by 8.8 percent and women without a high school degree by 2.3 percent, adjusted for inflation. 

A central conceptual point, Acemoglu says, is that automation should be regarded differently from other forms of innovation, with its own distinct effects in workplaces, and not just lumped in as part of a broader trend toward the implementation of technology in everyday life generally.

Consider again those self-checkout kiosks. Acemoglu calls these types of tools “so-so technology,” or “so-so automation,” because of the tradeoffs they contain: Such innovations are good for the corporate bottom line, bad for service-industry employees, and not hugely important in terms of overall productivity gains, the real marker of an innovation that may improve our overall quality of life.

“Technological change that creates or increases industry productivity, or productivity of one type of labor, creates [those] large productivity gains but does not have huge distributional effects,” Acemoglu says. “In contrast, automation creates very large distributional effects and may not have big productivity effects.”

A new perspective on the big picture

The results occupy a distinctive place in the literature on automation and jobs. Some popular accounts of technology have forecast a near-total wipeout of jobs in the future. Alternately, many scholars have developed a more nuanced picture, in which technology disproportionately benefits highly educated workers but also produces significant complementarities between high-tech tools and labor.

The current study differs at least by degree with this latter picture, presenting a more stark outlook in which automation reduces earnings power for workers and potentially reduces the extent to which policy solutions — more bargaining power for workers, less market concentration — could mitigate the detrimental effects of automation upon wages.

“These are controversial findings in the sense that they imply a much bigger effect for automation than anyone else has thought, and they also imply less explanatory power for other [factors],” Acemoglu says.

Still, he adds, in the effort to identify drivers of income inequality, the study “does not obviate other nontechnological theories completely. Moreover, the pace of automation is often influenced by various institutional factors, including labor’s bargaining power.”

Labor economists say the study is an important addition to the literature on automation, work, and inequality, and should be reckoned with in future discussions of these issues.

“Acemoglu and Restrepo’s paper proposes an elegant new theoretical framework for understanding the potentially complex effects of technical change on the aggregate structure of wages,” says Patrick Kline, a professor of economics at the University of California, Berkeley. “Their empirical finding that automation has been the dominant factor driving U.S. wage dispersion since 1980 is intriguing and seems certain to reignite debate over the relative roles of technical change and labor market institutions in generating wage inequality.”

For their part, in the paper Acemoglu and Restrepo identify multiple directions for future research. That includes investigating the reaction over time by both business and labor to the increase in automation; the quantitative effects of technologies that do create jobs; and the industry competition between firms that quickly adopted automation and those that did not.

The research was supported in part by Google, the Hewlett Foundation, Microsoft, the National Science Foundation, Schmidt Sciences, the Sloan Foundation, and the Smith Richardson Foundation.

• PAPER – Tasks, Automation, and the Rise in U.S. Wage Inequality. Daron Acemoglu, and Pascual Restrepo. Econometrica, 90: 1973-2016. https://doi.org/10.3982/ECTA19815

Michael M. Lee

In early 19th-century England, the Luddites rebelled against the introduction of machinery in the textile industry. The Luddites’ name originates from the mythical tale of a weaver’s apprentice called Ned Ludd who, in an act of anger against increasingly dangerous and poor working conditions, supposedly destroyed two knitting machines. Contrary to popular belief, the Luddites were not against technology because they were ignorant or inept at using it (1). In fact, the Luddites were perceptive artisans who cared about their craft, and some even operated machinery. Moreover, they understood the consequences of introducing machinery to their craft and working conditions. Specifically, they were deeply concerned about how technology was being used to shift the balance of power between workers and owners of capital.

The problem is not the advent of technology; the problem is how technology is applied. This is the essence of the intensely polarizing debate on robotic labor. Too often the debate is oversimplified to two opposing factions: the anti-tech pessimist versus the pro-tech optimist. On the one hand, the deeply pessimistic make the case that there will be greatly diminished workers’ rights, mass joblessness, and a widening gulf between socioeconomic classes. On the other hand, the overly optimistic believe that technology will bring better jobs and unbridled economic wealth. The reality is that, although extreme, both sides have valid points. The debate in its present form lacks a middle ground, leaving little room for nuanced and thoughtful discussion. It is simplistic to assume those who are pessimistic towards technological change do not understand the potential of technology as it is incorrect to conclude those who are optimistic about technological change are not thinking about the consequences. Pessimists may fully understand the potential for technological change and still feel that the drawbacks outweigh benefits. Optimists may not want change at any cost, but they feel that the costs are worthwhile.

There are various examples of how the introduction of machines have made industries more efficient and innovative, raising both the quality of work and the quality of output (for example, automated teller machines in banking, automated telephone exchanges in telecommunications, and industrial robots in manufacturing). An important detail in these success stories that is rarely mentioned, however, are timelines. The first industrial revolution did lead to higher levels of urbanization and rises in output; however, crucially, it took several decades before workers saw higher wages. This period of constant wages in the backdrop of rising output per worker is known as Engels’ pause, named after Friedrich Engels, the philosopher who first observed it (2).

Timing matters because, although there will be gains in the long term, there will certainly be losses in the short term. Support for retraining those most at risk of job displacement is needed to bridge this gap. Unfortunately, progress is disappointingly slow on this front. On one level, there are those who are apathetic to the challenges facing the workforce and feel that the loss of jobs is part of the cut and thrust of technological change. On another level, it is possible that there is a lack of awareness of the challenges of transitioning people to a new era of work. We need to bring change and light to both cases, respectively. Those at risk of being displaced by machines need to feel empowered by being a part of the change and not a by-product of change. Moreover, in developing the infrastructure to retrain and support those at risk, we must also recognize that retraining is itself a solution encased in many unsolved problems that include technical, economic, social, and even cultural challenges.

There is more that roboticists should be doing to advance the debate on robotic labor beyond the current obsessive focus on job-stealing robots. First, roboticists should provide a critical and fair assessment of the current technological state of robots. If the public were aware of just how far the field of robotics needs to advance to realize highly capable and truly autonomous robots, then they might be more assured. Second, roboticists should openly communicate the intent of their research goals and aspirations. Understanding that, in the foreseeable future, robotics will be focused on task replacement, not comprehensive job replacement, changes the conversation from how robots will take jobs from workers to how robots can help workers do their job better. The ideas of collaborative robots and multiplicity are not new (3), but they seldom get the exposure that they deserve. Opening an honest and transparent dialogue between roboticists and the general public will go a long way to building a middle ground that will elevate discussion on the future of work.

References

1. J. Sadowski, “I’m a Luddite. You should be one too,” The Conversation, 25 November 2021 [accessed 3 April 2022].
2. R. C. Allen, Engels’ pause: Technical change, capital accumulation, and inequality in the British industrial revolution. Explor. Econ. Hist. 46, 418–435 (2019).
3. K. Goldberg, Editorial multiplicity has more potential than singularity. IEEE Trans. Autom. Sci. Eng. 12, 395 (2015).

From “Lee, M. M., Robots will open more doors than they close. Science Robotics, 7, 65 (2022).” Reprinted with permission from AAAS. Further distribution or republication of this article is not permitted without prior written permission from AAAS.

Sustainable Development Goals relevant for robotics

“The use of robots responds to the UN´s call for action,” says Milton Guerry, President of the International Federation of Robotics (IFR). “The IFR supports the United Nations´ sustainable development goals. There are fantastic new ways in which robots save resources and produce green technologies of the future.”

“The transformation on the way to a sustainable use of resources is proving robotics and automation to be key technologies,” says Dr Susanne Bieller, General Secretary of the International Federation of Robotics. “Intelligent automation reduces production costs: This helps battery technology achieve a breakthrough in e-mobility for example or fuel cells production for hydrogen-power as an alternative to fossil energy. At the same time, highly efficient production technology reduces CO2-emissions.”

Clean energy, industrial innovation and sustainable agriculture are just three examples which show how the use of robots contribute to achieve these UN sustainable development goals:

Energies of the future – electricity and solar heat – Clean Energy (UN´s SDG 7)

Pivoting away from fossil fuel makes solar heat and electricity energies of the future: Solar panels are predicted to provide roughly a third of the world’s total electricity demand by 2050 – says the International Energy Agency. Heat usage makes up for half of the world’s energy usage. Keeping pace with this booming customer demand for solar panels and reflectors mean being able to produce units in greater quantities.

Solar collectors made by Absolicon © Absolicon

Industrial robots are now used as part of an automated factory production line in Sweden. Solar energy company Absolicon has developed what it sees as a gamechanger for the widespread adoption of solar heat as an energy source: A parabolic reflector focuses the solar irradiation on a receiver and turns it into steam with a temperature of up to 160 degrees for the use in industrial production. Almost all industries require heat for production processes. The solar collector´s emission-saving potential: every square meter of a solar thermal collector can produce the equivalent energy of 100 liters of oil.

ABB robot in the production line of Absolicon © ABB Robotics

The automation at Absolicon´s factory in Härnösand using two ABB robots has increased production drastically. Where the company previously produced three solar collectors per day with manual production methods, the newly installed robotic production line now has the capacity to produce a finished collector every six minutes.

“Prepare-to-repair” – Industrial Innovation (UN´s SDG 9)

Prepare-to-repair is a successful strategy for robot manufacturers and their customers to save costs and resources. This takes into account that a robot has an average service life of up to thirty years. Using less parts translating into lower risk of future failure is the first step of this approach. To offer long-term repair to customers, storage of parts is a challenge. In order to keep the large number of spare parts in stock, Japanese robot manufacturer Fanuc e.g. runs a central warehouse for Europe. It is located in Luxembourg and has the size of a football pitch with 600,000 spare parts on stock.

Since every hour of machine downtime costs the customer money, it is oftentimes more resource-efficient to transport the spare parts to the customer and repair the machine on site – rather than to manufacture and ship new machines. Manufacturers like ABB, Fanuc, KUKA or Yaskawa all run dedicated repair centers where thousands of industrial robots are refurbished and upgraded for a second life.

“Robots eliminate chemical agents” – Smart Agriculture (UN´s SDG 2)

In agriculture, new field robots eliminate the use of chemical agents. These agricultural robots travel slowly up and down the rows of crops. Equipped with cameras and artificial intelligence software, they are able to locate weeds and burn them selectively with a laser shot. The new technology does not only completely eliminate the use of herbicides. Organic farmers now have an alternative to a related process called “flaming” using propane torches to kill weeds. Flaming could only operate before crops were planted – otherwise it would also have killed the crops.

The Fraunhofer EZRT research centre and partners equipped a farming robot with navigation technology for mechanical weed control in sugar beets. The BlueBob 2.0 also does the job autonomously, so that farmers can devote their time to tasks with higher added value than manual or chemical weeding. Since manual weeding is a very tedious task for humans, the new technology also helps to improve conditions of farm work. Taking over dirty, dull and dangerous tasks is something robots excel at.

When it comes to the future of intelligent robots, the first question people ask is often: how many jobs will they make disappear? Whatever the answer, the second question is likely to be: how can I make sure that my job is not among them?

In a study just published in Science Robotics, a team of roboticists from EPFL and economists from the University of Lausanne offers answers to both questions. By combining the scientific and technical literature on robotic abilities with employment and wage statistics, they have developed a method to calculate which of the currently existing jobs are more at risk of being performed by machines in the near future. Additionally, they have devised a method for suggesting career transitions to jobs that are less at risk and require smallest retraining efforts.

“There are several studies predicting how many jobs will be automated by robots, but they all focus on software robots, such as speech and image recognition, financial robo-advisers, chatbots, and so forth. Furthermore, those predictions wildly oscillate depending on how job requirements and software abilities are assessed. Here, we consider not only artificial intelligence software, but also real intelligent robots that perform physical work and we developed a method for a systematic comparison of human and robotic abilities used in hundreds of jobs”, says Prof. Dario Floreano, Director of EPFL’s Laboratory of Intelligent Systems, who led the study at EPFL.

The key innovation of the study is a new mapping of robot capabilities onto job requirements. The team looked into the European H2020 Robotic Multi-Annual Roadmap (MAR), a strategy document by the European Commission that is periodically revised by robotics experts. The MAR describes dozens of abilities that are required from current robot or may be required by future ones, ranging, organised in categories such as manipulation, perception, sensing, interaction with humans. The researchers went through research papers, patents, and description of robotic products to assess the maturity level of robotic abilities, using a well-known scale for measuring the level of technology development, “technology readiness level” (TRL).

For human abilities, they relied on the O*net database, a widely-used resource database on the US job market, that classifies approximately 1,000 occupations and breaks down the skills and knowledge that are most crucial for each of them

After selectively matching the human abilities from O*net list to robotic abilities from the MAR document, the team could calculate how likely each existing job occupation is to be performed by a robot. Say, for example, that a job requires a human to work at millimetre-level precision of movements. Robots are very good at that, and the TRL of the corresponding ability is thus the highest. If a job requires enough such skills, it will be more likely to be automated than one that requires abilities such as critical thinking or creativity.

The result is a ranking of the 1,000 jobs, with “Physicists” being the ones who have the lowest risk of being replaced by a machine, and “Slaughterers and Meat Packers”, who face the highest risk. In general, jobs in food processing, building and maintenance, construction and extraction appear to have the highest risk.

“The key challenge for society today is how to become resilient against automation” says Prof. Rafael Lalive. who co-led the study at the University of Lausanne. “Our work provides detailed career advice for workers who face high risks of automation, which allows them to take on more secure jobs while re-using many of the skills acquired on the old job. Through this advice, governments can support society in becoming more resilient against automation.”

The authors then created a method to find, for any given job, alternative jobs that have a significantly lower automation risk and are reasonably close to the original one in terms of the abilities and knowledge they require – thus keeping the retraining effort minimal and making the career transition feasible. To test how that method would perform in real life, they used data from the US workforce and simulated thousands of career moves based on the algorithm’s suggestions, finding that it would indeed allow workers in the occupations with the highest risk to shift towards medium-risk occupations, while undergoing a relatively low retraining effort.

The method could be used by governments to measure how many workers could face automation risks and adjust retraining policies, by companies to assess the costs of increasing automation, by robotics manufacturers to better tailor their products to the market needs; and by the public to identify the easiest route to reposition themselves on the job market.

Finally, the authors translated the new methods and data into an algorithm that predicts the risk of automation for hundreds of jobs and suggests resilient career transitions at minimal retraining effort, publicly accessible at http://lis2.epfl.ch/resiliencetorobots.

This research was funded by the CROSS (Collaborative Research on Science and Society) Program in EPFL’s College of Humanities; by the Enterprise for Society Center at EPFL; as a part of NCCR Robotics, a National Centres of Competence in Research, funded by the Swiss National Science Foundation (SNSF grant number 51NF40_185543); by the European Commission through the Horizon 2020 projects AERIAL-CORE (grant agreement no. 871479) and MERGING (grant agreement no. 869963); and by SNSF grant no. 100018_178878.

• PAPER – How to compete with robots by assessing job automation risks and resilient alternatives Antonio Paolillo, Fabrizio Colella, Nicola Nosengo, Fabrizio Schiano, William Stewart, Davide Zambrano, Isabelle Chappuis, Rafael Lalive, and Dario Floreano. Science Robotics, Vol 7, Issue 65

Photo credits: Studio Number One (SNO)

On March 8, International Women’s Day (IWD) we celebrate the political, socioeconomic and cultural achievements of women and the women right’s movement towards gender equality. “Whilst the social and political rights of women are greater in some places than others, there is no country where gender equality has been achieved” says Mary Evans, professor at the London School of Economics and Political Science in her book “The persistence of gender inequality” (Polity Press 2017). In 2022 this situation has not changed either globally or at the European level as indicated in the EU Gender Equality index for 2020 where the average of the EU is 67.4% and the maximum is Sweden with 83.8%. Although there has been a clear commitment from the European Union on gender equality (specially in innovation and science), there are still structural forms of inequality that must be challenged and changed. It is not the aim of this article to analyse or comment on those, but to show what is being done and is available, especially in the European Union, for us to contribute as individuals and as a community towards gender equality in the field of robotics.

Any journey starts with a first step, so I asked Maja Hadziselimovic, member of the Board of Directors of euRobotics and active United Nations (UN) Generation Equality champion of women and girls in STEM, how a person (no matter the gender) can contribute to gender equality. She told me “There are three actions you can take to be part of Generation Equality: Empower women and girls in science; raise awareness about gender equality and raise the visibility of women in science”. So, let’s explore different activities linked to those actions.

Maja Hadziselimovic (Photo Credits: Marijana Bicvic / UN Women)

Empower women

Although inequalities still exist, the EU has a well-established regulatory framework on gender equality and has made significant progress over the last decades. In March 2020, the European Commission published the EU Gender Equality Strategy for 2020-2025, setting out their commitment towards “a Union where women and men, girls and boys, in all their diversity, are free to pursue their chosen path in life, have equal opportunities to thrive, and can equally participate in and lead our European society.”

Thus, it is not surprising that the EC is making an eligibility criterion for all public bodies, higher education institutions and research organisations to have a Gender Equality Plan (GEP) in order to participate in Horizon Europe. Those looking for extra information on GEPs, can take a look at the work of EU-H2020 project SPEAR (Supporting and Implementing Plans for Gender Equality in Academia and Research) which provides the SPEAR’s compass guide, to help you draft and implement gender equality measures in your Research Performing Organisation (RPO); and GEARING ROLES: Gender Equality Actions in Research Institutions to traNsform Gender ROLES with examples of implementation of GEPs in universities and research organisations, because ensuring effective implementation and avoiding resistance is not always easy.

Empowering women can also be done outside the EC sphere, at a national or local level. This is the case with the work of Ana-Maria Stancu, CEO of Bucharest Robots, founder of the association E-Civis, and member of the Board of Directors of euRobotics. She has a personal and professional mission to empower women and girls in the field of robotics in Romania: “One of the hashtags we are using in our work in Romania is #vinrobotii (robots are coming). We are not using this as a threat, but as a reminder that the robots are here and will develop further. So far, what I can see in my day-to-day work is that women are generally perceived as outsiders in this industry. Every time I talk to a potential client or public authority, the general discussion is led by me, but the minute they have a technical question, they unconsciously turn to my male colleagues. The main problem I see is that most of them do this unintentionally – which makes it even more problematic. It means that this perception is deeply rooted in people’s minds.” This probably sounds familiar to you and gender bias can be found in different fields. A good read in this regard is the book of “Invisible Women” (Chatto & Windus 2019) by Caroline Criado Perez.

Ana-Maria Stancu (Photo Credits: Scuola di Robotica)

Ana-Maria underlines how is important to be persistent in bringing women into the field of robotics: “There is no doubt that the robotics industry will see a tremendous growth in the very near future and will need lots of specialists. We have to make sure that this development will not include only men but will also reflect the activity of women. I believe this day – International Women’s Day – should be a reminder that we have to do more for more women in technical fields like robotics.”

To make sure women are not left behind in the robotics industry, Ana -Maria promotes robotics classes to all children and encourages girls to participate. The E-Civis association also organises robotics and programming summer schools just for girls, so they can learn in a pressure-free environment. From her experience in teaching programming and robotics in public schools in rural areas, she has no doubt that “girls are as interested as boys in this field. Every child loves robots! Let’s use this love to teach and encourage all of them to build them!”

Programming summer school for girls by E-Civis (Photo credits: E-Civis)

For those who do not have the time or opportunity to be involved in mentorship or for this level of commitment, there is one activity that all of us can do in our everyday life, this is to fight micro-aggressions, correct unconscious bias, and avoid what Rebecca Solnit calls “mansplaining” in her book “Men Explain Things To Me” (Haymarket Books, 2014), and provide a sexist-free workplace. An easy and hilarious read for women and men that offers different tools to fight sexism in the workplace is “Feminist Fight Club” by Jessica Bennet (Harper Wave, 2016). Of course, there is a multitude of excellent research papers, policy reports and books you can read, and just having a coffee and listening to or supporting your female colleagues can be already of great help.

If you are a member of euRobotics, I would like you to encourage your female colleagues or employees to present themselves as candidates for the next elections. Our research board is screaming for some women power!

Raise awareness on gender equality

Integrating the gender dimension in research ensures that researchers question gender norms and stereotypes and address the evolving needs and social roles of women and men. It is a matter of improving scientific excellence and increasing the number of women in science, research and innovation. Networks and portals born as FP7 and Horizon 2020 projects, are still in place nowadays to raise awareness on gender equality, provide training and share knowledge. This is the case of GENDERACTION, GE Academy: Gender Equality Academy, GENDER-NET PLUS and GENPORT.

Since 2013, the European Commission publishes every three years the “She Figures” report that monitors the state of gender equality in research and innovation in Europe. The ERA Progress report also monitors the implementation of the objectives set by the EC: gender equality in scientific careers, gender balance in decision making and integration of the gender dimension in R&I.

As an individual you can publicly show your support to gender equality. You can join the Women’s Day march in your city or raise awareness in your social media or your workplace. You can download women’s rights posters and square images by Studio Number One for free here. Be part of the wave of change today.

Raise visibility of women in science

In 2014 a new grassroots community was started to support women in robotics across the world – the Women in Robotics network. Since 2020, the network founded by Andra Keay and Sabine Hauert has grown exponentially and become a non-profit organisation in the U.S. This global community supports women working (or interested in working) in the field of robotics. Their activities include local networking events, outreach, education, mentoring and the promotion of positive role models in robotics. They also publish every year the “list of women in robotics you need to know” on Ada Lovelace Day (13 October). Why not to join the network today? https://womeninrobotics.org/

On International Women’s Day (8 March) The European Commission celebrates the #EUWomen4future campaign and the EU Prize for Women Innovators with a seminar co-hosted by Commissioner Mariya Gabriel and President Roberta Metsola. The event highlights women’s professional achievements in culture, education, sport and science. You can attend the event online here and still use the hashtag.

Women in Robotics publication on Ada Lovelace’s Day. (Photo credit: Women in Robotics)

This article shows a peak of a long list of projects, activities and work concerning gender equality in the area of science and technology (mainly in the EU), and some specifically in robotics. We are in a period of changes, of “Me too”, of breaking ceilings and structural inequality, of building the future we want for our sisters, our daughters, our partners, and most important of all, for ourselves. Don’t be afraid to be challenging, don’t be afraid to be loud.

Jakob Foerster an accredited Machine Learning Research Scientist who has been at the forefront of research on Multi-Agent Learning speaks with interviewer Kegan Strawn.

Dr. Foerster explains why incorporating uncertainty into multi-agent interactions is essential to creating robust algorithms that can operate not only in games but in real-world applications.

Jakob Foerster
Jakob Foerster is an Associate Professor at the University of Oxford. His papers have gained prestigious awards at top machine learning conferences (ICML, AAAI) and have helped push deep multi-agent reinforcement learning to the forefront of AI research.

Jakob previously worked at Facebook AI Research and received his Ph.D. from the University of Oxford under the supervision of Shimon Whiteson. During his Ph.D., Jakob also interned at Google Brain, OpenAI, and DeepMind.

Jakob’s research interests span Deep Multi-Agent Reinforcement Learning, Human-AI Coordination, Emergent Communication, Search, Planning, and Game Theory.

Links

• Download mp3 (36.4 MB)
• Subscribe to Robohub using iTunes, RSS, or Spotify
• Support us on Patreon

Robot and artificial intelligence are poised to increase their influences within our every day lives. (Shutterstock)

By Shane Saunderson

In the mid-1990s, there was research going on at Stanford University that would change the way we think about computers. The Media Equation experiments were simple: participants were asked to interact with a computer that acted socially for a few minutes after which, they were asked to give feedback about the interaction.

Participants would provide this feedback either on the same computer (No. 1) they had just been working on or on another computer (No. 2) across the room. The study found that participants responding on computer No. 2 were far more critical of computer No. 1 than those responding on the same machine they’d worked on.

People responding on the first computer seemed to not want to hurt the computer’s feelings to its face, but had no problem talking about it behind its back. This phenomenon became known as the computers as social actors (CASA) paradigm because it showed that people are hardwired to respond socially to technology that presents itself as even vaguely social.

The CASA phenomenon continues to be explored, particularly as our technologies have become more social. As a researcher, lecturer and all-around lover of robotics, I observe this phenomenon in my work every time someone thanks a robot, assigns it a gender or tries to justify its behaviour using human, or anthropomorphic, rationales.

What I’ve witnessed during my research is that while few are under any delusions that robots are people, we tend to defer to them just like we would another person.

Social tendencies

While this may sound like the beginnings of a Black Mirror episode, this tendency is precisely what allows us to enjoy social interactions with robots and place them in caregiver, collaborator or companion roles.

The positive aspects of treating a robot like a person is precisely why roboticists design them as such — we like interacting with people. As these technologies become more human-like, they become more capable of influencing us. However, if we continue to follow the current path of robot and AI deployment, these technologies could emerge as far more dystopian than utopian.

The Sophia robot, manufactured by Hanson Robotics, has been on 60 Minutes, received honorary citizenship from Saudi Arabia, holds a title from the United Nations and has gone on a date with actor Will Smith. While Sophia undoubtedly highlights many technological advancements, few surpass Hanson’s achievements in marketing. If Sophia truly were a person, we would acknowledge its role as an influencer.

However, worse than robots or AI being sociopathic agents — goal-oriented without morality or human judgment — these technologies become tools of mass influence for whichever organization or individual controls them.

If you thought the Cambridge Analytica scandal was bad, imagine what Facebook’s algorithms of influence could do if they had an accompanying, human-like face. Or a thousand faces. Or a million. The true value of a persuasive technology is not in its cold, calculated efficiency, but its scale.

Seeing through intent

Recent scandals and exposures in the tech world have left many of us feeling helpless against these corporate giants. Fortunately, many of these issues can be solved through transparency.

There are fundamental questions that are important for social technologies to answer because we would expect the same answers when interacting with another person, albeit often implicitly. Who owns or sets the mandate of this technology? What are its objectives? What approaches can it use? What data can it access?

Since robots could have the potential to soon leverage superhuman capabilities, enacting the will of an unseen owner, and without showing verbal or non-verbal cues that shed light on their intent, we must demand that these types of questions be answered explicitly.

As a roboticist, I get asked the question, “When will robots take over the world?” so often that I’ve developed a stock answer: “As soon as I tell them to.” However, my joke is underpinned by an important lesson: don’t scapegoat machines for decisions made by humans.

I consider myself a robot sympathizer because I think robots get unfairly blamed for many human decisions and errors. It is important that we periodically remind ourselves that a robot is not your friend, your enemy or anything in between. A robot is a tool, wielded by a person (however far removed), and increasingly used to influence us.

Shane receives funding from the Natural Sciences and Engineering Research Council of Canada (NSERC). He is affiliated with the Human Futures Institute, a Toronto-based think tank.

This article appeared in The Conversation.

I took part in the first panel at the BSI conference The Digital World: Artificial Intelligence.  The subject of the panel was AI Governance and Ethics. My co-panelist was Emma Carmel, and we were expertly chaired by Katherine Holden.

Emma and I each gave short opening presentations prior to the Q&A. The title of my talk was Why is Ethical Governance in AI so hard? Something I’ve thought about alot in recent months.

Here are the slides exploring that question.

And here are my words.

Early in 2018 I wrote a short blog post with the title Ethical Governance: what is it and who’s doing it? Good ethical governance is important because in order for people to have confidence in their AI they need to know that it has been developed responsibly. I concluded my piece by asking for examples of good ethical governance. I had several replies, but none were nominating AI companies.

So. why is it that 3 years on we see some of the largest AI companies on the planet shooting themselves in the foot, ethically speaking? I’m not at all sure I can offer an answer but, in the next few minutes, I would like to explore the question: why is ethical governance in AI so hard? 

But from a new perspective. 

Slide 2

In the early 1970s I spent a few months labouring in a machine shop. The shop was chaotic and disorganised. It stank of machine oil and cigarette smoke, and the air was heavy with the coolant spray used to keep the lathe bits cool. It was dirty and dangerous, with piles of metal swarf cluttering the walkways. There seemed to be a minor injury every day.

Skip forward 40 years and machine shops look very different. 

Slide 3

So what happened? Those of you old enough will recall that while British design was world class – think of the British Leyland Mini, or the Jaguar XJ6 – our manufacturing fell far short. “By the mid 1970s British cars were shunned in Europe because of bad workmanship, unreliability, poor delivery dates and difficulties with spares. Japanese car manufacturers had been selling cars here since the mid 60s but it was in the 1970s that they began to make real headway. Japanese cars lacked the style and heritage of the average British car. What they did have was superb build quality and reliability”*.

What happened was Total Quality Management. The order and cleanliness of modern machine shops like this one is a strong reflection of TQM practices. 

Slide 4

In the late 1970s manufacturing companies in the UK learned – many the hard way – that ‘quality’ is not something that can be introduced by appointing a quality inspector. Quality is not something that can be hired in.

This word cloud reflects the influence from Japan. The words Japan, Japanese and Kaizen – which roughly translates as continuous improvement – appear here. In TQM everyone shares the responsibility for quality. People at all levels of an organization participate in kaizen, from the CEO to assembly line workers and janitorial staff. Importantly suggestions from anyone, no matter who, are valued and taken equally seriously.

Slide 5

In 2018 my colleague Marina Jirotka and I published a paper on ethical governance in robotics and AI. In that paper we proposed 5 pillars of good ethical governance. The top four are:

• have an ethical code of conduct, 
• train everyone on ethics and responsible innovation,
• practice responsible innovation, and
• publish transparency reports.

The 5th pillar underpins these four and is perhaps the hardest: really believe in ethics.

Now a couple of months ago I looked again at these 5 pillars and realised that they parallel good practice in Total Quality Management: something I became very familiar with when I founded and ran a company in the mid 1980s.

Slide 6 

So, if we replace ethics with quality management, we see a set of key processes which exactly parallel our 5 pillars of good ethical governance, including the underpinning pillar: believe in total quality management.

I believe that good ethical governance needs the kind of corporate paradigm shift that was forced on UK manufacturing industry in the 1970s.

Slide 7

In a nutshell I think ethics is the new quality

Yes, setting up an ethics board or appointing an AI ethics officer can help, but on their own these are not enough. Like Quality, everyone needs to understand and contribute to ethics. Those contributions should be encouraged, valued and acted upon. Nobody should be fired for calling out unethical practices.

Until corporate AI understands this we will, I think, struggle to find companies that practice good ethical governance. 

Quality cannot be ‘inspected in’, and nor can ethics.

Thank you.

Notes.

[1] I’m quoting here from the excellent history of British Leyland by Ian Nicholls.

[2] My company did a huge amount of work for Motorola and – as a subcontractor – we became certified software suppliers within their six sigma quality management programme.

[3] It was competitive pressure that forced manufacturing companies in the 1970s to up their game by embracing TQM. Depressingly the biggest AI companies face no such competitive pressures, which is why regulation is both necessary and inevitable.

I’ve worried about the sustainability of consumer electronics for some time, and, more recently, the shocking energy costs of big AI. But the climate emergency has also caused me to think hard about the sustainability of robots. In recent papers we have defined responsible robotics as

… the application of Responsible Innovation in the design, manufacture, operation, repair and end-of-life recycling of robots, that seeks the most benefit to society and the least harm to the environment.

I will wager that few robotics manufacturers – even the most responsible – pay much attention to repairability and recyclability of their robots. And, I’m ashamed to say, very little robotics research is focused on the development of sustainable robots. A search on google scholar throws up a handful of excellent papers detailing work on upcycled and sustainable robots (2018), sustainable robotics for smart cities (2018), green marketing of sustainable robots (2019), and sustainable soft robots (2020).

I was therefore delighted when, a few weeks ago, my friend and colleague Michael Fisher, drafted a proposal for a new standard on Sustainable Robotics. The proposal received strong support from the BSI robotics committee. Here is the formal notice requesting comments on Michael’s proposal: BS XXXX Guide to the Sustainable Design and Application of Robotic Systems.

So what would make a robot sustainable? In my view it would have to be:

• Made from sustainable materials. This means the robot should, as far as possible, use recycled materials (plastics or metals), or biodegradable materials like wood. Any new materials should be ethically sourced.
• Low energy. The robot should be designed to use as little energy as possible. It should have energy saving modes. If an outdoor robot then is should use solar cells and/or hydrogen cells when they become small enough for mobile robots. Battery powered robots should always be rechargeable.
• Repairable. The robot would be designed for ease of repair using modular, replaceable parts as much as possible – especially the battery. Additionally the manufacturers should provide a repair manual so that local workshops could fix most faults.
• Recyclable. Robots will eventually come to the end of their useful life, and if they cannot be repaired or recycled we risk them being dumped in landfill. To reduce this risk the robot should be designed to make it easy re-use parts, such as electronics and motors, and re-cycle batteries, metals and plastics.

These are, for me, the four fundamental requirements, but there are others. The BSI proposal adds also the environmental effects of deployment (it is unlikely we would consider a sustainable robot designed to spray pesticides as truly sustainable), or of failure in the field. Also the environmental effect of maintenance; cleaning materials, for instance. The proposal also looks toward sustainable, upcyclable robots as part of a circular economy.

This is Ecobot III, developed some years ago by colleagues in the Bristol Robotics Lab’s Bio-energy group. The robot runs on electricity extracted from biomass by 48 microbial fuel cells (the two concentric brick coloured rings). The robot is 90% 3D printed, and the plastic is recyclable.

I would love to see, in the near term, not only a new standard on Sustainable Robotics as a guide (and spur) for manufacturers, but the emergence of Sustainable Robotics as a thriving new sub-discipline in robotics.

In this technical talk, Chad Jenkins from the University of Michigan posed the following question: “who will pay the cost for the likely mistakes and potential misuse of AI systems?” As he states, “we are increasingly seeing how AI is having a pervasing impact on our lives, both for good and for bad. So, how do we ensure equal opportunity in science and technology?”

Abstract

It would be great to talk about the many compelling ideas, innovations, and new questions emerging in robotics research. I am fascinated by the ongoing NeRF Explosion, prospects for declarative robot programming by demonstration, and potential for a reemergence of probabilistic generative inference. However, there is a larger issue facing our intellectual enterprise: who will pay the cost for the likely mistakes and potential misuse of AI systems? My nation is poised to invest billions of dollars to remain the leader in artificial intelligence as well as quantum computing. This investment is critically needed to reinvigorate the science that will shape our future. In order to get the most from this investment, we have to create an environment that will produce innovations that are not just technical advancements but will also benefit and uplift everybody in our society. We are increasingly seeing how AI is having a pervasing impact on our lives, both for good and for bad. So, how do we ensure equal opportunity in science and technology? It starts with how we invest in scientific research. Currently, when we make investments, we only think about technological advancement. Equal opportunity is a non-priority and, at best, a secondary consideration. The fix is simple really — and something we can do almost immediately: we must start enforcing existing civil rights statutes for how government funds are distributed in support of scientific advancement. This will mostly affect universities, as the springwell that generates the intellectual foundation and workforce for other organizations that are leading the way in artificial intelligence.This talk will explore the causes of systemic inequality in AI, the impact of this inequity within the field of AI and across society today, and offer thoughts for the next wave of AI inference systems for robotics that could provide introspectability and accountability. Ideas explored build upon the BlackInComputing.org open letter and “Before we put $100 billion into AI…” opinion. Equal opportunity for anyone requires equal opportunity for everyone.

Biography

Odest Chadwicke Jenkins, Ph.D., is a Professor of Computer Science and Engineering and Associate Director of the Robotics Institute at the University of Michigan. Prof. Jenkins earned his B.S. in Computer Science and Mathematics at Alma College (1996), M.S. in Computer Science at Georgia Tech (1998), and Ph.D. in Computer Science at the University of Southern California (2003). He previously served on the faculty of Brown University in Computer Science (2004-15). His research addresses problems in interactive robotics and human-robot interaction, primarily focused on mobile manipulation, robot perception, and robot learning from demonstration. Prof. Jenkins has been recognized as a Sloan Research Fellow and is a recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE). His work has also been supported by Young Investigator awards from the Office of Naval Research (ONR), the Air Force Office of Scientific Research (AFOSR) and the National Science Foundation (NSF). Prof. Jenkins is currently serving as Editor-in-Chief for the ACM Transactions on Human-Robot Interaction. He is a Fellow of the American Association for the Advancement of Science and Association for the Advancement of Artificial Intelligence, and Senior Member of the Association for Computing Machinery and the Institute of Electrical and Electronics Engineers. He is an alumnus of the Defense Science Study Group (2018-19).

Featuring Guest Panelist: Sarah Brown, Hadas Kress-Gazit, Aisha Walcott

The next technical talk will be delivered by Raia Hadsell from DeepMind, and it will take place on March 26 at 3pm EST. Keep up to date on this website.

The European Commission has published a report by an independent group of experts on Ethics of Connected and Automated Vehicles (CAVs). This report advises on specific ethical issues raised by driverless mobility for road transport. The report aims to promote a safe and responsible transition to connected and automated vehicles by supporting stakeholders in the systematic inclusion of ethical considerations in the development and regulation of CAVs.

The report presents 20 ethical recommendations concerning the future development and use of CAVs based on ethical and legal principles. The recommendations are discussed in the context of three topics areas:

Road Safety, Risk, Dilemmas

Improvements in safety achieved by CAVs should be publicly demonstrable and monitored through solid and shared scientific methods and data; these improvements should be achieved in compliance with basic ethical and legal principles, such as a fair distribution of risk and the protection of basic rights, including those of vulnerable users; these same considerations should apply to dilemma scenarios.

Data and algorithm Ethics: Privacy, Fairness, Explainability

The acquisition and processing of static and dynamic data by CAVs should safeguard basic privacy rights, should not create discrimination between users, and should happen via processes that are accessible and understandable to the subjects involved.

Responsibility

Considering who should be liable for paying compensation following a collision is not sufficient; it is also important to make different stakeholders willing, able and motivated to take responsibility for preventing undesirable outcomes and promoting societally beneficial outcomes of CAVs, that is creating a culture of responsibility for CAVs.

Read the full report.

Participants James Mickens, Ludovic Righetti, Aude Billard, Melonee Wise and moderator Hallie Siegel at the ICRA 2019 “Debates on the Future of Robotics Research” workshop

By Brian Wang, Sarah Tang, Jaime Fernandez Fisac, Felix von Drigalski, Lee Clement, Matthew Giamou, Sylvia Herbert, Jonathan Kelly, Valentin Pertroukhin, and Florian Shkurti

As the field of robotics matures, our community must grapple with the multifaceted impact of our research; in this article, we describe two previous workshops hosting robotics debates and advocate for formal debates to become an integral, standalone part of major international conferences, whether as a plenary session or as a parallel conference track.

As roboticists build increasingly complex systems for applications spanning manufacturing, personal assistive technologies, transportation and others, we face not only technical challenges, but also the need to critically assess how our work can advance societal good. Our rapidly growing and uniquely multidisciplinary field naturally cultivates diverse perspectives, and informal dialogues about our impact, ethical responsibilities, and technologies. Indeed, such discussions have become a cornerstone of the conference experience, but there has been relatively little formal programming in this direction at major technical conferences like the IEEE International Conference on Robotics and Automation (ICRA) and Robotics: Science and Systems (RSS) Conference.

To fill this void, we organized two workshops entitled “Debates on the Future of Robotics Research” at ICRA 2019 and 2020, inspired by a similar workshop at the 2018 International Conference on Machine Learning (ICML). In these workshops, panellists from industry and academia debated key issues in a structured format, with groups of two arguing either “for” or “against” resolutions relating to these issues. The workshops featured three 80-minute sessions modelled roughly after the Oxford Union debate format, consisting of:

1. An initial audience poll asking whether they “agree” or “disagree” with the proposed resolution
2. Opening statements from all four panellists, alternating for and against
3. Moderated discussion including audience questions
4. Closing remarks
5. A final audience poll
6. Panel discussion and audience Q&A

The “Debates” workshops attracted approximately 400 attendees in 2019 and 1100 in 2020 (ICRA 2020 was held virtually in response to the COVID-19 pandemic). In some instances, panellists took positions out of line with their personally held beliefs and nonetheless swayed audience members to their side, with audience polls displaying notable shifts in majority opinion. These results demonstrate the ability of debate to engage our community, elucidate complex issues, and challenge ourselves to adopt new ways of thinking. Indeed, the popularity of the format is on the rise within the robotics community, as other recent workshops have also adopted a debates format — for instance, the 2${}^{nd}$ Workshop on Closing the Reality Gap in Sim2Real Transfer for Robotics or Soft Robotics Debates).

Audience agree/disagree poll results before and after each debate of the virtual “Debates” workshop at the 2020 IEEE International Conference on Robotics and Automation (ICRA)

Given this positive community response, we argue that major robotics conferences should begin to organize structured debates as a core element of their programming. We believe a debate-style plenary session or parallel track would provide a number of benefits to conference attendees and the wider robotics community:

• Exposure to ideas: Attendees would have more opportunities to be exposed to unfamiliar ideas and perspectives in a well-attended plenary session, while minimizing overlap with technical sessions.
• Equity: Panellists and moderators no longer have to choose between accepting a debate invitation, accepting a workshop speaking engagement, supporting their students’ workshop presentations, and/or hosting a workshop of their own. Some of these conflicting responsibilities disproportionately affect early career researchers.
• Inclusion: Conference organizers have the discretion to provide travel support, enabling panellists and moderators from a wider range of countries, career stages, and backgrounds to participate.
• Impact: A notable Science Robotics article cited debates as a necessary driver of progress towards solving robotics’ grand challenges. By facilitating structured and critical self-reflection, a widely accessible debates plenary could help identify avenues for future work and move the field forward.

The driving force behind such an event should be a diverse debates committee responsible for inviting representatives within and outside academia, with different research interests, at different career stages, and from different parts of the world. The committee will also lead the selection of appropriate debate topics. Historically, our debate propositions revolved around three key questions: “what problems should we solve?”, “how should we solve them?”, and “how do we measure success and progress?”.

Recent high-profile shutterings of several prominent robotics companies testify to the importance of identifying the right problem. How do we transform our research into products that provide value to end-users, while keeping in mind our environmental and economic impact? How can we quickly introspect, learn from, and pivot in response to failures, as well as avoid repeating past mistakes? Our 2020 resolution, “robots designed for personal or household use have failed because of fundamental misunderstandings of Human-Robot Interaction (HRI),” provided an opportunity to discuss such questions.

As a multidisciplinary field drawing on advances in machine learning, computer science, mechanical design, control, optimization, biology, and more, robotics has a wealth of tools at its disposal. A central problem in robotics research is then to identify the right tool for the right problem. Our 2019 debate proposition, “The pervasiveness of deep learning in robotics research is an impediment to gaining scientific insights into robotics problems.”, probed the ascendance of data-driven approaches to robotics problems, and attracted a large audience.

In addition to identifying the right problems and the right tools to solve them, it is equally important to discuss how best to evaluate and compare proposed solutions. A central tension in robotics research is how to strike a balance between accessible and replicable benchmark datasets, and real world experiments. Our 2020 debate proposition, “Robotics research is over reliant on benchmark datasets and simulation”, challenged the audience to consider how to rigorously and accessibly evaluate the performance of robotic systems without overfitting to a particular benchmark. Indeed, failures to adequately evaluate robotics algorithms have already led to tragic loss of life, underscoring the importance of establishing common standards for measuring the performance and safety of our technologies in the context of rapid commercialization and real-world deployments. These standards must be informed by regular and rigorous critical reflection on our ethical obligations as researchers, practitioners and policymakers. The fact that our 2019 debate proposition, “Robotics needs a similar level of regulation and certification as other engineering disciplines (e.g., aviation), even if this results in slower technological innovation”, attracted and resonated with a significant audience is evidence of demand for structured discussion of these topics.

As robotics technologies continue to move from the research laboratory to the real world, we believe there should be room for debate at major robotics conferences. Enshrining debate as a core element of major robotics conferences will serve to create opportunities for self-reflection, establish institutional memory, and drive the field forward.

For more information, please see https://roboticsdebates.org/.

The UK Robotics Growth Partnership (RGP) aims to set the conditions for success to empower the UK to be a global leader in Robotics and Autonomous Systems whilst delivering a smarter, safer, more prosperous, sustainable and competitive UK. The aim is for smart machines to become ubiquitous, woven into the fabric of society, in every sector, every workplace, and at home. If done right, this could lead to increased productivity, and improved quality of life. It could enable us to meet Net Zero targets, and support workers as their roles transition from menial tasks.

One thing that’s striking is that although robotics holds so much potential, they are not yet ready. The covid crisis has made this very clear. If it had been ready, we could have – at scale – deployed robots to sanitise hospitals, enable doctor-patient communication through telepresence, or connect patients with loved ones. Robots could have produced, organised, and delivered much needed samples, tests, PPE, medicine, and food across the UK. And many businesses could be reopening with a robotic interface. Robots could have powered a low-touch economy, where activities continue even when humans can’t be in close physical contact, driving recovery and resilience.

For the past year, we’ve been thinking about this at the RGP. How could we have done things better? What would it take to make an armada of disinfecting robots for a Covid pop-up hospital (called a Nightingale hospital in the UK)? Ideally we would have been able to log into a digital twin of the hospital, port in a model of a robot platform from a database, work-up a solution in the virtual world, and readily port it to an actual testbed to demonstrate its function in the physical world. We could then trial the solution in a living lab, maybe a dedicated Nightingale, before scaling up the solution to other hospitals. Others developing telepresence robots could follow the same methodology, checking that their solutions are interoperable, and working within the same virtual and physical environments. 

We have many bits of the puzzle in the UK – great research and industry, plus government buy-in, but what we need is to bring this together.

To explore this further, we spent the last months hosting ThinkIns with Tortoise Media to get feedback from Academia, Industry, Policy and the Public. You can read all the blog posts and watch the videos here:

The future of smart machines: reflections from academia https://ukrgp.org/the-future-of-smart-machines-reflections-from-academia/

Building an ecosystem to make useful robots https://ukrgp.org/building-an-ecosystem-to-make-useful-robots/

Musings with the public about their future with smart machines https://ukrgp.org/musings-with-the-public-about-their-future-with-smart-machines/

Keeping up with the pace of change – positioning the UK as a leader in smart machines https://ukrgp.org/keeping-up-with-the-pace-of-change-positioning-the-uk-as-a-leader-in-smart-machines/

Below are some preliminary findings.

From digital twins to living labs

In his blog, James Kell from Rolls Royce says “The UK is small enough to collaborate well, but big enough to be a global leader. But to be successful we will need new tools, in particular better, cheaper digital twins – synthetic environments where we can develop and test new approaches before we test them in real world environments on our $35m engines.”

Professor Samia Nefti-Meziani from Salford University had a similar comment “Society needs better tools to support a sustainable future, with a new network of synthetic environments to build and fine-tune new technologies, to ensure they work in the real world and to reduce the time from their inception to deployment from years to months. Digital platforms and accessible, open-source software tools will empower SMEs, academics and the public sector to engage and benefit from these new solutions.”

Collaboration across academia and industry

As Samia highlights, “Collaboration was a recurring theme in the ThinkIns, with academic and industry partnerships essential to ensure we target the most pressing challenges and drive innovation in the sectors that need its solutions. As smart machines become more capable and cheaper, their adoption and development within the UK business ecosystem will broaden across sectors and applications.”

Government support to unlock incentives

James continued, “Collaboration needs coordination: Government is critical to convening and leading, creating new ways and incentives to work better together. The new tools will only equip our researchers and SMEs to accelerate product development, validation and speed to market if they can trust each other and all both contribute and benefit. We need new ways for big industry (companies like mine) to have their challenges understood and find new partners to work with, to learn together to develop solutions and put them in place quicker. And if we join up the academics and link across our innovation infrastructure and existing test areas, we will accelerate the adoption of smart machines and unleash the multiple benefits they bring.”  

The human element

‘Taking the public with us’, is critical to mass adoption, says Samia. “Key will be:

– Involving the public in co-creating research and industry ambitions, to help them understand and engage with what RAS can offer

– Engaging with those who distrust RAS, to understand their concerns and gain their confidence

– Improving RAS education and lifelong learning, so those with interest and capability can be trained in RAS and directly involved in shaping their future.”

“The sector must ‘show its workings’ and be clear of the problems and challenges to prioritise. Ensuring standards and protocols are developed to protect the input of the public and the quality of the outputs is vital to buy-in in the long-term.”

David Bisset, a Robotics Consultant, commented on the Public session, highlighting that “Smart Machines are already with us, cars, aeroplanes, vacuum cleaners we don’t call them robots but they all use that technology. To make them work requires many skills; industrial design, AI people, sensor experts and interaction designer… and many more. At a human level we need to be able to trust, to know it’s built right and safe.”

He highlights the issue of “Tech Wash” mentioned by the public. “Is ‘smart machine’ just some clever rebranding? The needless selling of technology as a solution to every senior manager’s need to outshine their peers? We need to stop and think about the consequences of forcing through technology driven organisational change without evidence and stop needless disruption. We need to know these things will work!”

Overall, to make smart machines a success, we need to bring the discussion to a human level, to where this makes a difference to people.

Bringing it all together

Rob Buckingham, Head of RACE at the UK Atomic Energy Authority commented on the policy ThinkIn “Robotics includes both tools that are physically discrete from us and physical augmentation. In either case the interface between person and machine is going to be a field of rapid development driven at least in part by gaming and zooming.

The much bigger part is the informed discussion with people, with society, about the world we want to live in. Are we Canute (spoiler alert – it doesn’t end well) or are we the voice of sustainable democracy that values both people and nature?

I think Living Labs are going to sit at the heart of this… physical places where we explore the issues and opportunities together. In my field of nuclear, mock-ups have always been sensible because experimenting with the real thing is only allowed in exceptional circumstances. My hope is that we will invest in many Living Labs around the country that enable us, collectively, to explore the benefits and unintended consequences of our creativity. Of course, we might expect all of the Living Labs to be connected by data and the management of data; indeed we might expect common tech platforms and digital models of ‘nearly everything’ to be one of the highest value spin-offs.”

The gendering of robots is something I’ve found fascinating since I first started building robots out of legos with my brother. We all ascribe character to robots, consciously or not, even when we understand exactly how robots work. Until recently we’ve been able to write this off as science fiction stuff, because real robots were boring industrial arms and anything else was fictional. However, since 2010, robots have been rolling out into the real world in a whole range of shapes, characters and notably, stereotypes. My original research on the naming of robots gave some indications as to just how insidious this human tendency to anthropomorphize and gender robots really is. Now we’re starting to face the consequences and it matters.

Firstly, let’s consider that many languages have gendered nouns, so there is a preliminary linguistic layer of labelling, ahead of the naming of robots, which if not defined, then tends to happen informally. The founders of two different robot companies have told me that they know when their robot has been accepted in a workplace by when it’s been named by teammates, and they deliberately leave the robot unnamed. Whereas some other companies focus on a more nuanced brand name such as Pepper or Relay, which can minimize gender stereotypes, but even then the effects persist.

Because with robots the physical appearance can’t be ignored and often aligns with ideas of gender. Next, there is the robot voice. Then, there are other layers of operation which can affect both a robot’s learning and its response. And finally, there is the robot’s task or occupation and its socio-cultural context.

Names are both informative and performative. We can usually ascribe a gender to a named object. Similarly, we can ascribe gender based on a robot’s appearance or voice, although it can differ in socio-cultural contexts.

Astro Boy original comic and Pepper from SoftBank Robotics

The robot Pepper was designed to be a childlike humanoid and according to SoftBank Robotics, Pepper is gender neutral. But in general, I’ve found that US people tend to see Pepper as female helper, while Asian people are more likely to see Pepper as a boy robot helper. This probably has something to do with the popularity of Astro Boy (Mighty Atom) from 1952 to 1968.

One of the significant issues with gendering robots is that once embodied, individuals are unlikely to have the power to change the robot that they interact with. Even if they rename it, recostume it and change the voice, the residual gender markers will be pervasive and ‘neutral’ will still elicit a gender response in everybody.

This will have an impact on how we treat and trust robots. This also has much deeper social implications for all of us, not just those who interact with robots, as robots are recreating all of our existing gender biases. And once the literal die is cast and robots are rolling out of a factory, it will be very hard to subsequently change the robot body.

Interestingly, I’m noticing a transition from a default male style of robot (think of all the small humanoid fighting, dancing and soccer playing robots) to a default female style of robot as the service robotics industry starts to grow. Even when the robot is simply a box shape on wheels, the use of voice can completely change our perception. One of the pioneering service robots from Savioke, Relay, deliberately preselected a neutral name for their robot and avoided using a human voice completely. Relay makes sounds but doesn’t use words. Just like R2D2, Relay expresses character through beeps and boops. This was a conscious, and significant, design choice for Savioke. Their preliminary experimentation on human-robot interaction showed that robots that spoke were expected to answer questions, and perform tasks at a higher level of competency than a robot that beeped.

Relay from Savioke delivering at Aloft Hotel

Not only did Savioke remove the cognitive dissonance of having a robot seem more human that it really is, but they removed some of the reiterative stereotyping that is starting to occur with less thoughtful robot deployments. The best practice for designing robots for real world interaction is to minimize human expressivity and remove any gender markers. (more about that next).

The concept of ‘marked’ and ‘unmarked’ arose in linguistics in the 1930s, but we’ve seen it play out in Natural Language Processing, search and deep learning repeatedly since then, perpetuating, reiterating and exaggerating the use of masculine terminology as the default, and feminine terminology used only in explicit (or marked) circumstances. Marked circumstances almost always relate to sexual characteristics or inferiority within power dynamics, rather than anything more interesting.

An example of unmarked or default terminology is the use of ‘man’ to describe people, but ‘woman’ to only describe a subset of ‘man’. This is also commonly seen in the use of a female specifier on a profession, ie. female police officer, female president, or female doctor. Otherwise, in spite of there being many female doctors, the search will return male examples, call female doctors he, or miscategorize them as nurse. We are all familiar with those mistakes in real life but had developed social policies to reduce the frequency of them. Now AI and robotics are bringing the stereotype back.

And so it happens that the ‘neutral’ physical appearance of robots is usually assumed to be male, rather than female, unless the robot has explicit female features. Sadly, female robots mean either a sexualized robot, or a robot performing a stereotypically female role. This is how people actually see and receive robots unless a company, like Savioke, consciously refrains from triggering our stereotypically gendered responses.

I can vouch for the fact that searching for images using the term “female roboticists”, for example, always presents me with lots of men building female robots instead. It will take a concerted effort to change things. Robot builders have the tendency to give our robots character. And unless you happen to be a very good (and rich) robotics company, there is also no financial incentive to degender robots. Quite the opposite. There is financial pressure to take advantage of our inherent anthropomorphism and gender stereotypes.

In The Media Equation in 1996, Clifford Reeves and Byron Nass demonstrated how we all attributed character, including gender, to our computing machines, and that this then affected our thoughts and actions, even though most people consciously deny conflating a computer with a personality. This unconscious anthropomorphizing can be used to make us respond differently, so of course robot builders will increasingly utilize the effect as more robots enter society and competition increases.

Can human beings relate to computer or television programs in the same way they relate to other human beings? Based on numerous psychological studies, this book concludes that people not only can but do treat computers, televisions, and new media as real people and places. Studies demonstrate that people are “polite” to computers; that they treat computers with female voices differently than “male” ones; that large faces on a screen can invade our personal space; and that on-screen and real-life motion can provoke the same physical responses.

The Media Equation

The history of voice assistants shows a sad trend. These days, they are all female, with the exception of IBM Watson, but then Watson occupies a different ecosystem niche. Watson is an expert. Watson is the doctor to the rest of our subservient, map reading, shopping list helpful nurses. By default, unless you’re in Arabia, your voice assistant device will have a female voice. You have to go through quite a few steps to consciously change it and there are very few options. In 2019, Q, a genderless voice assistant was introduced, however I can’t find it offered on any devices yet.

And while it may be possible to upload a different voice to a robot, there’s nothing we can do if the physical design of the robot evokes gender. Alan Winfield wrote a very good article “Should robots be gendered?” here on Robohub in 2016, in which he outlines three reasons that gendered robots are a bad idea, all stemming from the 4th of the EPSRC Principles of Robotics, that robots should be transparent in action, rather than capitalizing on the illusion of character, so as not to influence vulnerable people.

Robots are manufactured artefacts: the illusion of emotions and intent should not be used to exploit vulnerable users.

EPSRC Principles of Robotics

My biggest quibble with the EPSRC Principles is underestimating the size of the problem. By stating that vulnerable users are the young or the elderly, the principles imply that the rest of us are immune from emotional reaction to robots, whereas Reeves and Nass clearly show the opposite. We are all easily manipulated by our digital voice and robot assistants. And while Winfield recognizes that gender queues are powerful enough to elicit a response in everybody, he only sees the explicit gender markers rather than understanding that unmarked or neutral seeming robots also elicit a gendered response, as ‘not female’.

So Winfield’s first concern is emotional manipulation for vulnerable users (all of us!), his second concern is anthropomorphism inducing cognitive dissonance (over promising and under delivering), and his final concern is that the all the negative stereotypes contributing to sexism will be reproduced and reiterated as normal through the introduction of gendered robots in stereotyped roles (it’s happening!). These are all valid concerns, and yet while we’re just waking up to the problem, the service robot industry is growing by more than 30% per annum.

Where the growth of the industrial robotics segment is comparatively predictable, the world’s most trusted robotics statistics body, the International Federation of Robotics is consistently underestimating the growth of the service robotics industry. In 2016, the IFR predicted 10% growth for professional service robotics over the next few years from \$4.6 Billion, but by 2018 they were recording 61% growth to \$12.6B and by 2020 the IFR has recorded 85% overall growth expecting revenue from service robotics to hit \$37B by 2021.

It’s unlikely that we’ll recall robots, once designed, built and deployed, for anything other than a physical safety issue. And the gendering of robots isn’t something we can roll out a software update to fix. We need to start requesting companies to not deploy robots that reinforce gender stereotyping. They can still be cute and lovable, I’m not opposed to the R2D2 robot stereotype!

Consumers are starting to fight back against the gender stereotyping of toys, which really only started in the 20th century as a way to extract more money from parents, and some brands are realizing that there’s an opportunity for them in developing gender neutral toys. Recent research from the Pew Research Center found that overall 64% of US adults wanted boys to play with toys associated with girls, and 76% of US adults wanted girls to play with toys associated with boys. The difference between girls and boys can be explained because girls’ role playing (caring and nurturing) is still seen as more negative than boys’ roles (fighting and leadership). But the overall range that shows that society has developed a real desire to avoid gender stereotyping completely.

Sadly, it’s like knowing sugar is bad for us, while it still tastes sweet.

In 2016, I debated Ben Goertzel, maker of Sophia the Robot, on the main stage of the Web Summit on whether humanoid robots were good or bad. I believe I made the better case in terms of argument, but ultimately the crowd sided with Goertzel, and by default with Sophia. (there are a couple of descriptions of the debate referenced below).

Robots are still bright shiny new toys to us. When are we going to realize that we’ve already opened the box and played this game, and women, or any underrepresented group, or any stereotype role, is going to be the loser. No, we’re all going to lose! Because we don’t want these stereotypes any more and robots are just going to reinforce the stereotypes that we already know we don’t want.

And did I mention how white all the robots are? Yes, they are racially stereotyped too. (See Ayanna Howard’s new book “Sex, Race and Robots: How to be human in an age of AI”)

References:

• Should robots be gendered? – Robohub
• Machine Translation: Analyzing Gender – Gendered Innovations (Stanford University)
• The Media Equation: How People Treat Computers, Television, and New Media Like Real People and Places – Byron Reeves and Clifford Nass
• Alexa, Siri, Cortana: The Problem With All-Female Digital Assistants – The Wall Street Journal
• I’d blush if I could: closing gender divides in digital skills through education – UNESCO
• Principles of robotics – EPSRC
• Web Summit: A Debate on Robots and People – IEEE Transmitter
• Should robots look and act like humans? – Keycass on Medium
• Web Summit Lisbon 2016 – Cristina Turbatu
• IFR’s two reports for 2015 show double-digit growth for remainder of decade – The Robot Report
• World Robotics Report: Global Sales of Robots Hit $16.5B in 2018 – Robotics Business Review
• What are Professional Service Robots? – Robotic Industries Association
• How toys became gendered – and why it’ll take more than a gender-neutral doll to change how boys perceive femininity – The Conversation
• Most Americans see value in steering children toward toys, activities associated with opposite gender – Pew Research Center
• Why Are So Many Robots White? Study Shows There’s a Racial Bias – Observer
• Should robots have a gender or ethnicity? One roboticist says no – Marketplace

The Five Laws of Robotics are drawn from the EPSRC Principles of Robotics, first developed in 2010 and a living document workshopped by experts across many relevant disciplines. These five principles are practical and concise, embracing the majority of principles expressed across a wide range of ethics documents. I will explain in more detail.

1. There should be no killer robots.
2. Robots should (be designed to) obey the law.
3. Robots should (be designed to) be good products.
4. Robots should be transparent in operation
5. Robots should be identifiable

EPSRC says that robots are multi-use tools. Robots should not be designed solely or primarily to kill or harm humans, except in the interests of national security. More information is at the Campaign to Stop Killer Robots.

Humans, not robots, are the responsible agents. Robots should be designed and operated as far as is practicable to comply with existing laws and fundamental rights and freedoms, including privacy.

Robots are products. They should be designed using processes which assure their safety and security. Quality guidelines, processes and standards already exist.

Robots are manufactured artefacts. They should not be designed in a deceptive way to exploit users, instead their machine nature should be made transparent.

It should be possible to find out who is responsible for any robot. My suggestion here is that robots in public spaces require a license plate; a clear identification of robot and the responsible organization.

As well as speaking about Five Laws of Robotics, I introduced five practical proposals to help us respond at a regional, national and global level.

1. Robot Registry (license plates, access to database of owners/operators)
2. Algorithmic Transparency (via Model Cards and Testing Benchmarks)
3. Independent Ethical Review Boards (as in biotech industry)
4. Robot Ombudspeople to liaise between public and policy makers
5. Rewarding Good Robots design awards and case studies

Silicon Valley Robotics is about to announce the first winners of our inaugural Robotics Industry Awards. The SVR Industry Awards consider the responsible design as well as technological innovation and commercial success. There are also some ethical checkmark or certification initiatives under preparation, but like the development of new standards, these can take a long time to do properly, whereas awards, endorsements and case studies can be available immediately to foster the discussion of what constitutes good robots and what are the social challenges that robotics needs to solve.

In fact, the robot registry suggestion was picked up recently by Stacey Higginbotham in the IEEE Spectrum. Silicon Valley Robotics is putting together these policy suggestions for the new White House administration.

The logo of the UAE’s annual World Government Summit which this year included a Global Governance of AI Roundtable for the first time.

In this episode Andrew Vaziri speaks with Nicolas Economou, CEO of the eDiscovery company H5 and co-founder and chair of the Science, Law and Society Initiative at The Future Society, a 501c3 think tank incubated at the Harvard Kennedy School of Government. Economou discusses how AI is applied in the legal system, as well as some of the key points from the recent “Global Governance of AI Roundtable”. The roundtable, hosted by the government of the UAE, brought together a diverse group of leaders from tech companies, governments, and academia to discuss the societal implications of AI.

Nicolas Economou

Nicolas Economou is the chief executive of H5 and was a pioneer in advocating the application of scientific methods to electronic discovery. He contributes actively to advancing dialogue on public policy challenges at the intersection of law, science, and technology. He is co-chair of the Law Committee of the IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems; is chair of The Future Society’s Science, Law and Society Initiative and Senior Advisor to its Artificial Intelligence Initiative; and chaired the Law Committee of the Global Governance of AI Roundtable hosted in Dubai as part of the 2018 World Government Summit.

Links

• Nicholas Economou’s Website
• Subscribe to Robots using RSS
• Support us on Patreon
• Download MP3 (15.6 MB)

I blogged about Dieter’s very elegant experiment here, but let me summarise. With two NAO robots he set up a demonstration of an ethical robot helping another robot acting as a proxy human, then showed that with a very simple alteration of the ethical robot’s logic it is transformed into a distinctly unethical robot – behaving either competitively or aggressively toward the proxy human.

Here are our paper’s key conclusions:

The ease of transformation from ethical to unethical robot is hardly surprising. It is a straightforward consequence of the fact that both ethical and unethical behaviours require the same cognitive machinery with – in our implementation – only a subtle difference in the way a single value is calculated. In fact, the difference between an ethical (i.e. seeking the most desirable outcomes for the human) robot and an aggressive (i.e. seeking the least desirable outcomes for the human) robot is a simple negation of this value.

On the face of it, given that we can (at least in principle) build explicitly ethical machines then it would seem that we have a moral imperative to do so; it would appear to be unethical not to build ethical machines when we have that option. But the findings of our paper call this assumption into serious doubt. Let us examine the risks associated with ethical robots and if, and how, they might be mitigated. There are three.

1. First there is the risk that an unscrupulous manufacturermight insert some unethical behaviours into their robots in order to exploit naive or vulnerable users for financial gain, or perhaps to gain some market advantage (here the VW diesel emissions scandal of 2015 comes to mind). There are no technical steps that would mitigate this risk, but the reputational damage from being found out is undoubtedly a significant disincentive. Compliance with ethical standards such as BS 8611 guide to the ethical design and application of robots and robotic systems, or emerging new IEEE P700X ‘human’ standards would also support manufacturers in the ethical application of ethical robots. 
2. Perhaps more serious is the risk arising from robots that have user adjustable ethics settings. Here the danger arises from the possibility that either the user or a technical support engineer mistakenly, or deliberately, chooses settings that move the robot’s behaviours outside an ‘ethical envelope’. Much depends of course on how the robot’s ethics are coded, but one can imagine the robot’s ethical rules expressed in a user-accessible format, for example, an XML like script. No doubt the best way to guard against this risk is for robots to have no user adjustable ethics settings, so that the robot’s ethics are hard-coded and not accessible to either users or support engineers. 
3. But even hard-coded ethics would not guard against undoubtedly the most serious risk of all, which arises when those ethical rules are vulnerable to malicious hacking. Given that cases of white-hat hacking of cars have already been reported, it’s not difficult to envisage a nightmare scenario in which the ethics settings for an entire fleet of driverless cars are hacked, transforming those vehicles into lethal weapons. Of course, driverless cars (or robots in general) without explicit ethics are also vulnerable to hacking, but weaponising such robots is far more challenging for the attacker. Explicitly ethical robots focus the robot’s behaviours to a small number of rules which make them, we think, uniquely vulnerable to cyber-attack.

Ok, taking the most serious of these risks: hacking, we can envisage several technical approaches to mitigating the risk of malicious hacking of a robot’s ethical rules. One would be to place those ethical rules behind strong encryption. Another would require a robot to authenticate its ethical rules by first connecting to a secure server. An authentication failure would disable those ethics, so that the robot defaults to operating without explicit ethical behaviours. Although feasible, these approaches would be unlikely to deter the most determined hackers, especially those who are prepared to resort to stealing encryption or authentication keys.

It is very clear that guaranteeing the security of ethical robots is beyond the scope of engineering and will need regulatory and legislative efforts. Considering the ethical, legal and societal implications of robots, it becomes obvious that robots themselves are not where responsibility lies. Robots are simply smart machines of various kinds and the responsibility to ensure they behave well must always lie with human beings. In other words, we require ethical governance, and this is equally true for robots with or without explicit ethical behaviours.

Two years ago I thought the benefits of ethical robots outweighed the risks. Now I’m not so sure. I now believe that – even with strong ethical governance – the risks that a robot’s ethics might be compromised by unscrupulous actors are so great as to raise very serious doubts over the wisdom of embedding ethical decision making in real-world safety critical robots, such as driverless cars. Ethical robots might not be such a good idea after all.

Here is the full reference to our paper:

Vanderelst D and Winfield AFT (2018), The Dark Side of Ethical Robots, AAAI/ACM Conf. on AI Ethics and Society (AIES 2018), New Orleans.

K. Daron Acemoglu, the Elizabeth and James Killian Professor of Economics at MIT, is a leading thinker on the labor market implications of artificial intelligence, robotics, automation, and new technologies.
Photo: Jared Charney

By Meg Murphy
K. Daron Acemoglu, the Elizabeth and James Killian Professor of Economics at MIT, is a leading thinker on the labor market implications of artificial intelligence, robotics, automation, and new technologies. His innovative work challenges the way people think about these technologies intersect with the world of work. In 2005, he won the John Bates Clark Medal, an honor shared by a number of Nobel Prize recipients and luminaries in the field of economics.

Acemoglu holds a bachelor’s degree in economics from University of York. His master’s degree in mathematical economics and econometrics and doctorate in economics are from the London School of Economics. With political scientist James Robinson, Acemoglu co-authored the much discussed book “Why Nations Fail” (Crown Business, 2012) and “Economic Origins of Dictatorship and Democracy” (Cambridge University Press, 2006). He also wrote the book, “Introduction to Modern Economic Growth” (Princeton University Press, 2008). Acemoglu recently answered a few questions about technology and work.

Q: How do we begin to understand the rise of artificial intelligence and its future impact on society?

A: We need to look to the past in the face of modern innovations in machine learning, robotics, artificial intelligence, big data, and beyond. The process of machines replacing labor in the production process is not a new one. It’s been going on pretty much continuously since the Industrial Revolution. Spinning and weaving machines took jobs away from spinners and weavers. One innovation would follow another, and people would be thrown out of work by a machine performing the job in a cheaper way.

But at the end of the day, the Industrial Revolution and its aftermath created much better opportunities for people. For much of the 20th century in the U.S., workers’ wages and employment kept growing. New occupations and new tasks and new jobs were generated within the framework of new technological knowledge. A huge number of occupations in the American economy today did not exist 50 years ago — radiologists, management consultants, software developers, and so on. Go back a century and most of the white-collar jobs today did not exist.

Q:  Do you think public fears about the future of work are just?

A: The way we live continuously changes in significant ways — how we learn, how we acquire food, what we emphasize, our social organizations.

Our adjustments to technology — especially transformative technologies — are not a walk in the park. It is not going to be easy and seamless and just sort itself out. A lot of historical evidence shows the process is a painful one. The mechanization of agriculture is one of the greatest achievement of the American economy but it was hugely disruptive for millions of people who suffered joblessness.

At the same time, we are capable technologically and socially of creating many new jobs that will take people to new horizons in terms of productivity and freedom from the hardest types of manual labor. There are great opportunities with artificial intelligence but whether or not we exploit them is a different question. I think you should never be too optimistic but neither should you be too pessimistic.

Q: How do you suggest people prepare for the future job market?

A: We are very much in the midst of understanding what sort of process we are going through. We don’t even necessarily know what skills are needed for the jobs of the future.

Imagine one scenario. Artificial intelligence removes the need for seasoned accountants to fulfill numeracy-related tasks. But we need tax professionals, for instance, to inform clients about their choices and options in some sort of emphatic human way. They will have to become the interface between the machines and the customers. The jobs of the future, in this instance and many others, would require communications, flexibility, and social skills.

However, I don’t know if my hypothesis is true because we haven’t tested it. We haven’t lived through it. I see the biggest void in our knowledge. People at institutions like MIT must learn more about what’s is going on so that we are better prepared to understand the future.

To explore these issues, former Robohub Editor Hallie Siegel and Open Roboethics Institute (ORi) Co-founder and Director AJung Moon invited researchers, policymakers, and industry members who have played a significant role in launching and shaping major strategic robotics initiatives in their regions to participate in an IROS 2017 workshop “Best practices in designing effective roadmaps for robotics innovation” to see what is working, what is not, and to uncover what best practices in roadmap development (if any) might be broadly applied to other regions.

Specifically, the workshop sought to examine the process of how these policy frameworks came to be created in the first place, how they have been tailored to local capabilities and strengths, and what performance indicators are being used to measure their success — so that participants could draw from international collective experience as they design and evaluate strategic robotics initiatives for their own regions.
The highlight of the workshop was a pair of panel discussions moderated by robotics ecosystem expert Andra Keay, Managing Director of the robotics industry cluster association Silicon Valley Robotics.
The first panel — featuring Peter Corke (QUT, Australia), Kyung-Hoon Kim (Ministry of Trade, Industry & Energy (MOTIE), South Korea), Rainer Bischoff (euRobotics AISBL, EU), Dario Floreano (NCCR Robotics, EPFL, Switzerland), Sue Keay (Queensland University of Technology, Australia), and Raj Madhavan (Humanitarian Robotics Technologies,LLC, USA) — was an in-depth discussion of regional differences in roadmap motivation, leadership, and strategy design from the perspectives of government, industry and academia.
The second panel — featuring Raja Chatila (IEEE Global Initiative for Ethical Considerations
in Artificial Intelligence & Autonomous Systems), AJung Moon (Open Roboethics Institute),
Alex Shikany (Robotic Industries Association), and Sabine Hauert (Robohub.org) — covered the ways in which issues such as roboethics, public perception and the fear of job loss due to automation are influencing robotics policy in different regions around the globe.

Lenka Pitonakova was part of the audience and provides her impressions below.

State-of-the-art in different countries

The workshop started with presentations on the process of designing roadmaps for robotics research and development. Individual speakers discussed best practises and problems encountered in their respective countries.

Dr. Rainer Bischoff (euRobotics, EU)

Robotics research is in a very mature stage in the EU. A network of robotics researchers and companies closely cooperate on issues such as using robots in healthcare, logistics, as well as for maintenance and inspection of infrastructure. The partnership between the European Commission, and European industry and academia is called SPARC. Decisions about relevant research areas to fund in the European Commission’s H2020 programme are made in a bottom-up fashion – meaning stakeholders get to influence priority areas for funding. This is done through topic groups in robotics established throughout the EU, which help shape the Robotics Multi-annual Roadmap which in turn shapes the work programme of the European Commission. Public outreach is also very important in the EU. Not only are all funding decisions openly available to the public, researchers are encouraged to perform outreach activities.

Dr. Dario Floreano (NCCR Robotics and EPFL, Switzerland)


One main source of funding in Switzerland currently comes from the NCCR scheme, which is a 12-year programme that started in 2010 and has four target areas: research, technology transfer (especially when it comes to creating start-ups), education and outreach. A part of this programme is also structural change in the research institutions of Switzerland. Since 2010, new robotics centres have been created and many new professors have been appointed. Switzerland takes a very pro-active approach to applied research, and technology transfer is as important for them as research itself. The most important areas of interest include wearable robots, mobile robots for search and rescue, human-robot interaction and teleoperation, as well as educational robots that can teach computational thinking.

Dr. Sue Keay (Australian Centre for Robotic Vision, Australia)


Australia is currently behind the Western countries when it comes to automation. This is mostly because there is no overreaching body that could unify different research groups and provide substantial research funding. A plan towards creating a centralised institution to support research is currently being formed and efforts are underway to persuade the government about the importance of investing into robotics. To this end, the ICRA 2018 conference, which will take place in Brisbane, Australia, is an important event for the Australian robotics community. Among the focus areas for future research, mining, manufacturing, defence and healthcare have been identified as the most important.

Dr. Kyung-Hoon Kim (Ministry of Trade, Industry & Energy, South Korea)

The government of South Korea has controlled the robotics research focus via the Intelligent Robots Development and Promotion Act since 2008. Every five years, the roadmap for research is re-visited by government experts and new funding areas are identified. The areas of interest include machine learning and big data analysis, human-robot collaboration, development of hardware parts and software platforms, as well as application of robotics in smart factories.

Dr. Raj Madhavan (Humanitarian Robotics Technologies, USA and India)

Researchers in India currently do not get much support from the government when it comes to robotics research, although some work on a research roadmap for robotics started in early 2017. However, the lack of government support is not the only problem that India faces. There also seems to be a lack of interest and commitment from individual researchers to unite their efforts and collaborate on a national level.

Research roadmaps and funding

In a panel discussion that followed the presentations, the following stakeholders in research and innovation were identified:

• Governments: Provide investment money and shape regulations
• Academia: Provides the foresight into new technologies
• Industry: Creates the need for research and applies it.

A crucial factor that influences the interest from government and industry was idenfied: The ability of researchers to provide estimates about the economic impact of their work. Especially in the EU, robotics started growing as a research field when industry became more involved in funding and roadmap creation.

Secondly, it was mentioned that engaging the government and the public is also very important. Because of the ability of government regulations to stop technology from being developed and used, politicians should be engaged with new ideas and with how they will shape the society. On the other hand, end-users, i.e., the public, also need to understand the impact of new technology on their lives, both to encourage the use of new technology and to mitigate fears of its negative impacts. However, engaging all stakeholders and making research and development relevant to all of them is often very difficult because of differences in opinions and long-term goals.

Challenges for adoption of robotics technologies

The second panel discussion focused on challenges for adoption of robotic technologies.

Public acceptance and uncertainty about the impact of technology on well-being of people and society as a whole, as well as the fear of loosing control of autonomous systems, were identified as the most important topics to address. To mitigate these fears, it is useful to provide the public with statistics on how technology impacted jobs in the recent past, as well as to provide well-informed projections for the near future.

It is crucial for scientists to be trained in and to apply best practice in Public communication, especially as the commentary on new technology often comes from the media, where non-experts (consciously or not) misinform the public about the impacts and capabilities of new technology.

Asimov’s three laws of Robotics (1950)

1. A robot may not injure a human being or, through inaction, allow a human being to come to harm. 
2. A robot must obey the orders given it by human beings except where such orders would conflict with the First Law. 
3. A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws. 

I have included these to explicitly acknowledge, firstly, that Asimov undoubtedly established the principle that robots (and by extension AIs) should be governed by principles, and secondly that many subsequent principles have been drafted as a direct response. The three laws first appeared in Asimov’s short story Runaround [1]. This wikipedia article provides a very good account of the three laws and their many (fictional) extensions.


Murphy and Wood’s three laws of Responsible Robotics (2009)

1. A human may not deploy a robot without the human-robot work system meeting the highest legal and professional standards of safety and ethics. 
2. A robot must respond to humans as appropriate for their roles. 
3. A robot must be endowed with sufficient situated autonomy to protect its own existence as long as such protection provides smooth transfer of control which does not conflict with the First and Second Laws. 

These were proposed in Robin Murphy and David Wood’s paper Beyond Asimov: The Three Laws of Responsible Robotics [2].

EPSRC Principles of Robotics (2010)

1. Robots are multi-use tools. Robots should not be designed solely or primarily to kill or harm humans, except in the interests of national security. 
2. Humans, not Robots, are responsible agents. Robots should be designed and operated as far as practicable to comply with existing laws, fundamental rights and freedoms, including privacy. 
3. Robots are products. They should be designed using processes which assure their safety and security. 
4. Robots are manufactured artefacts. They should not be designed in a deceptive way to exploit vulnerable users; instead their machine nature should be transparent. 
5. The person with legal responsibility for a robot should be attributed. 

These principles were drafted in 2010 and published online in 2011, but not formally published until 2017 [3] as part of a two-part special issue of Connection Science on the principles, edited by Tony Prescott & Michael Szollosy [4]. An accessible introduction to the EPSRC principles was published in New Scientist in 2011.

Future of Life Institute Asilomar principles for beneficial AI (Jan 2017)

I will not list all 23 principles but extract just a few to compare and contrast with the others listed here:

6. Safety: AI systems should be safe and secure throughout their operational lifetime, and verifiably so where applicable and feasible.

7. Failure Transparency: If an AI system causes harm, it should be possible to ascertain why.

8. Judicial Transparency: Any involvement by an autonomous system in judicial decision-making should provide a satisfactory explanation auditable by a competent human authority.

9. Responsibility: Designers and builders of advanced AI systems are stakeholders in the moral implications of their use, misuse, and actions, with a responsibility and opportunity to shape those implications.

10. Value Alignment: Highly autonomous AI systems should be designed so that their goals and behaviors can be assured to align with human values throughout their operation.

11. Human Values: AI systems should be designed and operated so as to be compatible with ideals of human dignity, rights, freedoms, and cultural diversity.

12. Personal Privacy: People should have the right to access, manage and control the data they generate, given AI systems’ power to analyze and utilize that data.

13. Liberty and Privacy: The application of AI to personal data must not unreasonably curtail people’s real or perceived liberty.

14. Shared Benefit: AI technologies should benefit and empower as many people as possible.

15. Shared Prosperity: The economic prosperity created by AI should be shared broadly, to benefit all of humanity.

An account of the development of the Asilomar principles can be found here.

The ACM US Public Policy Council Principles for Algorithmic Transparency and Accountability (Jan 2017)

1. Awareness: Owners, designers, builders, users, and other stakeholders of analytic systems should be aware of the possible biases involved in their design, implementation, and use and the potential harm that biases can cause to individuals and society.
2. Access and redress: Regulators should encourage the adoption of mechanisms that enable questioning and redress for individuals and groups that are adversely affected by algorithmically informed decisions.
3. Accountability: Institutions should be held responsible for decisions made by the algorithms that they use, even if it is not feasible to explain in detail how the algorithms produce their results.
4. Explanation: Systems and institutions that use algorithmic decision-making are encouraged to produce explanations regarding both the procedures followed by the algorithm and the specific decisions that are made. This is particularly important in public policy contexts.
5. Data Provenance: A description of the way in which the training data was collected should be maintained by the builders of the algorithms, accompanied by an exploration of the potential biases induced by the human or algorithmic data-gathering process.
6. Auditability: Models, algorithms, data, and decisions should be recorded so that they can be audited in cases where harm is suspected.
7. Validation and Testing: Institutions should use rigorous methods to validate their models and document those methods and results. 

See the ACM announcement of these principles here. The principles form part of the ACM’s updated code of ethics.

Japanese Society for Artificial Intelligence (JSAI) Ethical Guidelines (Feb 2017)>

1. Contribution to humanity Members of the JSAI will contribute to the peace, safety, welfare, and public interest of humanity. 
2. Abidance of laws and regulations Members of the JSAI must respect laws and regulations relating to research and development, intellectual property, as well as any other relevant contractual agreements. Members of the JSAI must not use AI with the intention of harming others, be it directly or indirectly.
3. Respect for the privacy of others Members of the JSAI will respect the privacy of others with regards to their research and development of AI. Members of the JSAI have the duty to treat personal information appropriately and in accordance with relevant laws and regulations.
4. Fairness Members of the JSAI will always be fair. Members of the JSAI will acknowledge that the use of AI may bring about additional inequality and discrimination in society which did not exist before, and will not be biased when developing AI. 
5. Security As specialists, members of the JSAI shall recognize the need for AI to be safe and acknowledge their responsibility in keeping AI under control. 
6. Act with integrity Members of the JSAI are to acknowledge the significant impact which AI can have on society. 
7. Accountability and Social Responsibility Members of the JSAI must verify the performance and resulting impact of AI technologies they have researched and developed. 
8. Communication with society and self-development Members of the JSAI must aim to improve and enhance society’s understanding of AI.
9. Abidance of ethics guidelines by AI AI must abide by the policies described above in the same manner as the members of the JSAI in order to become a member or a quasi-member of society.

An explanation of the background and aims of these ethical guidelines can be found here, together with a link to the full principles (which are shown abridged above).

Draft principles of The Future Society’s Science, Law and Society Initiative (Oct 2017)

1. AI should advance the well-being of humanity, its societies, and its natural environment. 
2. AI should be transparent. 
3. Manufacturers and operators of AI should be accountable. 
4. AI’s effectiveness should be measurable in the real-world applications for which it is intended. 
5. Operators of AI systems should have appropriate competencies. 
6. The norms of delegation of decisions to AI systems should be codified through thoughtful, inclusive dialogue with civil society.

This article by Nicolas Economou explains the 6 principles with a full commentary on each one.

Montréal Declaration for Responsible AI draft principles (Nov 2017)

1. Well-being The development of AI should ultimately promote the well-being of all sentient creatures.
2. Autonomy The development of AI should promote the autonomy of all human beings and control, in a responsible way, the autonomy of computer systems.
3. Justice The development of AI should promote justice and seek to eliminate all types of discrimination, notably those linked to gender, age, mental / physical abilities, sexual orientation, ethnic/social origins and religious beliefs.
4. Privacy The development of AI should offer guarantees respecting personal privacy and allowing people who use it to access their personal data as well as the kinds of information that any algorithm might use.
5. Knowledge The development of AI should promote critical thinking and protect us from propaganda and manipulation.
6. Democracy The development of AI should promote informed participation in public life, cooperation and democratic debate.
7. Responsibility The various players in the development of AI should assume their responsibility by working against the risks arising from their technological innovations.

The Montréal Declaration for Responsible AI proposes the 7 values and draft principles above (here in full with preamble, questions and definitions).

IEEE General Principles of Ethical Autonomous and Intelligent Systems (Dec 2017)

1. How can we ensure that A/IS do not infringe human rights? 
2. Traditional metrics of prosperity do not take into account the full effect of A/IS technologies on human well-being. 
3. How can we assure that designers, manufacturers, owners and operators of A/IS are responsible and accountable? 
4. How can we ensure that A/IS are transparent? 
5. How can we extend the benefits and minimize the risks of AI/AS technology being misused? 

These 5 general principles appear in Ethically Aligned Design v2, a discussion document drafted and published by the IEEE Standards Association Global Initiative on Ethics of Autonomous and Intelligent Systems. The principles are expressed not as rules but instead as questions, or concerns, together with background and candidate recommendations.

A short article co-authored with IEEE general principles co-chair Mark Halverson Why Principles Matter explains the link between principles and standards, together with further commentary and references.

UNI Global Union Top 10 Principles for Ethical AI (Dec 2017)

1. Demand That AI Systems Are Transparent
2. Equip AI Systems With an “Ethical Black Box”
3. Make AI Serve People and Planet 
4. Adopt a Human-In-Command Approach
5. Ensure a Genderless, Unbiased AI
6. Share the Benefits of AI Systems
7. Secure a Just Transition and Ensuring Support for Fundamental Freedoms and Rights
8. Establish Global Governance Mechanisms
9. Ban the Attribution of Responsibility to Robots
10. Ban AI Arms Race

Drafted by UNI Global Union‘s Future World of Work these 10 principles for Ethical AI (set out here with full commentary) “provide unions, shop stewards and workers with a set of concrete demands to the transparency, and application of AI”.

References
[1] Asimov, Isaac (1950): Runaround,  in I, Robot, (The Isaac Asimov Collection ed.) Doubleday. ISBN 0-385-42304-7.
[2] Murphy, Robin; Woods, David D. (2009): Beyond Asimov: The Three Laws of Responsible Robotics. IEEE Intelligent systems. 24 (4): 14–20.
[3] Margaret Boden et al (2017): Principles of robotics: regulating robots in the real world
Connection Science. 29 (2): 124:129.
[4] Tony Prescott and Michael Szollosy (eds.) (2017): Ethical Principles of Robotics, Connection Science. 29 (2) and 29 (3).

There were several fascinating keynote presentations. Mathias Scheutz’ inaugural speech highlighted the need for robots to act in a way that would be perceived as using moral principles and judgement. While enabling autonomous reasoning on ethical decisions is a difficult research problem, researchers should continue to pursue this. One avenue is enabling reasoning systems to examine obligations and permissions and on the basis of such analysis, robots would perform sophisticated choices between actions and plans to achieve correct behaviour. It was refreshing to see that we could potentially have autonomous robots that arrive at appropriate decisions that would be seen as “right” or “wrong” by an external observer.

On the other hand, Rodolphe Gélin provided the perspective of robot manufacturers, and how difficult the issues of safety have become. The expectations of the public regarding robots seem to go beyond other conventional machines. The discussion was very diverse, and some suggested schemes similar to licensing would be required to qualify humans to operate robots (as they re-train them or re-program them). Other schemes for insurance and liabilities were suggested.

Professional bodies, experts and standards were discussed by the other two keynote presentations. Raja Chatila from the IEEE Global AI Ethics Initiative perspective, and Gurvinder Singh Virk from that of several ISO robot standardisation groups.

The conference also hosted a panel discussion, where interesting issues were debated like the challenges posed by the proliferation of drones in the general public. Such a topic has characteristics different from many other problems societies have faced with the introduction of new technologies. Drones can be 3D printed from many designs with potentially no liability to the designer, they can be operated with virtually no complex training, and they can be controlled from sufficient long distances that recuperating the drone would be insufficient to track the operator/owner. Their cameras and data recording can potentially be used to what some would consider privacy breaches, and they could compete for the space of already operating commercial aviation. It seems unclear what regulations and what bodies are to intervene and even so, how to enforce them. Would something similar happen when the public acquires pet-robots or artificial companions?

The presentations of accepted papers raised many issues, including the difficulties to create legal foundations for liability schemes and the responsibilities attributed to machines and operators. Particular aspects included the fact that for specific tasks, computers do significantly better than the average person (examples are driving a car and negotiation a curve). Other challenges are that humans will be in the proximity of robots on a regular basis in manufacturing or office environments with many new potential risks.

The vibrant nature of the conference concluded that the challenges are emerging much more rapidly than the answers.

We’ve also just launched a survey on software behaviours that an autonomous car should have when faced with difficult decisions. Just click here. Participants may win a prize, participation is completely voluntary and anonymous.

As AI surpasses human abilities in Go and poker – two decades after Deep Blue trounced chess grandmaster Garry Kasparov – it is seeping into our lives in ever more profound ways. It affects the way we search the web, receive medical advice and whether we receive finance from our banks.

Instead of worrying so much about robots taking away jobs, maybe we should worry more about wages being too low for robots to even get a chance. Seasonal labor for harvesting agricultural products, particularly fruits and vegetables, is dependent on human labor from a diminishing universe of willing workers.

Robots that can supplement or replace human workers in the harvesting process are being developed and tested in startups and academia, but almost all are not yet ready for prime time.

In a NY Times article written by Neil Irwin entitled Rethinking Low Productivity, productivity growth has been on a downward path since the Financial Crisis. Irwin, who writes about economic trends, asks whether the downward trend is the cause of low growth, or the result, a troubling question in the dynamics of the agriculture industry.

“Inventors and business innovators are always developing better ways to do things, but it takes a labor shortage and high wages to coax firms to deploy the investment it takes to actually put those innovations into widespread use.”

Those two phenomena are happening today in the U.S. agricultural industry. Labor rates are rising while willing laborers are diminishing — yet farmers are not yet investing in robotics. In fact, they are accommodating to the labor crisis by planting less and changing the crops they grow to be less labor intensive.

J.W. Mason, the author of a paper by the Roosevelt Institute cited as the basis for the thesis of Irwin’s article, says:

“If the labor market tightens and wages rise, that will be the impetus to get companies to consider more big-ticket innovations that generate productivity growth so that we don’t perpetuate the present conundrum in which both arguments cannot be true:

“On Mondays and Wednesdays, economists argue that wages are low because robots are taking people’s jobs.

On Tuesdays and Thursdays, it’s that we can’t have wages rise because productivity growth is low.”

This situation cannot continue without radical changes else we will become buyers of foreign fruits and vegetables paying prices set by foreign providers and dependent on them for availability and quality.

A number of factors are causing a reset in the ag industry in addition to the declining availability of farm workers: the challenges and complexities of employing and retaining farm labor; the rising cost of farm workers; changing farmlands; climate change; the growth of indoor farming; and the broader automation of the agriculture industry. All are propelling farmers to make changes in how they farm (or change downward the product mix they farm (which is often a path toward failure)). Market challenges for the sector include unclear value propositions, limited awareness of robotic systems among growers, insufficient robotic solutions, the difficulty of matching human-like dexterity with machines, fragmented technology development, and weak support.

Tractica, in their report on agricultural robotics, forecast an optimistic resolution to this temporary conundrum in the very near term and that that shipments of agricultural robots will increase significantly in the years ahead, rising from 32,000 units in 2016 to 594,000 units annually by 2024, by which time the market is expected to reach $74 billion in annual revenue. Certainly the time is ripe and, like Mason said, “Both arguments can’t be true.”

Setting some ground rules

Android textured with flag of china. Technology concept. Isolated

China has recently announced their long-term goal to become #1 in A.I. by 2030. They plan to grow their A.I. industry to over $22 billion by 2020, $59 billion by 2025 and $150 billion by 2030. They did this same type of long-term strategic planning for robotics – to make it an in-country industry and to transform the country from a low-cost labor source to a high-tech manufacturing resource, and it’s working.

China's Artificial Intelligence Manifesto

With this major strategic long-term push into A.I., China is looking to rival U.S. market leaders such as Alphabet/Google, Apple, Amazon, IBM and Microsoft. China is keen not to be left behind in a technology that is increasingly pivotal — from online commerce to self-driving vehicles, energy, and consumer products. China aims to catch up by solving issues including a lack of high-end computer chips, software that writes software, and trained personnel. Beijing will play a big role in policy support and regulation as well as providing and funding research, incentives and tax credits.

The local and central government are supporting this AI effort,” said Rui Yong, chief technology officer at PC maker Lenovo Group. “They see this trend coming and they want to invest more.

Many cited the defeat of the world's top Go players from China and South Korea by the Google-owned A.I. company DeepMind and their AlphaGo game-playing software as the event that caused China's State Council to enact and launch its A.I. plan which it announced on July 20th. The NY Times called it “a sort of Sputnik moment for China.”

Included in the announcement:

China will be investing heavily to ensure its companies, government and military leap to the front of the pack in a technology many think will one day form the basis of computing.

The plan covers almost every field: from using the technology  for voice recognition to dispatching robots for deep-sea and Arctic exploration, as well as using AI in military security. The Council said the country must “firmly grasp this new stage of AI development.”

China said it plans to build “special-force” AI robots for ocean and Arctic exploration, use the technology for gathering evidence and reading court documents, and also use machines for “emotional interaction functions.”

In the final stage, by 2030, China will “become the world’s premier artificial intelligence innovation center,” which in turn will “foster a new national leadership and establish the key fundamentals for an economic great power.”

Chinese Investments in A.I.

The DoD regularly warns that Chinese money has been flowing into American A.I. companies — some of the same companies it says are likely to help the United States military develop future weapons systems. The NY Times cites the following example:

When the United States Air Force wanted help making military robots more perceptive, it turned to a Boston-based artificial intelligence start-up called Neurala. But when Neurala needed money, it got little response from the American military.

So Neurala turned to China, landing an undisclosed sum from an investment firm backed by a state-run Chinese company.

Chinese firms have become significant investors in American start-ups working on cutting-edge technologies with potential military applications. The start-ups include companies that make rocket engines for spacecraft, sensors for autonomous navy ships, and printers that make flexible screens that could be used in fighter-plane cockpits. Many of the Chinese firms are owned by state-owned companies or have connections to Chinese leaders.

Chinese venture firms have offices in Silicon Valley, Boston and other areas where A.I. startups are happening. Many Chinese companies — such as Baidu — have American-based research centers to take advantage of local talent. 

The Committee on Foreign Investment in the United States (CFIUS), which reviews U.S. acquisitions by foreign entities for national security risks, appears to be blind to all of this.

China's Robot Manifesto Has Been Quite Successful

Chinese President Xi Jinping initiated “a robot revolution” and launched the “Made in China 2025” program. More than 1,000 firms and a new robotics association, CRIA (Chinese Robotics Industry Alliance) have emerged (or begun to transition) into robotics to take advantage of the program. By contrast, the sector was virtually non-existent a decade ago.

Under “Made in China 2025,” and the five-year robot plan launched last April, Beijing is focusing on automating key sectors of the economy including car manufacturing, electronics, home appliances, logistics, and food production. At the same time, the government wants to increase the share of in-country-produced robots to more than 50% by 2020; up from 31% last year and to be able to make 150,000 industrial robots in 2020; 260,000 in 2025; and 400,000 by 2030. China's stated goal in both their 5-year plan and Made in China 2025 program is to overtake Germany, Japan, and the United States in terms of manufacturing sophistication by 2049, the 100th anniversary of the founding of the People’s Republic of China. To make that happen, the government needs Chinese manufacturers to adopt robots by the millions. It also wants Chinese companies to start producing more of those robots and has encouraged strategic acquisitions.

Four of the top 15 acquisitions in 2016 were of robotic-related companies by Chinese acquirers:

1. Midea, a Chinese consumer products manufacturer, acquired KUKA, one of the Big 4 global robot manufacturers
2. The Kion Group, a predominately Chinese-funded warehousing systems and equipment conglomerate, acquired Dematic, a large European AGV and material handling systems company
3. KraussMaffei, a big German industrial robots integrator, was acquired by ChemChina
4. Paslin, a US-based industrial robot integrator, was acquired by Zhejiang Wanfeng Technology, a Chinese industrial robot integrator

The Robot Report wrote an extensive review of robot growth in China and recapped 500 of them by type and sub-category. For fundings and acquisitions in 2017, read the monthly posts on the subject.

By Jeff Morgan, Trinity College Dublin

Robots have been taking our jobs since the 1960s. So why are politicians and business leaders only now becoming so worried about robots causing mass unemployment?

It comes down to the question of what a robot really is. While science fiction has often portrayed robots as androids carrying out tasks in the much the same way as humans, the reality is that robots take much more specialised forms. Traditional 20th century robots were automated machines and robotic arms building cars in factories. Commercial 21st century robots are supermarket self-checkouts, automated guided warehouse vehicles, and even burger-flipping machines in fast-food restaurants.

Ultimately, humans haven’t become completely redundant because these robots may be very efficient but they’re also kind of dumb. They do not think, they just act, in very accurate but very limited ways. Humans are still needed to work around robots, doing the jobs the machines can’t and fixing them when they get stuck. But this is all set to change thanks to a new wave of smarter, better value machines that can adapt to multiple tasks. This change will be so significant that it will create a new industrial revolution.

Industry 4.0

This era of “Industry 4.0” is being driven by the same technological advances that enable the capabilities of the smartphones in our pockets. It is a mix of low-cost and high-power computers, high-speed communication and artificial intelligence. This will produce smarter robots with better sensing and communication abilities that can adapt to different tasks, and even coordinate their work to meet demand without the input of humans.

In the manufacturing industry, where robots have arguably made the most headway of any sector, this will mean a dramatic shift from centralised to decentralised collaborative production. Traditional robots focused on single, fixed, high-speed operations and required a highly skilled human workforce to operate and maintain them. Industry 4.0 machines are flexible, collaborative and can operate more independently, which ultimately removes the need for a highly skilled workforce.

For large-scale manufacturers, Industry 4.0 means their robots will be able to sense their environment and communicate in an industrial network that can be run and monitored remotely. Each machine will produce large amounts of data that can be collectively studied using what is known as “big data” analysis. This will help identify ways to improve operating performance and production quality across the whole plant, for example by better predicting when maintenance is needed and automatically scheduling it.

For small-to-medium manufacturing businesses, Industry 4.0 will make it cheaper and easier to use robots. It will create machines that can be reconfigured to perform multiple jobs and adjusted to work on a more diverse product range and different production volumes. This sector is already beginning to benefit from reconfigurable robots designed to collaborate with human workers and analyse their own work to look for improvements, such as BAXTER, SR-TEX and CareSelect.

While these machines are getting smarter, they are still not as smart as us. Today’s industrial artificial intelligence operates at a narrow level, which gives the appearance of human intelligence exhibited by machines, but designed by humans.

What’s coming next is known as “deep learning”. Similar to big data analysis, it involves processing large quantities of data in real time to make decisions about what is the best action to take. The difference is that the machine learns from the data so it can improve its decision making. A perfect example of deep learning was demonstrated by Google’s AlphaGo software, which taught itself to beat the world’s greatest Go players.

The turning point in applying artificial intelligence to manufacturing could come with the application of special microchips called graphical processing units (GPUs). These enable deep learning to be applied to extremely large data sets at extremely fast speeds. But there is still some way to go and big industrial companies are recruiting vast numbers of scientists to further develop the technology.

Impact on industry

As Industry 4.0 technology becomes smarter and more widely available, manufacturers of any size will be able to deploy cost-effective, multipurpose and collaborative machines as standard. This will lead to industrial growth and market competitiveness, with a greater understanding of production processes leading to new high-quality products and digital services.

Exactly what impact a smarter robotic workforce with the potential to operate on its own will have on the manufacturing industry, is still widely disputed. Artificial intelligence as we know it from science fiction is still in its infancy. It could well be the 22nd century before robots really have the potential to make human labour obsolete by developing not just deep learning but true artificial understanding that mimics human thinking.

Ideally, Industry 4.0 will enable human workers to achieve more in their jobs by removing repetitive tasks and giving them better robotic tools. In theory, this would allow us humans to focus more on business development, creativity and science, which it would be much harder for any robot to do. Technology that has made humans redundant in the past has forced us to adapt, generally with more education.

But because Industry 4.0 robots will be able to operate largely on their own, we might see much greater human redundancy from manufacturing jobs without other sectors being able to create enough new work. Then we might see more political moves to protect human labour, such as taxing robots.

Again, in an ideal scenario, humans may be able to focus on doing the things that make us human, perhaps fuelled by a basic income generated from robotic work. Ultimately, it will be up to us to define whether the robotic workforce will work for us, with us, or against us.

This article was originally published on The Conversation. Read the original article.

This week’s news is preliminary, but a U.S. house committee panel passed some new federal regulations which suggest sweeping change in the US regulatory approach to robocars.

Today, all cars sold must comply with the Federal Motor Vehicles Safety Standards (FMVSS). This is a huge set of standards, and it’s full of things written with human driven cars in mind, and making a radically different vehicle, like the Zoox, or the Waymo Firefly, or a delivery robot, is simply not going to happen under those standards. There is a provision where NHTSA can offer exemptions but it’s in small volumes, for prototype and testing vehicles mostly. The new rules would allow a vendor to get an exemption to make 100,000 vehicles per year, which should be enough for the early years of robocar deployment.

Secondly, these and other new regulations would preempt state regulations. Most players (except some states) have pushed for this. Many states don’t want the burden of regulating robocar design, since they don’t have the resources to do so, and most vendors don’t want what they call a “patchwork” of 50 regulations in the USA. My take is different. I agree the cost of a patchwork is not to be ignored, but the benefits of having jurisdictional competition may be much greater. When California proposed to ban vehicles like the Google Firefly, Texas immediately said, “Come to Texas, we won’t get in your way.” That pushed California to rethink. Having one regulation is good — but it has to be the right regulation, and we’re much too early in the game to know what the right regulation is.

This is just a committee in the house, and there is lots more distance to go, including the Senate and all the other usual hurdles. Whatever people thought about how much regulation there should be, everybody has known that the FMVSS needs a difficult and complex revision to work in the world of robocars, and a temporary exemption can be a solution to that.

By Christoph Salge, Marie Curie Global Fellow, University of Hertfordshire

How do you stop a robot from hurting people? Many existing robots, such as those assembling cars in factories, shut down immediately when a human comes near. But this quick fix wouldn’t work for something like a self-driving car that might have to move to avoid a collision, or a care robot that might need to catch an old person if they fall. With robots set to become our servants, companions and co-workers, we need to deal with the increasingly complex situations this will create and the ethical and safety questions this will raise.

Science fiction already envisioned this problem and has suggested various potential solutions. The most famous was author Isaac Asimov’s Three Laws of Robotics, which are designed to prevent robots harming humans. But since 2005, my colleagues and I at the University of Hertfordshire, have been working on an idea that could be an alternative.

Instead of laws to restrict robot behaviour, we think robots should be empowered to maximise the possible ways they can act so they can pick the best solution for any given scenario. As we describe in a new paper in Frontiers, this principle could form the basis of a new set of universal guidelines for robots to keep humans as safe as possible.

The Three Laws

Asimov’s Three Laws are as follows:

• A robot may not injure a human being or, through inaction, allow a human being to come to harm.
• A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.
• A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws.

While these laws sound plausible, numerous arguments have demonstrated why they are inadequate. Asimov’s own stories are arguably a deconstruction of the laws, showing how they repeatedly fail in different situations. Most attempts to draft new guidelines follow a similar principle to create safe, compliant and robust robots.

One problem with any explicitly formulated robot guidelines is the need to translate them into a format that robots can work with. Understanding the full range of human language and the experience it represents is a very hard job for a robot. Broad behavioural goals, such as preventing harm to humans or protecting a robot’s existence, can mean different things in different contexts. Sticking to the rules might end up leaving a robot helpless to act as its creators might hope.

Our alternative concept, empowerment, stands for the opposite of helplessness. Being empowered means having the ability to affect a situation and being aware that you can. We have been developing ways to translate this social concept into a quantifiable and operational technical language. This would endow robots with the drive to keep their options open and act in a way that increases their influence on the world.

When we tried simulating how robots would use the empowerment principle in various scenarios, we found they would often act in surprisingly “natural” ways. It typically only requires them to model how the real world works but doesn’t need any specialised artificial intelligence programming designed to deal with the particular scenario.

But to keep people safe, the robots need to try to maintain or improve human empowerment as well as their own. This essentially means being protective and supportive. Opening a locked door for someone would increase their empowerment. Restraining them would result in a short-term loss of empowerment. And significantly hurting them could remove their empowerment altogether. At the same time, the robot has to try to maintain its own empowerment, for example by ensuring it has enough power to operate and it does not get stuck or damaged.

Robots could adapt to new situations

Using this general principle rather than predefined rules of behaviour would allow the robot to take account of the context and evaluate scenarios no one has previously envisaged. For example, instead of always following the rule “don’t push humans”, a robot would generally avoid pushing them but still be able to push them out of the way of a falling object. The human might still be harmed but less so than if the robot didn’t push them.

In the film I, Robot, based on several Asimov stories, robots create an oppressive state that is supposed to minimise the overall harm to humans by keeping them confined and “protected”. But our principle would avoid such a scenario because it would mean a loss of human empowerment.

While empowerment provides a new way of thinking about safe robot behaviour, we still have much work to do on scaling up its efficiency so it can easily be deployed on any robot and translate to good and safe behaviour in all respects. This poses a very difficult challenge. But we firmly believe empowerment can lead us towards a practical solution to the ongoing and highly debated problem of how to rein in robots’ behaviour, and how to keep robots -– in the most naive sense -– “ethical”.

This article was originally published on The Conversation. Read the original article.

Join us at the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017) for a full day workshop that will bring together international stakeholders in robotics to examine best practices for accelerating robotics innovation through strategic policy frameworks.

Best practices in designing effective roadmaps for robotics innovation

IROS Workshop | 8:30-17:00 September 28, 2017 | Vancouver Convention Centre | Vancouver BC

This is a unique opportunity to learn from people who have played a significant role in designing and implementing major strategic robotics initiatives around the globe.

Objectives
In the past decade, a number of governing bodies and industry consortia have developed strategic roadmaps to guide investment and development of robotic technology. With the roadmaps from the US, South Korea, Japan and EU etc. well underway, the time is right to take stock of these strategic robotics initiatives to see what is working, what is not, and what best practices in roadmap development might be broadly applied to other regions.

The objective of this two-part workshop is to examine the process of how these policy frameworks came to be created in the first place, how they have been tailored to local capabilities and strengths, and what performance indicators are being used to measure their success — so that participants may draw from international collective experience as they design and evaluate strategic robotics initiatives for their own regions.

Program Part ONE — Morning Session: “Developing innovation policy for robotics, and establishing key performance indicators that are relevant to your region”
The morning session will feature international speakers who have played a significant role in launching and shaping major strategic robotics initiatives across the globe. The focus of this session will be on the history and process of designing the roadmap (rather than on merely presenting the roadmap itself) and key performance indicators of roadmap success. Via presentations and panel discussion, the outcome of this session will be an exploratory overview of best practices and key performance metrics, so that participants can apply knowledge gained from the workshop as they design strategic robotics policy frameworks for their own regional or national contexts.

Program Part TWO — Afternoon Session: “Towards a national robotics strategy for Canada”
The afternoon session will bring together leading Canadian robotics experts from academia, industry, federal/provincial policy, and the national research council to discuss and strategize the future of robotics in Canada, with an emphasis on addressing the social, economic, legal/ethical and regulatory issues, and the robotics strengths and capabilities specific to this country. The main goals of this session will be to 1) establish a clear picture of the internal Canadian robotics landscape and how it compares to other nations worldwide, 2) discuss lessons learned in the International session within the Canadian context, and 3) discuss and identify gaps and opportunities for a Canadian initiative. Ultimately the Canadian session will serve as a venue for collecting data and viewpoints to support the development of a Canadian Robotics Roadmap.

Who should attend
This workshop is open to all members of academia, government, and industry with an interest in funding, policy and research strategy.

Part One of the workshop (morning session) will be broadly applicable to anyone with an interest in robotics policy, partnerships and funding.

Part Two (afternoon session) will be of particular interest to Canadian conference-goers as well as those who are interested in Canadian research and industry partnerships.

Call for participation
We are actively seeking participants for this workshop. If you are …

… involved in developing or evaluating a major strategic robotics initiative in your region and would like to participate in our international discussion, OR …

… are a member of the Canadian robotics ecosystem or are a Canadian roboticist living abroad who would like to be involved in a national robotics strategy for Canada …

… then please send an email with your expression of interest to: info@canadianroboticsnetwork.com. We look forward to hearing from you!!

Workshop Organizers

• Elizabeth Croft, Director, Collaborative Advanced Robotics and Intelligent Systems (CARIS) Lab, UBC
• Clément Gosselin, Professor, Laboratoire de Robotique, Université Laval
• Paul Johnston, Research Policy Consultant, former President of Precarn Incorporated
• Dana Kulić, Associate Professor, Adaptive Systems Laboratory, University of Waterloo
• AJung Moon, Co-Founder & Director, Open Roboethics Institute
• Angela Schoellig, Assistant Professor, Institute for Aerospace Studies (UTIAS), University of Toronto
• Hallie Siegel, Strategic Foresight & Innovation @ OCAD U, former Managing Editor @Robohub

Workshop Registration

Registration for IROS workshops is separate from the IROS conference fee. Please see the IROS Website for details: http://www.iros2017.org/registration-travel/registration# 

Early workshop registration deadline: July 22nd.

More Info
http://canadianroboticsnetwork.com/iros-workshop/
http://www.iros2017.org/program/workshops-and-tutorials

Contact
info@canadianroboticsnetwork.com

A quick, hassle-free way to stay on top of robotics news, our robotics digest is released on the first Monday of every month. Sign up to get it in your inbox.

Robots, drones and AI in action

Let’s kick off our June review by looking at some great new robotics research and development in action: Inspired by arthropod insects and spiders, Harvard Professor George Whitesides and Alex Nemiroski—a former postdoctoral fellow in Whitesides’ Harvard lab—have created a type of semi-soft robot capable of standing and walking. The team also created a robotic water strider capable of pushing itself along the liquid surface. The robots are described in a recently published paper in the journal Soft Robotics.

And in news from the garden shed, Franklin Robotics has launched a Kickstarter campaign for Tertill, their solar-powered, garden-weeding robot. Tertill lives in your garden, collecting sunlight to power its weed patrol, and cutting down short plants with a string trimmer/weed whacker with almost no intervention required. Available for about $300USD, the fully autonomous Tertill is the first weeding robot available to home gardeners! Check out the video below.

You may have heard that humankind lost another important battle with artificial intelligence last month when AlphaGo beat the world’s leading Go player Ke Jie by three games to zero. AlphaGo is an AI program developed by DeepMind, part of Google’s parent company Alphabet. Last year it beat another leading player, Lee Se-dol, by four games to one, but since then AlphaGo has substantially improved. Ke Jie described AlphaGo’s skill as “like a God of Go”. But AlphaGo will now retire from playing Go, leaving behind a legacy of games played against itself. These games have been described by one Go expert as like “games from far in the future”, which humans will study for years to improve their own play.

Elsewhere, Chinese education authorities have gone high-tech to catch cheaters as millions of high-school students take their “gaokao”, the annual university entrance exam seen as key to landing a lucrative white-collar job. So high are the stakes and so competitive is the exam that some students resort to cheating. But not if these facial recognition drones can help it.

Meanwhile, back in the pub, students from the University of Leeds have created a robot which they claim is capable of pulling the perfect pint. The team, from the School of Mechanical Engineering, worked with local engineering company Quality Bearings, and Saltaire Brewery to come up with the concept. The robot was put through its paceswith a taste test, a consistency test and a wastage test. Check out the clip below.

At ICRA 2017, researchers from the Japan Aerospace Exploration Agency (JAXA) introduced a small robotic explorer that uses a single solid-fuel rocket to launch itself into the air. What’s new is that their robot includes some braking rockets that help it make pinpoint landings, as well as a clever gyroscopic system to make sure that it flies straight as well as providing a way for the robot to get around after landing.

In other flying news, a team of MIT engineers has come up with a much less expensive UAV design that can hover for longer durations to provide wide-ranging communications support. The researchers designed, built, and tested a UAV resembling a thin glider with a 24-foot wingspan. The vehicle can carry 4.5 to 9 kg of communications equipment while flying at an altitude of 15,000 feet. Weighing in at just under 68 kg, the vehicle is powered by a 5-horsepower gasoline engine and can keep itself aloft for more than five days — longer than any gasoline-powered autonomous aircraft has remained in flight, the researchers say. Check out the Jungle Hawk Owl’s maiden flight below.

Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) are aiming to develop flying robots that can both drive through a city-like setting with parking spots, no fly zones and landing pads. In a new paper, the team presented a system of eight quadcopter drones that can do all of that and more.

And last but not least: Authors of the ICRA 2017 Best Automation Paper “UAV-Based Crop and Weed Classification for Smart Farming” wrote about their findings for Robohub. Check it out.

Policy and financing

Europe needs a “human in command approach,” says the European Economic and Social Comitte (EESC). The EU must pursue a policy that ensures the development, deployment and use of artificial intelligence in Europe in favor, and not conducive to the detriment, of society and social welfare, the Committee said in an initiative opinion on the social impact of AI which 11 fields are identified for action.

Across the pond, some of the nation’s leading wireless giants and drone makers offered “effusive praise” of President Donald Trump in June as they lobbied his administration to eliminate the federal regulations that stand in the way of their businesses. As part of the White House’s five-day focus on technology, Trump gathered executives from those industries—including AT&T CEO Randall Stephenson, PrecisionHawk CEO Michael Chasen and a number of venture capitalists—for a morning of brainstorming sessions devoted to spurring new investments in emerging fields.

And in a long-awaited business transaction, The New York Times Dealbook announced that SoftBank was buying Boston Dynamics from Alphabet (Google). Also included in the deal is the Japanese startup Schaft. Acquisition details were not disclosed. Both Boston Dynamics and Schaft were acquired by Google when Andy Rubin was developing Google’s robot group through a series of acquisitions. Both companies have continued to develop innovative mobile robots. And both have been on Google’s “for sale” list.

The Drone Racing League (DRL) has announced the closing of a $20 million Series B round of financing, bringing the total amount raised to $32 million. The new round of financing was led by Sky, Liberty Media LMCA +% (owner of Formula 1) and Lux Capital, with involvement by a couple of new investors in Allianz and World Wrestling Entertainment (WWE). Investment from Allianz was expected after it was announced that Allianz signed on to become title sponsor of DRL’s elite racing circuit in February.

Pilots are gearing up to compete in the DRL. Image: Drone Racing League.

Health and medicine

Researchers from MIT’s CSAIL have developed a new system that uses a 3-D camera, a belt with separately controllable vibrational motors distributed around it, and an electronically reconfigurable Braille interface to give visually impaired users more information about their environments.

New algorithms power a prototype system for helping visually impaired users avoid obstacles and identify objects. Courtesy of the researchers.

Elsewhere, a researcher at the University of the West of England (UWE Bristol) is developing a bio-inspired ‘smart’ knee joint for prosthetic lower limbs. Dr Appolinaire Etoundi, based at Bristol Robotics Laboratory, is leading the research and will analyse the functions, features and mechanisms of the human knee in order to translate this information into a new bio-inspired procedure for designing prosthetics.

And the medical innovations didn’t stop there in June. How about Robot snakes slithering into the delicate field of heart surgery? Or a robotic doctor that can be controlled hundreds of kilometres away by a human counterpart? Getting a check-up from a robot may sound like something from a sci-fi film, but scientists are closing in on this real-life scenario and have already tested a prototype.

An aging population means the age-dependency ratio—the proportion of the elderly compared with the number of workers—will almost double from 28.8 % in 2015 to 51 % in 2080, straining healthcare systems and national budgets. The creators of one humanoid robot (below) under development for the elderly say it can understand people’s actions and learn new behaviors in response, even though it is devoid of arms. Robots can be programmed to understand an elderly person’s preferences and habits to detect changes in behavior: for example if a yoga devotee misses a class, it will ask why, while if an elderly person falls it will automatically alert caregivers or emergency services.

Humanoid robots under development can be programmed to detect changes in an elderly person’s preferences and habits. Image credit: GrowMeUp

Self-driving news

June was a huge month of wheeling and dealing in the self-driving cars industry. NuTonomy, a small Boston startup that makes software for self-driving cars, has launched a research-and-development partnership with San Francisco’s Lyft Inc., the second-largest ride-hailing company in the United States. It’s the latest alliance between Lyft and a maker of autonomous vehicle technology, and could boost nuTonomy’s efforts to become a major force in self-driving vehicles. Lyft chief executive Logan Green said the partnership “could lead to thousands of Lyft cars on the nuTonomy platform.”

Source: Uber

Meanwhile, competitors Uber, the global ride-sharing transportation company, named two replacements to recover from the recent firing of Anthony Levandowski who headed their Advanced Technologies Group, their OTTO trucking unit, and their self-driving team. Levandowski was fired May 30th. Eric Meyhofer and Anthony Levandowski will pick up the slack.

In other high-level firing news, Tesla Inc. has parted ways with another senior leader on its self-driving technology team, adding more turmoil to a program that is under pressure to meet the grand ambitions of Chief Executive Elon Musk. The Silicon Valley electric-car maker said Chris Lattner—head of development of Tesla’s Autopilot program—left his post as after he and Musk failed to see eye to eye on some important issues during Lattner’s six months in post.

Meanwhile, Waymo is done driving around the cute, steering-wheel-free autonomous cars that were introduced by Google back in 2014. In a blog post, Waymo leaders write that time has come to “retire our fleet of Fireflies” —their name for the tiny cars—and focus instead of integrating self-driving technology into other vehicles, like the Chrysler Pacifica minivans Waymo put on the road earlier this year.

Source: Waymo

In the UK, Venturer driverless car project published results of their first trials. Venturer is the first Connected and Autonomous Vehicle project to start in the UK. The results of Venturer’s preliminary trials show that the handover process is a safety critical issue in the development of Autonomous Vehicles. The first Venturer  trials set out to investigate ‘takeover’ (time taken to reengage with vehicle controls) and ‘handover’ (time taken to regain a baseline/normal level of driving behavior and performance) when switching frequently between automated and manual driving modes within urban and extra-urban settings. This trial is believed to be the first to directly compare handover to human driver-control from autonomous mode in both simulator and autonomous road vehicle platforms.

Honda was the latest automaker to commit to an ambitious self-driving car goal in June. It wants cars with SAE Level 4 autonomy on the road by 2025, CEO Takahiro Hachigo announced at a media event in Japan. This news will likely stoke a fire underneath other Japanese self-driving car developers, so stay tuned for lots of new developments.

Auto supplier Robert Bosch GmbH will build a 1 billion-euro ($1.1 billion) semiconductor plant, the biggest single investment in its history, as the maker of brakes and engines prepares for a surge in demand for components used in self-driving vehicles.

Tutorials

Robohub showcased some great tutorials last month. Here’s one: The Robot Academy is a new learning resource from Professor Peter Corke and the Queensland University of Technology, the team behind the award-winning Introduction to Robotics and Robotic Vision courses. There are over 200 lessons available, all for free. The courses were designed for university undergraduate students but many lessons are suitable for anybody! So get stuck in.

Florian Enner offered this useful tutorial on programming: “Using MATLAB for hardware-in-the-loop prototyping #1 : Message passing systems”. Check it out here.

And Ricardo Téllez offered an article on “Teaching ROS quickly to students“—a novel method of teaching to class of students in a rapid time frame.

Enjoy!

Upcoming events for July– August 2017

CIROS: July 5, 2017 — July 8, 2017, Shanghai, China

ASCEND Conference and Expo: July 19, 2017 — July 21, 2017, Portland, OR

RoboCup: July 25, 2017 — July 31, 2017, Nagoya, Japan

Farm Progress Show: August 29, 2017 — August 31, 2017, Decatur, IL

World of Drones Congress (WoDC): August 31, 2017 — September 2, 2017, Brisbane, Australia

Driverless car merging into traffic. How big of a gap between vehicles is acceptable? Image credit: Jordan Collver

I’m examining the perception of autonomous cars using hypothetical scenarios. Each of the hypothetical scenarios is accompanied with an image to help illustrate the scene — using grey tones and nondescript human-like features — along with the option to listen to the question spoken out loud to fully visualise an association. 

If you live in the UK, you can take this survey and help contribute to my research!

Public perception has the potential to impact on the timescale and adoption of autonomous vehicles (AV). As the development of the technology advances, understanding attitudes and wider public acceptability is critical. It’s no longer a question of if, but when we will transition. Long range autonomous vehicles are expected between 2020 and 2025, with some estimates suggesting fully autonomous vehicles will take over by 2030. Currently, most modern cars are sold with automated features: automatic braking, autonomous parking, advanced lane assist, advanced cruise control, queue assist, for example. Adopting fully AV has the potential to improve significant societal aspects: efficient road safety, reducing pollution and congestion, and providing another type of transportation for the mobility impaired.

The project’s aim is to add to the conversation about public perception of AV. Survey experiments can be extremely useful tools for studying public attitudes, especially if researchers are fascinated by the “effects of describing or presenting a scenario in a particular way.”  This unusual and creative method may provide a model for other types of research surveys in the future where it’s difficult to visualise future technologies. An online survey was chosen to remove small sample bias and maximise responses by participants in the UK.

You can take this survey by clicking above, or alternatively, click the following link:

https://uwe.onlinesurveys.ac.uk/visualise-this

By Eran Klein, University of Washington and Katherine Pratt, University of Washington

In the 1995 film “Batman Forever,” the Riddler used 3-D television to secretly access viewers’ most personal thoughts in his hunt for Batman’s true identity. By 2011, the metrics company Nielsen had acquired Neurofocus and had created a “consumer neuroscience” division that uses integrated conscious and unconscious data to track customer decision-making habits. What was once a nefarious scheme in a Hollywood blockbuster seems poised to become a reality.

Recent announcements by Elon Musk and Facebook about brain-computer interface (BCI) technology are just the latest headlines in an ongoing science-fiction-becomes-reality story.

BCIs use brain signals to control objects in the outside world. They’re a potentially world-changing innovation – imagine being paralyzed but able to “reach” for something with a prosthetic arm just by thinking about it. But the revolutionary technology also raises concerns. Here at the University of Washington’s Center for Sensorimotor Neural Engineering (CSNE) we and our colleagues are researching BCI technology – and a crucial part of that includes working on issues such as neuroethics and neural security. Ethicists and engineers are working together to understand and quantify risks and develop ways to protect the public now.

Picking up on P300 signals

All BCI technology relies on being able to collect information from a brain that a device can then use or act on in some way. There are numerous places from which signals can be recorded, as well as infinite ways the data can be analyzed, so there are many possibilities for how a BCI can be used.

Some BCI researchers zero in on one particular kind of regularly occurring brain signal that alerts us to important changes in our environment. Neuroscientists call these signals “event-related potentials.” In the lab, they help us identify a reaction to a stimulus.

In particular, we capitalize on one of these specific signals, called the P300. It’s a positive peak of electricity that occurs toward the back of the head about 300 milliseconds after the stimulus is shown. The P300 alerts the rest of your brain to an “oddball” that stands out from the rest of what’s around you.

For example, you don’t stop and stare at each person’s face when you’re searching for your friend at the park. Instead, if we were recording your brain signals as you scanned the crowd, there would be a detectable P300 response when you saw someone who could be your friend. The P300 carries an unconscious message alerting you to something important that deserves attention. These signals are part of a still unknown brain pathway that aids in detection and focusing attention.

Reading your mind using P300s

P300s reliably occur any time you notice something rare or disjointed, like when you find the shirt you were looking for in your closet or your car in a parking lot. Researchers can use the P300 in an experimental setting to determine what is important or relevant to you. That’s led to the creation of devices like spellers that allow paralyzed individuals to type using their thoughts, one character at a time.

It also can be used to determine what you know, in what’s called a “guilty knowledge test.” In the lab, subjects are asked to choose an item to “steal” or hide, and are then shown many images repeatedly of both unrelated and related items. For instance, subjects choose between a watch and a necklace, and are then shown typical items from a jewelry box; a P300 appears when the subject is presented with the image of the item he took.

Everyone’s P300 is unique. In order to know what they’re looking for, researchers need “training” data. These are previously obtained brain signal recordings that researchers are confident contain P300s; they’re then used to calibrate the system. Since the test measures an unconscious neural signal that you don’t even know you have, can you fool it? Maybe, if you know that you’re being probed and what the stimuli are.

Techniques like these are still considered unreliable and unproven, and thus U.S. courts have resisted admitting P300 data as evidence.

Imagine that instead of using a P300 signal to solve the mystery of a “stolen” item in the lab, someone used this technology to extract information about what month you were born or which bank you use – without your telling them. Our research group has collected data suggesting this is possible. Just using an individual’s brain activity – specifically, their P300 response – we could determine a subject’s preferences for things like favorite coffee brand or favorite sports.

But we could do it only when subject-specific training data were available. What if we could figure out someone’s preferences without previous knowledge of their brain signal patterns? Without the need for training, users could simply put on a device and go, skipping the step of loading a personal training profile or spending time in calibration. Research on trained and untrained devices is the subject of continuing experiments at the University of Washington and elsewhere.

It’s when the technology is able to “read” someone’s mind who isn’t actively cooperating that ethical issues become particularly pressing. After all, we willingly trade bits of our privacy all the time – when we open our mouths to have conversations or use GPS devices that allow companies to collect data about us. But in these cases we consent to sharing what’s in our minds. The difference with next-generation P300 technology under development is that the protection consent gives us may get bypassed altogether.

What if it’s possible to decode what you’re thinking or planning without you even knowing? Will you feel violated? Will you feel a loss of control? Privacy implications may be wide-ranging. Maybe advertisers could know your preferred brands and send you personalized ads – which may be convenient or creepy. Or maybe malicious entities could determine where you bank and your account’s PIN – which would be alarming.

With great power comes great responsibility

The potential ability to determine individuals’ preferences and personal information using their own brain signals has spawned a number of difficult but pressing questions: Should we be able to keep our neural signals private? That is, should neural security be a human right? How do we adequately protect and store all the neural data being recorded for research, and soon for leisure? How do consumers know if any protective or anonymization measures are being made with their neural data? As of now, neural data collected for commercial uses are not subject to the same legal protections covering biomedical research or health care. Should neural data be treated differently?

These are the kinds of conundrums that are best addressed by neural engineers and ethicists working together. Putting ethicists in labs alongside engineers – as we have done at the CSNE – is one way to ensure that privacy and security risks of neurotechnology, as well as other ethically important issues, are an active part of the research process instead of an afterthought. For instance, Tim Brown, an ethicist at the CSNE, is “housed” within a neural engineering research lab, allowing him to have daily conversations with researchers about ethical concerns. He’s also easily able to interact with – and, in fact, interview – research subjects about their ethical concerns about brain research.

There are important ethical and legal lessons to be drawn about technology and privacy from other areas, such as genetics and neuromarketing. But there seems to be something important and different about reading neural data. They’re more intimately connected to the mind and who we take ourselves to be. As such, ethical issues raised by BCI demand special attention.

Working on ethics while tech’s in its infancy

As we wrestle with how to address these privacy and security issues, there are two features of current P300 technology that will buy us time.

First, most commercial devices available use dry electrodes, which rely solely on skin contact to conduct electrical signals. This technology is prone to a low signal-to-noise ratio, meaning that we can extract only relatively basic forms of information from users. The brain signals we record are known to be highly variable (even for the same person) due to things like electrode movement and the constantly changing nature of brain signals themselves. Second, electrodes are not always in ideal locations to record.

All together, this inherent lack of reliability means that BCI devices are not nearly as ubiquitous today as they may be in the future. As electrode hardware and signal processing continue to improve, it will be easier to continuously use devices like these, and make it easier to extract personal information from an unknowing individual as well. The safest advice would be to not use these devices at all.

The goal should be that the ethical standards and the technology will mature together to ensure future BCI users are confident their privacy is being protected as they use these kinds of devices. It’s a rare opportunity for scientists, engineers, ethicists and eventually regulators to work together to create even better products than were originally dreamed of in science fiction.

WeRobotics Global has become a premier forum for social good robotics. The feedback featured below was unsolicited. On June 1, 2017, we convened our first, annual global event, bringing together 34 organizations to New York City (full list below) to shape the global agenda and future use of robotics in the social good sector.  WeRobotics Global was kindly hosted by the Rockefeller Foundation, the first donor to support our efforts. They opened the event with welcome remarks and turned it over to Patrick Meier from WeRobotics who provided an overview of WeRobotics and the big picture context for social sector robotics.

I’ve been to countless remote sensing conferences over the past 30 years but WeRobotics Global absolutely ranks as the best event I’ve been to – Remote Sensing Expert

The event was really mind-blowing. I’ve participated in many workshops over the past 20 years. WeR Global was by far the most insightful and practical. It is also amazing how closely together everyone is working — irrespective of who is working where (NGO, UN, private sector, donor). I’ve never seen such a group of people come together this away. – Humanitarian Professional

WeRobotics Global is completely different to any development meeting or workshop I’ve been to in recent years. The discussions flowed seamlessly between real world challenges, genuine bottom-up approaches and appropriate technology solutions. Conversations were always practical and strikingly transparent. This was a highly unusual event. – International Donor

The first panel featured our Flying Labs Coordinators from Tanzania (Yussuf), Peru (Juan) and Nepal (Uttam). Each shared the hard work they’ve been doing over the past 6-10 months on localizing and applying robotics solutions. Yussuf spoke about the lab’s use of aerial robotics for disaster damage assessment following the earthquake in Bukoba and for coastal monitoring, environmental monitoring and forestry management. He emphasized the importance of community engagement and closed with new projects that Tanzania Flying Labs is working on such as mangrove monitoring for the Department of Forestry. Juan presented the work of the labs in the Amazon Rainforest, which is a joint effort with the Peruvian Ministry of Health. Together, they are field-testing the use of affordable and locally repairable flying robots for the delivery of antivenom and other medical payload between local clinics and remote villages. Juan noted that Peru Flying Labs is gearing up to carry out a record number of flight tests this summer using a larger and more diverse fleet of flying robots. Last but not least, Uttam showed how Nepal Flying Labs has been using flying robots for agriculture monitoring, damage assessment and mapping of property rights. He also gave an overview of the social entrepreneurship training and business plan competition recently organized by Nepal Flying Labs. This business incubation training has resulted in the launch of 4 new Nepali start-up companies focused on Robotics-as-a-Service. 

The following images provide highlights from each of our Flying Labs: Tanzania, Peru and Nepal.

The second panel featured talks on sector based solutions starting with the International Federation of the Red Cross (IFRC). The Federation (Aarathi) spoke about their joint project with WeRobotics; looking at cross-sectoral needs for various robotics solutions in the South Pacific. IFRC is exploring at the possibility of launching a South Pacific Flying Labs with a strong focus on women and girls. Pix4D (Lorenzo) addressed the role of aerial robotics in agriculture, giving concrete examples of successful applications while providing guidance to our Flying Labs Coordinators. The Wall Street Journal (Sally) spoke about the use of aerial robotics in news gathering and investigative journalism. She specifically emphasized the importance of using flying robots for storytelling. Duke Marine Labs (David) closed the panel with an overview of their projects in nature conservation and marine life protection, highlighting their use of machine learning for automated feature detection for real-time analysis.

Panel number three addressed the transformation of transportation. UNICEF (Judith) highlighted the field tests they have been carrying out in Malawi; using cargo robotics to transport HIV samples in order to accelerate HIV testing and thus treatment. UNICEF has also launched an air corridor in Malawi to enable further field-testing of flying robots. MSF (Oriol) shared their approach to cargo delivery using aerial robotics. They shared examples from Papua New Guinea (PNG) and emphasized the importance of localizing appropriate robotics solutions that can be maintained locally. MSF also called for the launch of PNG Flying Labs. IAEA was unable to attend WeR Global, so Patrick and Adam from WeRobotics gave the talk instead. WeRobotics is teaming up with IAEA to design and test a release mechanism for sterilized mosquitos in order to reduce the incidence of Zika and other mosquito-borne illnesses. More here. Finally, Llamasoft (Sid) closed the panel with a strong emphasis on the need to collect and share structured data to accurately carry out comparative cost-benefit-analyses of cargo delivery via flying robots versus conventional means. Sid used the analogy of self-driving cars to highlight how problematic the current lack of data vis-a-vis reliably evaluating the impact of cargo robotics.

The fourth and final panel went beyond aerial robotics. Digger (Thomas) showed how they convert heavy construction vehicles into semi-autonomous platforms to clear landmines and debris in conflict zones like Iraq and Syria. Science in the Wild (Ulyana) was alas unable to attend the event, so Patrick from WeRobotics gave the talk instead. This focused on the use of swimming robots to monitor glacial lakes in the Himalaya. The purpose of the effort is to identify cracks in the lake floors before they trigger what local villagers call the tsunamis of the Himalaya. OpenROV (David) gave a talk on the use of diving robots, sharing real-world examples and providing exciting updates on the new Trident diving robot. Planet Labs (Andrew) gave the closing talk, highlighting how space robotics (satellites) are being used across a wide range of social good projects. He emphasized the importance of integrating both aerial and satellite imagery to support social good projects.

The final session at WeR Global comprised breakout groups to identify next steps for WeRobotics and the social good sector more broadly. Many quality insights and recommendations were shared during the report back. One such recommendation was to hold WeR Global again, and sooner rather than later. So we look forward to organizing WeRobotics Global 2018. We will be providing updates via our blog and email list. We will also use our blog and email list to share select videos of the individual talks from Global 2017 along with their respective slide decks.

In the meantime, a big thanks to all participants and speakers for making Global 2017 such an unforgettable event. And sincerest thanks to the Rockefeller Foundation for hosting us at their headquarters in New York City.

Playing Go. Image: CC0

Humankind lost another important battle with artificial intelligence (AI) last month when AlphaGo beat the world’s leading Go player Ke Jie by three games to zero.

AlphaGo is an AI program developed by DeepMind, part of Google’s parent company Alphabet. Last year it beat another leading player, Lee Se-dol, by four games to one, but since then AlphaGo has substantially improved.

Ke Jie described AlphaGo’s skill as “like a God of Go”.

AlphaGo will now retire from playing Go, leaving behind a legacy of games played against itself. They’ve been described by one Go expert as like “games from far in the future”, which humans will study for years to improve their own play.

Ready, set, Go

Go is an ancient game that essentially pits two players – one playing black pieces the other white – for dominance on board usually marked with 19 horizontal and 19 vertical lines.

A typical game of Go: simple to learn but a lifetime to master. Flickr/Alper Cugun, CC BY

Go is a far more difficult game for computers to play than chess, because the number of possible moves in each position is much larger. This makes searching many moves ahead – feasible for computers in chess – very difficult in Go.

DeepMind’s breakthrough was the development of general-purpose learning algorithms that can, in principle, be trained in more societal-relevant domains than Go.

DeepMind says the research team behind AplhaGo is looking to pursue other complex problems, such as finding new cures for diseases, dramatically reducing energy consumption or inventing revolutionary new materials. It adds:

If AI systems prove they are able to unearth significant new knowledge and strategies in these domains too, the breakthroughs could be truly remarkable. We can’t wait to see what comes next.

This does open up many opportunities for the future, but challenges still remain.

Neuroscience meets AI

AlphaGo combines the two most powerful ideas about learning to emerge from the past few decades: deep learning and reinforcement learning. Remarkably, both were originally inspired by how biological brains learn from experience.

In the human brain, sensory information is processed in a series of layers. For instance, visual information is first transformed in the retina, then in the midbrain, and then through many different areas of the cerebral cortex.

This creates a hierarchy of representations where simple, local features are extracted first, and then more complex, global features are built from these.

The AI equivalent is called deep learning; deep because it involves many layers of processing in simple neuron-like computing units.

But to survive in the world, animals need to not only recognise sensory information, but also act on it. Generations of scientists and psychologists have studied how animals learn to take a series of actions that maximise their reward.

This has led to mathematical theories of reinforcement learning that can now be implemented in AI systems. The most powerful of these is temporal difference learning, which improves actions by maximising its expectation of future reward.

The best moves

By combining deep learning and reinforcement learning in a series of artificial neural networks, AlphaGo first learned human expert-level play in Go from 30 million moves from human games.

But then it started playing against itself, using the outcome of each game to relentlessly refine its decisions about the best move in each board position. A value network learned to predict the likely outcome given any position, while a policy network learned the best action to take in each situation.

Although it couldn’t sample every possible board position, AlphaGo’s neural networks extracted key ideas about strategies that work well in any position. It is these countless hours of self-play that led to AlphaGo’s improvement over the past year.

Unfortunately, as yet there is no known way to interrogate the network to directly read out what these key ideas are. Instead we can only study its games and hope to learn from these.

This is one of the problems with using such neural network algorithms to help make decisions in, for instance, the legal system: they can’t explain their reasoning.

We still understand relatively little about how biological brains actually learn, and neuroscience will continue to provide new inspiration for improvements in AI.

Humans can learn to become expert Go players based on far less experience than AlphaGo needed to reach that level, so there is clearly room for further developing the algorithms.

Also much of AlphaGo’s power is based on a technique called back-propagation learning that helps it correct errors. But the relationship between this and learning in real brains is still unclear.

What’s next?

The game of Go provided a nicely constrained development platform for optimising these learning algorithms. But many real world problems are messier than this and have less opportunity for the equivalent of self-play (for instance self-driving cars).

So are there problems to which the current algorithms can be fairly immediately applied?

One example may be optimisation in controlled industrial settings. Here the goal is often to complete a complex series of tasks while satisfying multiple constraints and minimising cost.

As long as the possibilities can be accurately simulated, these algorithms can explore and learn from a vastly larger space of outcomes than will ever be possible for humans. Thus DeepMind’s bold claims seem likely to be realised, and as the company says, we can’t wait to see what comes next.

This article was originally published on The Conversation. Read the original article.

The world’s brightest minds in Artificial Intelligence (AI) and humanitarian action will meet with industry leaders and academia at the AI for Good Global Summit, 7-9 June 2017, to discuss how AI will assist global efforts to address poverty, hunger, education, healthcare and the protection of our environment. The event will in parallel explore means to ensure the safe, ethical development of AI, protecting against unintended consequences of advances in AI.

View the live webcast at: http://bit.ly/AI-for-Good-Webcast.

The event is co-organized by ITU and the XPRIZE Foundation, in partnership with 20 other United Nations (UN) agencies, and with the participation of more than 70 leading companies and academic and research institutes.

“Artificial Intelligence has the potential to accelerate progress towards a dignified life, in peace and prosperity, for all people,” said UN Secretary-General António Guterres. “The time has arrived for all of us – governments, industry and civil society – to consider how AI will affect our future. The AI for Good Global Summit represents the beginnings of our efforts to ensure that AI charts a course that will benefit all of humanity.”

The AI for Good Global Summit will emphasize AI’s potential to contribute to the pursuit of the UN Sustainable Development Goals.

Opening sessions will share expert insight into the state of play in AI, with leading minds in AI giving voice to their greatest ambitions in driving AI towards social good. ‘Breakthrough’ sessions will propose strategies for the development of AI applications and systems able to promote sustainable living, reduce poverty and deliver citizen-centric public services.

“Today, we’ve gathered here to discuss how far AI can go, how much it will improve our lives, and how we can all work together to make it a force for good,” said ITU Secretary-General Houlin Zhao. “This event will assist us in determining how the UN, ITU and other UN Agencies can work together with industry and the academic community to promote AI innovation and create a good environment for the development of artificial intelligence.”

“The AI for Good Global Summit has assembled an impressive, diverse ecosystem of thought leaders who recognize the opportunity to use AI to solve some of the world’s grandest challenges,” said Marcus Shingles, CEO of the XPRIZE Foundation. “We look forward to this Summit providing a unique opportunity for international dialogue and collaboration that will ideally start to pave the path forward for a new future of problem solvers working with XPRIZE and beyond.”

The AI for Good Global Summit will be broadcast globally as well as captioned to ensure accessibility.

You can view the live webcast at: http://bit.ly/AI-for-Good-Webcast.

For more information about this event, please visit: AI for Good Global Summit web page.

The device named “Spark” flew high above the man on stage with his hands waving in the direction of the flying object. In a demonstration of DJI’s newest drone, the audience marveled at the Coke can-sized device’s most compelling feature: gesture controls. Instead of a traditional remote control, this flying selfie machine follows hand movements across the sky. Gestures are the most innate language of mammals, and including robots in our primal movements means we have reached a new milestone of co-existence.

Madeline Gannon of Carnegie Mellon University is the designer of Mimus, a new gesture controlled robot featured in an art installation at The Design Museum in London, England. Gannon explained: “In developing Mimus, we found a way to use the robot’s body language as a medium for cultivating empathy between museum-goers and a piece of industrial machinery. Body language is a primitive yet fluid means of communication that can broadcast an innate understanding of the behaviors, kinematics and limitations of an unfamiliar machine.” Gannon wrote about her experiences recently in the design magazine Dezeen: “In a town like Pittsburgh, where crossing paths with a driverless car is now an everyday occurrence, there is still no way for a pedestrian to read the intentions of the vehicle…it is critical that we design more effective ways of interacting and communicating with them.”

So far, the biggest commercially deployed advances in human-robot interactions have been conversational agents by Amazon, Google and Apple. While natural language processing has broken new ground in artificial intelligence, the social science of its acceptability in our lives might be its biggest accomplishment. Japanese roboticist Masahiro Mori described the danger of making computer generated voices too indistinguishable from humans as the “uncanny valley.” Mori cautioned inventors from building robots that are too human sounding (and possibly looking) as the result elicits negative emotions best described as “creepy” and “disturbing.”

Recently, many toys have embraced conversational agents as a way of building greater bonds and increasing the longevity of play with kids. Barbie’s digital speech scientist, Brian Langner of ToyTalk, detailed his experiences with crossing into the “Uncanny Valley” as: “Jarring is the way I would put it. When the machine gets some of those things correct, people tend to expect that it will get everything correct.”

Kate Darling of MIT’s Media Lab, whose research centers on human-robot interactions, suggested that “if you get the balance right, people will like interacting with the robot, and will stop using it as a device and start using it as a social being.”

This logic inspired Israeli startup Intuition Robotics to create ElliQ—a bobbing head (eyeless) robot. The purpose of the animatronics is to break down barriers between its customer base of elderly patients and their phobias of technology. According to Intuition Robotics’ CEO, Dor Skuler, the range of motion coupled with a female voice helps create a bond between the device and its user. Don Norman, usability designer of ElliQ, said: “It looks like it has a face even though it doesn’t. That makes it feel approachable.”

Mayfield Robotics decided to add cute R2D2-like sounds to its newest robot, Kuri. Mayfield hired former Pixar designers Josh Morenstein and Nick Cronan of Branch Creative with the sole purpose of making Kuri more adorable. To accomplish this mission, Morenstein and Cronan gave Kuri eyes, but not a mouth as that would be, in their words “creepy.” Conan shares the challenges with designing the eyes: “Just by moving things a few millimeters, it went from looking like a dumb robot to a curious robot to a mean robot. It became a discussion of, how do we make something that’s always looking optimistic and open to listen to you?” Kuri has a remarkable similarity to Morenstein and Cronan’s former theatrical robot, EVA.

In the far extreme of making robots act and behave human, RealDoll has been promoting six thousand dollar sex robots. To many, RealDoll has crossed the “Uncanny Valley” of creepiness with sex dolls that look and talk like humans. In fact, there is a growing grassroots campaign to ban RealDoll’s products globally, as it endangers the very essence of human relationships. Florence Gildea writes on the organization’s blog: “The personalities and voices that doll owners project onto their dolls is pertinent for how sex robots may develop, given that sex doll companies like RealDoll are working on installing increasing AI capacities in their dolls and the expectation that owners will be able to customize their robots’ personalities.” The example given is how the doll expresses her “feelings” for her owner on Twitter:

Obviously a robot companion has no feelings, however it is a projection of the doll owners’. “To anthropomorphize their dolls to sustain the fantasy that they have feelings for the owner. The Twitter accounts seemingly manifest the dolls’ independent existence so that their dependence on their owners can seem to signify their emotional attachment, rather than it following inevitable from their status as objects. Immobility, then, can be misread as fidelity and devotion.” The implications of this behavior is that their female companion, albeit mechanical, enjoys “being dominated.” The fear that the Campaign Against Sex Robots expresses is the objectification of women (even robotic ones) reinforces problematic human sexual stereotypes.

Today, with technology at our fingertips, there is growing phenomena of preferring one-directional device relationships over complicated human encounters. MIT Social Sciences Professor Sherry Turkle writes in her essay, Close Engagements With Artificial Companionship, that “over-stressed, overworked, people claim exhaustion and overload. These days people will admit they’d rather leave a voice mail or send an email than talk face-to-face. And from there, they say: ‘I’d rather talk to the robot. Friends can be exhausting. The robot will always be there for me. And whenever I’m done, I can walk away.’”

In the coming years humans will communicate more with robots in their lives from experiences in the home to the office to their leisure time. The big question will be not the technical barriers, but the societal norms that will evolve to accept Earth’s newest species.

“What do we think a robot is?” asked robot designer Norm. “Some people think it should look like an animal or a person, and it should move around. Or it just has to be smart, sense the environment, and have motors and controllers.”

Norm’s answer, like beauty, could be in the eye of the beholder.

After a successful 2016 first edition, our next summer school cohort on The Regulation of Robotics in Europe: Legal, Ethical and Economic Implications will take place in Pisa at the Scuola Sant’Anna, from 3- 8 July.

When the Robolaw project came to an end – and we presented our results before the European Parliament – we clearly perceived that a leap was needed not only in some approaches to regulation but also in the way social scientists, as well as engineers, are trained.

Indeed, in order to undergo technical analysis in law and robotics, without being lured into science fiction, an adequate understanding of the peculiarities of the systems being studied is required. A bottom-up approach, like the one adopted by Robolaw and its guidelines, is essential.

Social scientists, and lawyers in particular, often lack such knowledge and thus tend to either make unreasonable assumptions – of technological developments that are farfetched or simply unrealistic – or misperceive what the pivoting point in the analysis is going to be. The notion of autonomy is a fitting example. The consequence, however, is not simply bad scientific literature, but potentially inadequate policies being developed, thence wrong decision – even legislative ones – being adopted, impacting research and development of new applications, while overlooking relevant issues and impairments.

Similarly, engineers working in robotics are often confronted with philosophical and legal debates involving the devices they research that they are not always equipped to understand. Those debates are instead precious for they allow to identify societal concerns and expectations that can be used to orient research strategically, and engineers ought also participate and have a say.

Ultimately, it is everybody’s interest to better address existing and emerging needs, fulfilling desires and avoiding eliciting – often ungrounded – fears. This is what the European Union understands as Responsible Research and Innovation, but it also the prerequisite for the diffusion of new applications in society and the emergence of a sound robotic industry. Moreover, the current tendency in EU regulation favouring by design approaches – whereby privacy or other rights need to be enforced through the very functioning of the device – require technicians to consider such concerns early on, during the development phase of their products.

A common language needs thus be created, to avoid a babel-tower effect, that preserves each one’s peculiarities and specificities, yet allowing close cooperation.

A multidisciplinary approach, grounded in philosophy – ethics in particular –, law – and law and economics methodologies – economics and innovation management, and engineering is required.

With that idea in mind, we competed and won a Jean Monnet grant – a prestigious funding action of the EU Commission, mainly directed towards the promotion of education and teaching activities – with a project titled: Europe Regulates Robotics and organized the first edition of the Summer School The Regulation of Robotics in Europe: Legal, Ethical and Economic Implications in 2016.

The opening event of the Summer School saw the participation of MEP (Member of the EU Parliament) Mady Delvaux-Stehres, who presented what was then the draft recommendation – now approved – of the EU Parliament on the Civil Law Rules of Robotics, Mihalis Kritikos – a Policy Analyst of the Parliament –, who personally contributed to the drafting of that document, Maria Chiara Carrozza – former Italian minister of University Education and Research, professor of robotics and member of the Italian Senate – discussing Italian political initiatives.  We also had entrepreneurs, such as Roberto Valleggi, and engineers coming from the industry, such as Arturo Baroncelli – from Comau – and academia, Fabio Bonsignorio, who also taught the course.

Classes dealt with methodologies – the Robolaw approach – notions of autonomy, liability – and different liability models – privacy, benchmarking and robot design, machine ethics and human enhancement through technology, innovation management and technology assessment. Students also had the chance to visit the Biorobotics Institute laboratories in Pontedera (directed by Prof. Paolo Dario) and see many different applications and research being carried out, directly explained by the people who work on them.

The most impressive part was, however, our class. We managed to put together a truly international group of young and bright minds, many of which already enrolled in a PhD program – in law, philosophy, engineering and management – coming from Universities such as Edinburgh, London School of Economics, Sorbonne, Cambridge, Vienna, Bologna, Suor Orsola, Bicocca, Milan, Hannover, Pisa, Pavia and Freiburg. Other came from prominent European robotic industries, were practitioners, entrepreneurs, policy makers from EU institutions.

At the end of the Summer School, some presented their research on a broad set of extremely interesting topics, such as driverless car liability and innovation management, machine ethics and the trolley problem, anthrobotics and decision-making in natural and artificial systems.

We had vivid in class debates. A true community was created that is still in contact today. Five of our students actively participated in the 2017 European Robotics Forum in Edinburgh and more are working and publishing on such matters.

We can say we truly achieved our goal! However, the challenge has just begun. We want to reach out to more people and replicate this successful initiative. A second edition of the Summer School will take place again this year in Pisa at the Scuola Sant’Anna from July 3^rd to 8^th and we are accepting applications until June 7^th.

I am certain we will manage again to put together an incredible group of individuals, and I can’t wait to meet our new class. On our side, we are preparing a lot of interesting surprises for them, including the participation of policy makers involved in the regulation of robotics at EU level to provide a first-hand look at what is happening in the EU.

More information about the summer school can be found on our website here.

Registration to the summer school can be found here.

Credit: EESC

The EU must pursue a policy that ensures the development, deployment and use of artificial intelligence (AI) in Europe in favor, and not conducive to the detriment, acts of society and social welfare, the Committee said in an initiative opinion on the social impact of AI which 11 fields are identified for action.

“We have a human need in-command approach to artificial intelligence, with machines remain machines and people always maintain control of these machines” said rapporteur Catelijne Muller (NL – Workers’ Group). She was not just about technical check: “People can and must decide whether, when and how AI is used in our daily lives, what tasks we entrust to AI and how transparent and ethical is all. Eventually, we decide whether we want that certain activities are carried out, care or medical decisions are made by AI, and whether we want to accept that our AI security, privacy and autonomy might be jeopardized,” said Mrs. Muller.

Artificial intelligence has recently undergone rapid growth. The size of the market for AI is approximately USD 664 million and is expected to increase to 38.8 billion USD in 2025. It is virtually undisputed that artificial intelligence can have great social benefits: consider applications for sustainable agriculture, environmentally friendly production, safer traffic safety work, a safer financial system, better medicine and more. Artificial intelligence can even possibly contribute to the eradication of disease and poverty.

But the benefits of AI can only be realized as well as the challenges to be addressed related to it. The Committee has identified 11 areas in which AI ensures social challenges, ranging from ethics, security, transparency, privacy and standards, employment, education, (in) equality and inclusiveness, legislation, governance and democracy, besides warfare and super intelligence.

These challenges can not be filed with the industry, this is a matter for governments, social partners, scientists and companies together. The EESC believes that the EU should adopt policy frameworks herein and should play a global leadership role. “We have pan-European norms and standards required for AI, as we now have for food and household appliances. We need a pan-European code of ethics to ensure that remain compatible AI systems with the principles of dignity, integrity, freedom and cultural and gender diversity, as well as basic human rights, “said Catelijne Muller,” and we have employment strategies are needed to maintain or create jobs and ensure that employees remain autonomous and take pleasure in their work. “

The issue of the impact of AI on employment is indeed central to the debate on AI in Europe, where unemployment is still high because of the crisis. Although predictions about the number of jobs over the next 20 years will be lost due to the use of AI vary from a small 5% to a disastrous 100%, resulting in a society without jobs, the rapporteur believes, based on a recent report McKinsey that it is more likely that parts or parts of jobs, and not a complete job, will be taken over by AI. In this case, it comes down to education, lifelong learning and training, to ensure that workers benefit from these developments and not be victimized.

The EESC opinion also calls for a European AI infrastructure with open source privacy-respecting and learning environments, real-life test environments and high-quality data sets for training and development of AI systems. Artificial intelligence has been mainly developed by the “big five” (Amazon, Facebook, Apple, Google and Microsoft). Although these companies are in favor of the open development of AI, and some of their AI development platforms and offer open source, full accessibility this is not guaranteed. AI infrastructure for the EU, possibly even with a European AI certification or labeling, can not only help promote the development of responsible and sustainable AI but also the EU competitive advantage.

Jordan’s Fifth National Technology Parade. At the parade, university students showcased their grasp of modern technologies with projects spanning renewable energy and water to robotics. Photo Credit: UN Women/Hamza Mazra’awi CC

The Middle East and North Africa’s youthful, fast-urbanizing population are perfectly placed to embrace technology and reap the rewards of the Fourth Industrial Revolution.

Much has been written already about the arrival of the Fourth Industrial Revolution (4IR) and the opportunity that the convergence of its new technologies offers in terms of building value into production systems and economies around the world. In one sense, the playing field could be levelled out. Localized production is being made more feasible for many small producers, setting developing communities on a path towards self-sufficiency, while falling costs could enable factories of all sizes to boost their productivity levels.

However, on the opposite side of the equation, news headlines have been dominated by predictions that human workers will be substituted by robots, leading to widespread job losses and heightened societal challenges. Additionally, doubt has been shed on the ability of regions that are less industrialized, or those with fractured economies and infrastructure, to be able to respond to these disruptions and compete effectively in the future.

For the Middle East and North Africa, it’s a critical question. Clearly, the region contains a mixture of countries in very different situations, ranging from those with active conflicts, challenged societal cohesion and decreasing incomes from natural resource reserves, to thriving, inclusive, relatively advanced economies.

However, the collision of some of the region’s characteristic megatrends with the 4IR phenomenon actually positions it to take a leading role in adopting and leveraging new technologies. Here are some examples:

A rapidly growing, young, tech-savvy population and the role of augmented reality/virtual reality (AR/VR) and wearables. More than 40% of people across the Middle East and North Africa are under the age of 25, and population growth is second only to sub-Saharan Africa. This growth is set to continue, with the total population forecast to reach 700 million by 2050. While clearly this indicates an urgent need to create jobs and build new capabilities, a new generation of millennial workers who have grown up with technology at their fingertips are arguably more likely to adapt to the needs of the new production age. For example, companies could use augmented reality to conduct “hyper-training” for employees, resulting in increased engagement, dramatically faster training times and a more capable workforce.

Urbanization, new infrastructure development and the internet of things (IoT). Around 263 million people – or 62% of the region’s population – are city-dwellers, and this urban populace is expected to double by 2040. This means more construction, and more opportunities to embed IoT devices into current and future builds, traffic management, energy management and other smart systems, to help the region take a leap forward. Cities around the world have begun to harness the power of digital connectivity. Barcelona uses smart lamp posts that sense pedestrians to adjust lighting, sample air quality and share information with city agencies and the public. Singapore uses smart bus fleets that identify issues and significantly reduce crowding and wait times. Dubai has installed new traffic signals that spot the movement of pedestrians and automatically modify the signal timing to encourage more people to walk and help reduce accidents.

Economic diversification, productivity and the role of robots. Volatile oil prices have placed strain on the Middle East’s oil-exporting countries, resulting in a redoubled focus on economic diversification. All of the Gulf Cooperation Council (GCC) countries have designed long-term plans to increase their diversification, attract investment, grow the SME sector and private-sector jobs, and increase GDP and exports. Converging new technologies offer an alternative to traditional routes to development, and make it possible for countries to enter new industry sectors with relative ease.

Examples include “speed factories” that use robots and 3D printing to rapidly produce customized goods, which can help accelerate localization of production. This automation can multiply productivity and enable countries in the region to enter new markets where they could not compete before, such as aerospace-parts manufacturing. Furthermore, the use of automation and autonomy for cargo and passenger movement could increase the region’s competitive advantage.

Of course, there are undoubtedly challenges for the region to overcome as it builds out a new future, all of which will be key in alleviating the foundational reasons of regional security conflicts. Three that are central to unleashing the transformational growth of the Fourth Industrial Revolution are:

Building the right capabilities. Some regions are behind on the path towards industrialization, and it is acknowledged that students and professionals must be better equipped with the relevant skills – not only in STEM subjects, but in the intrinsically human skills that will be in demand more than ever before as automation alters the role for humans in the workplace.

Supportive governance, regulation and policies. This comes down to governments adopting more progressive and inclusive frameworks that encourage demand, stimulate investment, boost enterprise development, reduce corruption and redress the imbalances of historical exclusion in some of its societies.

Pan-regional integration. Finally, in a world where regions are beginning to look inwards, the Middle East and North Africa can stand to gain from improving their regional economic exchanges. Economic integration in the region is among the lowest globally. Increased intra-regional mobility of goods, capital and workforce would enable it to boost economic growth and better cope with the disruptions of 4IR.

I am in no doubt that the Middle East and North Africa stand at the threshold of an enormous opportunity as the digital revolution beckons. I believe that it has the capacity to seize that opportunity and to continue its path towards a highly productive and more integrated future.

 Read the original article on the WEF website here.

“The new frontier is learning how to design the relationships between people, robots, and infrastructure,” says David Mindell, the Dibner Professor of the History of Engineering and Manufacturing, and a professor of aeronautics and astronautics. “We need new sensors, new software, new ways of architecting systems.” Photo: Len Rubenstein

Science and technology are essential tools for innovation, and to reap their full potential, we also need to articulate and solve the many aspects of today’s global issues that are rooted in the political, cultural, and economic realities of the human world. With that mission in mind, MIT’s School of Humanities, Arts, and Social Sciences has launched The Human Factor — an ongoing series of stories and interviews that highlight research on the human dimensions of global challenges. Contributors to this series also share ideas for cultivating the multidisciplinary collaborations needed to solve the major civilizational issues of our time.

David Mindell, the Frances and David Dibner Professor of the History of Engineering and Manufacturing and Professor of Aeronautics and Astronautics at MIT, researches the intersections of human behavior, technological innovation, and automation. Mindell is the author of five acclaimed books, most recently “Our Robots, Ourselves: Robotics and the Myths of Autonomy” (Viking, 2015). He is also the co-founder of Humatics Corporation, which develops technologies for human-centered automation. SHASS Communications recently asked him to share his thoughts on the relationship of robotics to human activities, and the role of multidisciplinary research in solving complex global issues.

Q: A major theme in recent political discourse has been the perceived impact of robots and automation on the United States labor economy. In your research into the relationship between human activity and robotics, what insights have you gained that inform the future of human jobs, and the direction of technological innovation?

A: In looking at how people have designed, used, and adopted robotics in extreme environments like the deep ocean, aviation, or space, my most recent work shows how robotics and automation carry with them human assumptions about how work gets done, and how technology alters those assumptions. For example, the U.S. Air Force’s Predator drones were originally envisioned as fully autonomous — able to fly without any human assistance. In the end, these drones require hundreds of people to operate.

The new success of robots will depend on how well they situate into human environments. As in chess, the strongest players are often the combinations of human and machine. I increasingly see that the three critical elements are people, robots, and infrastructure — all interdependent.

Q: In your recent book “Our Robots, Ourselves,” you describe the success of a human-centered robotics, and explain why it is the more promising research direction — rather than research that aims for total robotic autonomy. How is your perspective being received by robotic engineers and other technologists, and do you see examples of research projects that are aiming at human-centered robotics?

A: One still hears researchers describe full autonom as the only way to go; often they overlook the multitude of human intentions built into even the most autonomous systems, and the infrastructure that surrounds them. My work describes situated autonomy, where autonomous systems can be highly functional within human environments such as factories or cities. Autonomy as a means of moving through physical environments has made enormous strides in the past ten years. As a means of moving through human environments, we are only just beginning. The new frontier is learning how to design the relationships between people, robots, and infrastructure. We need new sensors, new software, new ways of architecting systems.

Q: What can the study of the history of technology teach us about the future of robotics?

A: The history of technology does not predict the future, but it does offer rich examples of how people build and interact with technology, and how it evolves over time. Some problems just keep coming up over and over again, in new forms in each generation. When the historian notices such patterns, he can begin to ask: Is there some fundamental phenomenon here? If it is fundamental, how is it likely to appear in the next generation? Might the dynamics be altered in unexpected ways by human or technical innovations?

One such pattern is how autonomous systems have been rendered less autonomous when they make their way into real world human environments. Like the Predator drone, future military robots will likely be linked to human commanders and analysts in some ways as well. Rather than eliding those links, designing them to be as robust and effective as possible is a worthy focus for researchers’ attention.

Q: MIT President L. Rafael Reif has said that the solutions to today’s challenges depend on marrying advanced technical and scientific capabilities with a deep understanding of the world’s political, cultural, and economic realities. What barriers do you see to multidisciplinary, sociotechnical collaborations, and how can we overcome them?

A: I fear that as our technical education and research continues to excel, we are building human perspectives into technologies in ways not visible to our students. All data, for example, is socially inflected, and we are building systems that learn from those data and act in the world. As a colleague from Stanford recently observed, go to Google image search and type in “Grandma” and you’ll see the social bias that can leak into data sets — the top results all appear white and middle class.

Now think of those data sets as bases of decision making for vehicles like cars or trucks, and we become aware of the social and political dimensions that we need to build into systems to serve human needs. For example, should driverless cars adjust their expectations for pedestrian behavior according to the neighborhoods they’re in?

Meanwhile, too much of the humanities has developed islands of specialized discourse that is inaccessible to outsiders. I used to be more optimistic about multidisciplinary collaborations to address these problems. Departments and schools are great for organizing undergraduate majors and graduate education, but the old two-cultures divides remain deeply embedded in the daily practices of how we do our work. I’ve long believed MIT needs a new school to address these synthetic, far-reaching questions and train students to think in entirely new ways.

Interview prepared by MIT SHASS Communications
Editorial team: Emily Hiestand (series editor), Daniel Evans Pritchard

NAO robot. Photo courtesy: Paul Bremner/UWE

I was asked to write a short op-ed on the European Parliament Law Committee’s recommendations on civil law rules for robotics. In the end, the piece didn’t get published, so I am posting it here:

It is a great shame that most reports of the European Parliament’s Committee for Legal Affairs’ vote on its Draft Report on Civil Law Rules on Robotics headlined on ‘personhood’ for robots because the report has much else to commend it. Most important among its several recommendations is a proposed code of ethical conduct for roboticists, which explicitly asks designers to research and innovate responsibly. Some may wonder why such an invitation even needs to be made but, given that engineering and computer science education rarely includes classes on ethics (it should), it is really important that robotics engineers reflect on their ethical responsibilities to society – especially given how disruptive robot technologies are. This is not new – great frameworks for responsible research and innovation already exist. One such is the 2014 Rome Declaration on RRI, and in 2015 the Foundation for Responsible Robotics was launched.

Within the report’s draft Code of Conduct is a call for robotics funding proposals to include a risk assessment. This too is a very good idea and guidance already exists in British Standard BS 8611, published in April 2016. BS 8611 sets out a comprehensive set of ethical risks and offers guidance on how to mitigate them. It is very good also to see that the Code stresses that humans, not robots, are the responsible agents; this is something we regarded as fundamental when we drafted the Principles of Robotics in 2010.

For me, transparency (or the lack of it) is an increasing worry in both robots and AI systems. Labour’s industry spokesperson Chi Onwurah is right to say, “Algorithms are part of our world, so they are subject to regulation, but because they are not transparent, it’s difficult to regulate them effectively” (and don’t forget that it is algorithms that make intelligent robots intelligent). So it is very good to see the draft Code call for robotics engineers to “guarantee transparency … and right of access to information by all stakeholders”, and then in the draft ‘Licence for Designers’: you should ensure “maximal transparency” and even more welcome “you should develop tracing tools that … facilitate accounting and explanation of robotic behaviour… for experts, operators and users”.  Within the IEEE Standards Association Global Initiative on Ethics in AI and Autonomous Systems, launched in 2016, we are working on a new standard on Transparency in Autonomous Systems.

This brings me to standards and regulation.  I am absolutely convinced that regulation, together with transparency and public engagement, builds public trust. Why is it that we trust our tech? Not just because it’s cool and convenient, but also because it’s safe (and we assume that the disgracefully maligned experts will take care of assuring that safety). One of the reasons we trust airliners is that we know they are part of a highly regulated industry with an amazing safety record. The reason commercial aircraft are so safe is not just good design, it is also the tough safety certification processes and, when things do go wrong, robust processes of air accident investigation. So the Report’s call for a European Agency for Robotics and AI to recommend standards and regulatory framework is, as far as I’m concerned, not a moment too soon. We urgently need standards for safety certification of a wide range of robots, from drones and driverless cars to robots for care and assisted living.

Like many of my robotics colleagues, I am deeply worried by the potential for robotics and AI to increase levels of economic inequality in the world. Winnie Byanyima, executive director of Oxfam writes for the WEF, “We need fundamental change to our economic model. Governments must stop hiding behind ideas of market forces and technological change. They … need to steer the direction of technological development”. I think she is right – we need a serious public conversation about technological unemployment and how we ensure that the wealth created by AI and Automonous Systems is shared by all. A Universal Basic Income may or may not be the best way to do this – but it is very encouraging to see this question raised in the draft Report.

I cannot close the piece without at least mentioning artificial personhood. My own view is that personhood is the solution to a problem that doesn’t exist. I can understand why, in the context of liability, the Report raises this question for discussion, but – as the report itself later asserts in the Code of Conduct: humans, not robots, are the responsible agents. Robots are and should remain, artefacts.

If you enjoyed this article, you may also want to read:

• Does AI pose a threat to society?
• The infrastructure of life part 1: Safety
• The infrastructure of life part 2: Transparency
• Ethically Aligned Design
• Robust and transparent governance is key to building trust in driverless cars

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

Last week I had the pleasure of debating the question “does AI pose a threat to society?” with friends and colleagues Christian List, Maja Pantic and Samantha Payne. The event was organised by the British Academy and brilliantly chaired by the Royal Society’s director of science policy Claire Craig. Here follows my opening statement:

My opening statement

One Friday afternoon in 2009 I was called by a science journalist at, I recall, the Sunday Times. He asked me if I knew that there was to be a meeting of the AAAI to discuss robot ethics. I said no I don’t know of this meeting. He then asked “are you surprised they are meeting to discuss robot ethics” and my answer was no. We talked some more and agreed it was actually a rather dull story: a case of scientists behaving responsibly. I really didn’t expect the story to appear but checked the Sunday paper anyway, and there in the science section was the headline Scientists fear revolt of killer robots. (I then spent the next couple of days on the radio explaining that no, scientists do not fear a revolt of killer robots.)

So, fears of future super intelligence – robots taking over the world – are greatly exaggerated: the threat of an out-of-control super intelligence is a fantasy – interesting for a pub conversation perhaps. It’s true we should be careful and innovate responsibly, but that’s equally true for any new area of science and technology. The benefits of robotics and AI are so significant, the potential so great, that we should be optimistic rather than fearful. Of course robots and intelligent systems must be engineered to very high standards of safety for exactly the same reasons that we need our washing machines, cars and airplanes to be safe. If robots are not safe people will not trust them. To reach it’s full potential what robotics and AI needs is a dose of good old fashioned (and rather dull) safety engineering.

In 2011 I was invited to join a British Standards Institute working group on robot ethics, which drafted a new standard BS 8611 Guide to the ethical design of robots and robotic systems, published in April 2016. I believe this to be the world’s first standard on ethical robots.

Also in 2016 the very well regarded IEEE standards association – the same organization that gave us WiFi – launched a Global initiative on Ethical Considerations in AI and Autonomous Systems. The purpose of this Initiative is to ensure every technologist is educated and empowered to prioritize ethical considerations in the design and development of autonomous and intelligent systems; in a nutshell, to ensure ethics are baked in. In December we published Ethically Aligned Design: A Vision for Prioritizing Human Well Being with AI and Autonomous Systems. Within that initiative I’m also leading a new standard on transparency in autonomous systems, based on the simple principle that it should always be possible to find out why an AI or robot made a particular decision.

We need to agree ethical principles, because they are needed to underpin standards – ways of assessing and mitigating the ethical risks of robotics and AI. But standards needs teeth and in turn underpin regulation. Why do we need regulation? Think of passenger airplanes; the reason we trust them is because it’s a highly regulated industry with an amazing safety record, and robust, transparent processes of air accident investigation when things do go wrong. Take one example of a robot that we read a lot about in the news – the Driverless Car. I think there’s a strong case for a driverless car equivalent of the CAA, with a driverless car accident investigation branch. Without this it’s hard to see how driverless car technology will win public trust.

Does AI pose a threat to society? No. But we do need to worry about the down to earth questions of present day rather unintelligent AIs; the ones that are deciding our loan applications, piloting our driverless cars or controlling our central heating. Are those AIs respecting our rights, freedoms and privacy? Are they safe? When AIs make bad decisions, can we find out why? And I worry too about the wider societal and economic impacts of AI. I worry about jobs of course, but actually I think there is a bigger question: how can we ensure that the wealth created by robotics and AI is shared by all in society?

Thank you.

The featured image of this article was used to advertise the BA’s series of events on the theme Robotics, AI and Society. The reason I draw your attention to it here is that one of the many interesting questions to the panel was about the way that AI tends to be visualised in the media. This kind of human face coalescing (or perhaps emerging) from the atomic parts of the AI seems to have become a trope for AI. Is it a helpful visualisation of the human face of AI, or does it mislead to an impression that AI has human characteristics?

Robots raise all kinds of concerns. They could steal our jobs, as some experts think. And if artificial intelligence grows, it might even be tempted to enslave us, or to annihilate the whole of humanity. Robots are strange creatures, and not only for these frequently invoked reasons. We have good cause to be a little worried about these machines.

Imagine that you are visiting the Quai Branly-Jacques Chirac, a museum in Paris dedicated to anthropology and ethnology. As you walk through the collection, your curiosity leads you to a certain piece. After a while, you begin to sense a familiar presence heading towards the same objet d’art that has caught your attention.

You move slowly, and as you turn your head a strange feeling seizes you because what you seem to distinguish, still blurry in your peripheral vision, is a not-quite-human figure. Anxiety takes over.

As your head turns, and your vision become sharper, this feeling gets stronger. You realise that this is a humanoid machine, a robot called Berenson. Named after the American art critic Bernard Berenson and designed by the roboticist Philippe Gaussier (Image and Signal processing Lab) and the anthropologist Denis Vidal (Institut de recherche sur le développement), Berenson is part of an experiment underway at the Quai Branly museum since 2012.

The strangeness of the encounter with Berenson leaves you suddenly frightened, and you step back, away from the machine.

The uncanny valley

This feeling has been explored in robotics since the 1970s, when Japanese researcher Professor Masahiro Mori proposed his “uncanny valley” theory. If a robot resembles us, he suggested, we are inclined to consider its presence in the same way as we would that of a human being.

But when the machine reveals its robot nature to us, we will feel discomfort. Enter what Mori dubbed “the uncanny valley”. The robot will then be regarded as something of a zombie.

Mori’s theory cannot be systematically verified. But the feelings we experience when we meet an autonomous machine are certainly tinged with both incomprehension and curiosity.

The experiment conducted with Berenson at the Quai Branly, for example, shows that the robot’s presence can elicit paradoxical behaviour in museum goers. It underlines the deep ambiguity that characterises the relationship one can have with a robot, particularly the many communication problems they pose for humans.

If we are wary of such machines, it is mainly because it is not clear to us whether they have intentions. And, if so, what they are and how to establish a basis for the minimal understanding that is essential in any interaction. Thus, it is common to see visitors of the Quai Branly adopting social behaviour with Berenson, such as talking to it, or facing it, to find out how it perceives its environment.

In one way or another, visitors mainly try to establish contact. It appears that there is something strategic in considering the robot, even temporarily, as a person. And these social behaviours are not only observed when humans interact with machines that resembles us: it seems we make anthropomorphic projections whenever humans and robots meet.

Social interactions

An interdisciplinary team has recently been set up to explore the many dimensions revealed during these interactions. In particular, they are looking at the moments when, in our minds, we are ready to endow robots with intentions and intelligence.

This is how the PsyPhINe project was born. Based on interactions between humans and a robotic lamp, this project seeks to better understand people’s tendency to anthropomorphise machines.

Trying to communicate with a robotic lamp, 2016

After they get accustomed to the strangeness of the situation, it is not uncommon to observe that people are socially engaging with the lamp. During a game in which people are invited to play with this robot, they can be seen reacting to its movements and sometimes speaking to it, commenting on what it is doing or on the situation itself.

Mistrust often characterises the first moments of our relations with machines. Beyond their appearance, most people don’t know exactly what robots are made of, what their functions are and what their intentions might be. The robot world seems way too far from ours.

But this feeling quickly disappears. Assuming they have not already run away from the machine, people usually seek to define and maintain a frame for communication. Typically, they rely on existing communication habits, such as those used when talking to pets, for example, or with any living being whose world is to some degree different from theirs.

Ultimately, it seems, we humans are as suspicious of our technologies as we are fascinated by the possibilities they open up.

This article was originally published on The Conversation. Read the original article.

As human-robot interactions become commonplace, MEPs stress that EU-wide rules are needed to guarantee a standard level of safety and security.© AP Images/European Union – EP

Robots are the technology of the future. But the current legal system is incapable of handling them. This generic statement is often the premise for considerations about the possibility of awarding rights (and liabilities) to these machines at some, less-than clearly identified, point in time. Discussing the adequacy of existing regulation in accommodating new technologies is certainly necessary, but the ontological approach is incorrect. Instead a functional approach needs to be adopted, identifying:

1. what rules can be applied to robots (as is);
2. what incentives do such rules provide;
3. whether those incentives are desirable.

The recent Resolution of the European Parliament (henceforth Resolution) has great political relevance and strategic importance in the development of a European Robotic Industry. Its considerations and conclusions will be taken into account in the current position paper.

Issues

The first issue when discussing regulation is that of definitions, for one cannot regulate something without firstly defining it. However, the term robot is technical and encompasses a wide range of applications that have very little in common. For this very reason, it is impossible to develop a unitary body of rules applicable to all kinds of robotic applications, rather different rules should apply to different classes of devices.

The major issue when discussing civil law rules on robotics is that of liability (for damages). Automation might, to some extent, challenge some of the existing paradigms; and increasing human-machine cooperation might cause different sets of existing rules to overlap, leading to uncertainty, thence increased litigation and difficulties in insuring new products.

Connected to the above is robot testing. A clear legal framework for robot testing outside the restricted environment of the laboratory is needed to assess the kind of dangers that might emerge with the use and their statistical frequency (also for insurance purposes). Similarly, standardization and the development of adequate, narrow-tailored technical standards for different kinds of robots is a major concern, both to ensure product safety and the adoption of possible alternatives to existing liability rules.

A possible non-issue when discussing rules for robotics is that of the attribution of personhood. This, if intended in an ontological way, is deprived of any reasonable grounding in both technical, philosophical and legal considerations. Instead, if understood in a purely functional way the attribution of legal personhood (like in the case for corporations) might be open for discussion (in some cases). Considering some more specific kinds of applications, in particular biorobotic devices and the issue of human enhancement, its regulation and management becomes of the greatest importance and quite likely the single most relevant bioethical issues of the nearby future, requiring ad-hoc regulation to be adopted.

Finally, privacy regulation, access to data and data use is of pivotal importance, not only for the development of a European Robotics industry but more broadly for a digital market. All the mentioned issues might fall under some – direct or indirect – competences of the EU and would certainly benefit from regulations adopted at a supranational (thence European) level.

Responses

The Resolution addresses all the above mentioned issues with consistent considerations, depicting an adequate framework for a technical – legal – debate about what narrow tailored sets of rules should be adopted at the EU level. Overall, it is of the greatest political and strategical importance for defining a modern legal system, favorable to the immersion of new technologies and the proliferation of new businesses.

More specifically:

Definitions: there is need for a definition of “robot” that needs to be inclusive. What needs to be avoided are nominalistic discussions which would inevitably emerge as soon as a regulation was adopted (should the notion of robot be too narrow). Debates about whether a robot requires to be autonomous or not, controlled or not, embodied or not are irrelevant from a legal point of view. Instead, such characteristics should allow to distinguish sub-classes of robots that might be regulated unitarily. Thence, next to a broader and all-encompassing definition of robot (that should include software and non-embodied AI), narrower definitions should be elaborated, pooling together those applications that show some relevant similarities and that can be regulated unitarily.

Liability: Human-machine cooperation will cause different sets of rules to overlap (namely product liability rules and traditional tort law principles). This will cause high levels of uncertainty and litigation, delaying innovation. With respect to compensation, it is, in many cases, sensible to separate the function of ensuring product safety from that of providing the victim with compensation. This might justify different alternative solutions to be adopted: liability exemptions for users and/or manufacturers; creation of automatic compensations funds (privately or publicly funded); compulsory insurance provisions. More broadly, the inadequacies of existing rules (in particular product liability rules) might suggest to radically replace a fault based rule with a risk-management approach (based on absolute liability rules) holding liable the party who is better placed to minimize the cost and acquire insurance (Resolution nn. 53, 55). A one-stop-shop approach might be sensible, preventing complex litigation to apportion liability among different players involved. Which solution is preferable depends on the class of applications considered, the market for such products and the possibility to address those risks through insurance (Resolution nn. 57-59).

Testing: a uniform set of rules allowing testing outside the laboratories and even in human environments should be adopted, defining clear standards (in particular with respect to safety, insurance and management of the experiment) thus reducing discretionary powers of local authorities (Resolution n. 23).

Standardization & European Robotics Agency: standards represent the most effective way to ensure high levels of product safety and provide certainty ex ante to manufacturers who conform to them (Resolution n. 22). However, the time required for the adoption of a new standard and its breadth is incompatible with the current pace of technological innovation. A European Robotic Agency, such as the one suggested by the Resolution (nn. 15-17), could have a strategic importance setting a supranational standard, that could be of use beyond European borders. Otherwise, other leading economies will attempt to do the same.

Electronic Personhood: set forth by the Resolution, this notion is purely functional and intends to facilitate the registration, insurance and management of some devices (in particular nonembodied AI) with a legal tool that is equivalent to that used for corporations (so called legal personhood), see Resolution n. 59, let. E) and F).

Human Enhancement: the use of robotics to overcome human limits might become problematic given the lack of a clear sets or rules and criteria that could help discern what kind of manipulations of the human body should be allowed. The constitutional principles of human dignity, equality, and freedom of self-determination, as understood today in the broader bioethical debate are per se insufficient, and narrower criteria ought to be adopted. The legal grounds to justify an intervention by the EU in this field are less evident than in all other matters mentioned, however they can be found in the freedom of movement of EU citizens, which would suggest, to some extent, a uniform framework. With respect to the content of such principles, human dignity ought to be understood as objective and external, limiting self-determination, and reversibility of the intervention onto the body should also be taken into consideration.

Privacy & Free Flow of Data: privacy cannot be granted simply through informed consent. Consent is hardly ever truly informed, and the very possibility to dissent is limited, should one want to use the service or device requiring the collection of personal data for its operation. On the one hand, the current EU Regulation setting forth the “-by design” principle, should be narrowed down through the adoption of specific standards, specifying what satisfies that criteria in different classes of applications (see Recommendation nn. 17-21). On the other hand, consumers should be compensated for allowing access and use of – private and anonymized – data through post-sale services, enriching after-sale duties imposed on the producer.

University students experiment with human-robot interaction and autonomous manipulation, two elements of manufacturing’s future. Nikolaus Correll, CC BY-ND

America’s manufacturing heyday is gone, and so are millions of jobs, lost to modernization. Despite what Treasury Secretary Steven Mnuchin might think, the National Bureau of Economic Research and Silicon Valley executives, among many others, know it’s already happening. And a new report from PwC estimates that 38 percent of American jobs are at “high risk” of being replaced by technology within the next 15 years. But how soon automation will replace workers is not the real problem. The real threat to American jobs will come if China does it first.

Since the year 2000, the U.S. has lost five million manufacturing jobs. An estimated 2.4 million jobs went to low-wage workers in China and elsewhere between 1999 and 2011. The remainder fell victim to gains in efficiency of production and automation, making many traditional manufacturing jobs obsolete.

Though more than a million jobs have returned since the 2008 recession, the net loss has devastated the lives of millions of people and their families. Some blame robotics, others globalization. It turns out that those forces work together, and have been equally hurtful to manufacturing jobs. The car industry, for example, imports more and more parts from abroad, while automating their assembly in the U.S.

As a robotics researcher and educator, I strongly advocate that the best way to get those jobs back is to build on our existing strengths, remaining a leader in manufacturing efficiency and doing the hard work to further improve our educational and social systems to cope with a changing workforce. Particularly when looking at what’s happening in China, it’s clear we need to maintain America’s international competitiveness, as we have done since the beginning of industrialization.

Chinese competition

In 2014, China exported more, and more valuable, products than the U.S. for the first time. Many of these were made by the low-wage laborers China has become famous for.

Yet China has also emerged as the largest growth market for robotics. Chinese companies bought more than twice as many industrial robots (68,000) in 2015 than American companies did (27,000). China’s Midea – an appliance manufacturer – just purchased the German robotic powerhouse Kuka.

China has understood that its competitive advantage of cheap labor will not last forever. Instead, labor costs will rise as its economy develops. Look at FoxConn, for example, the Taiwanese manufacturing contractor of the iPhone known for the high-pressure work environment at its plants in China. The company already uses more than 60,000 robots, and has said it wants to use as many as a million robots by 2020.

That’s a bold goal, especially given the current state of robotics. At present, robots are good only at highly repetitive tasks in structured environments. They are still far inferior to humans in simple tasks like picking items from a shelf. But FoxConn’s goal of transforming its streamlined manufacturing line is definitely achievable. Many of the tasks now done by humans thousands of times a day can be easily automated – such as applying a puddle of glue, placing double-sided tape, positioning a piece of plastic, tightening screws or loading products onto a pallet.

The lesson here is simple: Some occupations will simply disappear, like those of weavers in the textile industry displaced by the power loom. We need to embrace this disruption if we want to avoid being taken out of the game altogether. Imagine if China is able to replace our low-wage jobs with its workers, and then can automate those jobs: Work Americans now do will be done here, or anywhere – but not by humans. FoxConn is planning its first plant in the U.S.; soon, Chinese robots will be working in America.

Seeing opportunity, not loss

The good news is that while many types of jobs will cease to exist, robots will create other jobs – and not only in the industry of designing new robots.

This is already beginning to happen. In 2014, there were more than 350,000 manufacturing companies with only one employee, up 17 percent from 2004. These companies combine globalization and automation, embracing outsourcing and technological tools to make craft foods, artisanal goods and even high-tech engineered products.

Many American entrepreneurs use digitally equipped manufacturing equipment like 3-D printers, laser cutters and computer-controlled CNC mills, combined with market places to outsource small manufacturing jobs like mfg.com to run small businesses. I’m one of them, manufacturing custom robotic grippers from my basement. Automation enables these sole proprietors to create and innovate in small batches, without large costs.

Returning to manufacturing dominance

This sort of solo entrepreneurship is just getting going. Were robots more available and cheaper, people would make jewelry and leather goods at home, and even create custom-made items like clothing or sneakers, directly competing with mass-produced items from China. As with the iPhone, even seemingly complex manufacturing tasks can be automated significantly; it’s not even necessary to incorporate artificial intelligence into the process.

Three trends are emerging that, with industry buy-in and careful government support, could help revitalize the U.S. manufacturing sector.

First, robots are getting cheaper. Today’s US$100,000 industrial robotic arms are not what the future needs. Automating iPhone assembly lines will require cheap robotic arms, simple conveyor belts, 3-D-printed fixtures and software to manage the entire process. As we saw in the 3-D printing industry, the maker movement is setting the pace, creating low-cost fabrication robots. The government is involved, too: The Pentagon’s research arm, DARPA, has backed the OtherMill, a low-cost computer-controlled mill.

In addition, more people are programming robots. Getting a robot to accomplish repetitive tasks in industry – for example, using Universal Robot’s interface – is as simple as programming LEGO Mindstorms. Many people think it’s much harder than that, confusing robotic automation with artificial intelligence systems playing chess or Go. In fact, building and programming robots is very similar both physically and intellectually to doing your own plumbing, electrical wiring and car maintenance, which many Americans enjoy and are capable of learning. “Maker spaces” for learning and practicing these skills and using the necessary equipment are sprouting across the country. It is these spaces that might develop the skill sets that enable Americans to take automation into their own hands at their workplaces.

Lastly, cutting-edge research is improving the hardware needed to grasp and manipulate manufacturing components, and the software to sense and plan movements for assembling complex items. Industrial robot technology is upgradeable and new robots are designed to complement human workers, allowing industry to make gradual changes, rather than complete factory retooling.

A path forward

To fully take advantage of these trends and other developments, we need to improve connections between researchers and businesses. Government effort, in the form of the Defense Department’s new Advanced Robotics Manufacturing Institute, is already working toward this goal. Funded by US$80 million in federal dollars, the institute has drawn an additional $173 million in cash, personnel, equipment and facilities from the academic and private sectors, aiming to create half a million manufacturing jobs in the next 10 years.

Those numbers might sound high, but China is way ahead: Just two provinces, Guangdong and Zhejiang, plan to spend a combined $270 billion over the next five years to equip factories with industrial robots.

The stakes are high: If the U.S. government ignores or avoids globalization and automation, it will stifle innovation. Americans can figure out how to strengthen society while integrating robotics into the workforce, or we can leave the job to China. Should it come to that, Chinese companies will be able to export their highly efficient manufacturing and logistics operations back to the U.S., putting America’s manufacturing workforce out of business forever.

This article was originally published on The Conversation. Read the original article here: http://theconversation.com/to-really-help-us-workers-we-should-invest-in-robots-71125.

Robotics technology is no longer limited to industry. Climate Controls, 3D printers, surveillance robots, drones, household and even sex robots are entering the private market. The more autonomous they become, the more difficult it becomes to resolve conflicts, such as those between humans and software.

The law currently recognizes individuals, like you and me. Also companies, organizations and governments can negotiate agreements and liability. These non-natural persons are represented by real people (they should be controlled after all). But what about autonomous systems that take over tasks and make intelligent decisions that might be interpreted as a legal act?

Social robots will not just vacuum your house, they might pay bills, independently enter into contracts and, who knows, get in the car to get groceries for you. Civil law as it is written, however, sees the robot as an object, not as a subject with legal capacity. One issue is that reminiscent of Bicentennial Man, a science fiction film from 1999 based on a story by Isaac Asimov in 1977. The robot Andrew Martin wants to be recognized as a ‘natural person’—a request that the court rejects with the argument that a robot lives eternally. Years later, the robot asks for a review. An update enables him to die. Moreover, according to Andrew, the judge himself makes use of non-natural resources.

Industrial robots do not yet have legal capacity. They carry out instructions in a defined process. The need for legal personality arises only when participating in society. A social robot must make arrangements, for example, in his care function with physicians and suppliers. That cannot be without any acceptance of legal personality. But at what level will artificial intelligence be “equivalent” to that of natural persons?

The Turing test may have seemed definitive in 1950, but does it still count? According to the test, a machine must qualify for intelligence on a “human” level. The interrogator, for example, in a chat, has to get the impression that she is talking to a real person. The robot statements should be appreciated emotionally and morally and react to it appropriately. So, it has to have social intelligence. It also should respond appropriately to changing circumstances to be qualified as a dynamic action-intelligence.

Could you imagine a robot who is your colleague or boss? The Swedish series “Real Humans” has already dramatized the scenario. Can you dismiss individuals once their job is better done by a robot? Can we accept that a robot has control over an individual? Labour law does not give an answer. Topical is a situation in which a self-driving car crashes. According to road traffic laws, the driver is responsible. But what if the controller is dependent on road management, vehicle manufacturer, meteorological service, navigation systems and the algorithm that made the car self-learning?

Robot sustainable law is therefore particularly complicated in the area of liability. And what if a robot is guilty, does it make sense to punish him? Can we pull the plug, as in the movie I, Robot? At the same time, there has to be expected less conflict. Ninety percent of traffic accidents are caused by human error, the rest being due to circumstances such as a falling tree or flat tire.

And what is a natural person anyway? Is it a man or woman who was not born of another human being, but composed of an artificial heart, artificial kidney, artificial limbs, artificial brains, etc., and brought to life in a laboratory? What about discrimination? What if a human body has been upgraded with robotics, can we just switch them off?

Back to the distinction between a legal object and a subject of law. Add to that sui generis, a legal phenomenon that is the only one of its kind. Asimov already offered three laws:

• A robot may not cause a human injury or by omission allow overtaking any human injuries.
• A robot must obey the orders given it by human beings, except where such contracts are in conflict with the first law.
• A robot must protect its own existence, as far as such protection does not conflict with the First or Second Law.

Good thoughts, but motivated by fear and the desire for human control. Adherence to these laws might actually put the brakes on the development of artificial intelligence. But what we want is progress, right?

It’s time to create a commission for this issue—an international multidisciplinary committee consisting of lawyers, philosophers, ethicists, computer scientists, political scientists and economists. Otherwise, the robots might in the long run provide themselves with a solution.

More adapted to the societal need are the proposed laws of Murphy and Woods:

• A human may not deploy a robot without the human-robot work system meeting the highest legal and professional standards of safety and ethics.
• A robot must respond to humans as appropriate for their roles.
• A robot must be endowed with sufficient situated autonomy to protect its own existence as long as such protection provides smooth transfer of control which does not conflict with the First and Second Laws.

But there is a precondition that: A robot must have an added value in society by performing its task. I would add from a legal perspective:

• An autonomous intelligent robot must be accepted as an equal partner in performing legal acts.

Legally acting robots must be certified as such based on preconditions as: >turingtest level with a socially acceptable dynamic intelligence and societal and legal understanding of moral and legal norms.

If you liked this article, you may also want to read:

• RoboLaw: Regulating robotics (video)
• Quest for Roboethics: An interview with Prof. Rafael Capurro
• Technical challenges in machine ethics
• How do we regulate robo-morality?

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

Credit: Ex Machina (2015).

Seventy-five years ago, the celebrated science fiction writer Isaac Asimov published a short story called Runaround. Set on Mercury, it features a sophisticated robot nicknamed Speedy that has been ordered to gather some of the chemical selenium for two human space adventurers.

Speedy gets near the selenium, but a toxic gas threatens to destroy the robot. When it retreats from the gas to save itself, the threat recedes and it feels obliged to go back for the selenium. It is left going round in circles.

Speedy is caught in a conflict between two of the laws that robots in Asimov’s stories follow as their core ethical programming: always obey human instructions and always protect your existence (as long as it doesn’t result in human injury).

Speedy’s custodians resolve the robot’s conflict by exploiting a third, overriding law: never let a human being come to any harm. They place themselves in harm’s way and tell Speedy if he does not get the selenium they will die. This information straightens out Speedy’s priorities, and the adventurers live to interfere with other planets.

Today, Asimov’s three laws of robotics are much better known than Runaround, the story in which they first appeared. The laws seem a natural response to the idea that robots will one day be commonplace and need internal programming to prevent them from hurting people.

Yet, while Asimov’s laws are organised around the moral value of preventing harm to humans, they are not easy to interpret. We need to stop viewing them as an adequate ethical basis for robotic interactions with people.

Part of the reason Asimov’s laws seem plausible is that fears about the harm robots could do to humans are not groundless. There have already been fatalities in the US due to malfunctioning autonomous cars.

Again, the capacities of artificial intelligence to adjust its routines to the things and people they interact with, makes some of its behaviour unpredictable. For example, machines that are capable of interacting with each other in a co-ordinated way can produce “swarm” behaviour. Swarm behaviour can be unpredictable because it can depend on adapting to random events.

So Asimov was right to worry about unexpected robot behaviour. But when we look more closely at how robots actually work and the tasks they are designed for, we find that Asimov’s laws fail to apply clearly. Take military drones. These are robots directed by humans to kill other humans. The whole idea of military drones seems to violate Asimov’s first law, which prohibits robot injury to humans.

But if a robot is being directed by a human controller to save the lives of its co-citzens by killing other attacking humans, it is both following and not following the first law. Nor is it clear if the drone is responsible when someone is killed in these circumstances. Perhaps the human controller of the drone is responsible. But a human cannot break Asimov’s laws, which are exclusively directed at robots.

Meanwhile, it may be that armies equipped with drones will vastly reduce the amount of human life lost overall. Not only is it better to use robots rather than humans as cannon fodder, but there is arguably nothing wrong with destroying robots in war, since they have no lives to lose and no personality or personal plans to sacrifice.

We need the freedom to harm ourselves

At the other end of the scale you have robots designed to provide social care to humans. At their most sophisticated, they act as companions, moving alongside their users as they fetch and carry, issue reminders about appointments and medication, and send out alarms if certain kinds of emergencies occur. Asimov’s laws are an appropriate ethics if keeping an elderly person safe is the robot’s main goal.

But often robotics fits into a range of “assistive” technology that helps elderly adults to be independent. Here the goal is to enable the elderly to prolong the period during which they can act on their own choices and lead their own lives, like any other competent adult. If this is the overriding purpose of a care robot, then, within limits, it needs to accommodate risk-taking, including its owner putting itself in harm’s way.

For example, falling is an everyday hazard of life after the age of 65. An elderly person can rationally judge that living with the after-effects of a fall is better than a regime in which they people are heavily monitored and insulated from all danger. But a robot that deferred to this judgement by not intefering when its user wants to take a steep walk would be violating the law that saved Asimov’s adventurers on Mercury.

So long as only minor injury results from a competent adult decision, it should be respected by everyone, robots and family alike, even when it is taken by an elderly person. Respect in these circumstances means not preventing the adult decision-maker from acting or informing others about their actions.

As robots become a bigger part of our society, we will undoubtedly need rules to govern how they operate. But Asimov’s laws either fail to recognise that robots get caught between human acts and cannot always avoid human injury. And that sometimes allowing humans to injure themselves is a way of respecting human autonomy.

If you take humans out of the driving seat, could traffic jams, accidents and high fuel bills become a thing of the past? As cars become more automated and connected, attention is turning to how to best choreograph the interaction between the tens or hundreds of automated vehicles that will one day share the same segment of Europe’s road network.

It is one of the most keenly studied fields in transport – how to make sure that automated cars get to their destinations safely and efficiently. But the prospect of having a multitude of vehicles taking decisions while interacting on Europe’s roads is leading researchers to design new traffic management systems suitable for an era of connected transport.

The idea is to ensure that traffic flows as smoothly and efficiently as possible, potentially avoiding the jams and delays caused by human behaviour.

‘Travelling distances and time gaps between vehicles are crucial,’ said Professor Markos Papageorgiou, head of the Dynamic Systems & Simulation Laboratory at the Technical University of Crete, Greece. ‘It is also important to consider things such as how vehicles decide which lane to drive in.’

Prof. Papageorgiou’s TRAMAN21 project, funded by the EU’s European Research Council, is studying ways to manage the behaviour of individual vehicles, as well as highway control systems.

For example, the researchers have been looking at how adaptive cruise control (ACC) could improve traffic flows. ACC is a ‘smart’ system that speeds up and slows down a car as necessary to keep up with the one in front. Highway control systems using ACC to adjust time gaps between cars could help to reduce congestion.

‘It may be possible to have a traffic control system that looks at the traffic situation and recommends or even orders ACC cars to adopt a shorter time gap from the car in front,’ Prof. Papageorgiou said.

‘So during a peak period, or if you are near a bottleneck, the system could work out a gap that helps you avoid the congestion and gives higher flow and higher capacity at the time and place where this is needed.’

Variable speed limits

TRAMAN21, which runs to 2018, has been running tests on a highway near Melbourne, Australia, and is currently using variable speed limits to actively intervene in traffic to improve flows.

An active traffic management system of this kind could even help when only relatively few vehicles on the highway have sophisticated automation. But he believes that self-driving vehicle systems must be robust enough to be able to communicate with each other even when there are no overall traffic control systems.

‘Schools of fish and flocks of birds do not have central controls, and the individuals base their movement on the information from their own senses and the behaviour of their neighbours,’ Prof. Papageorgiou said.

‘In theory this could also work in traffic flow, but there is a lot of work to be done if this is to be perfected. Nature has had a long head-start.’

One way of managing traffic flow is platooning – a way to schedule trucks to meet up and drive in convoy on the highway. Magnus Adolfson from Swedish truckmaker Scania AB, who coordinated the EU-funded COMPANION project, says that platooning – which has already been demonstrated on Europe’s roads – can also reduce fuel costs and accidents.

The three-year project tested different combinations of distances between trucks, speeds and unexpected disruptions or stoppages.

Fuel savings

In tests with three-vehicle platoons, researchers achieved fuel savings of 5 %. And by keeping radio contact with each other, the trucks can also reduce the risk of accidents.

‘About 90 percent of road accidents are caused by driver error, and this system, particularly by taking speed out of the driver’s control, can make it safer than driving with an actual driver,’ Adolfson said.

The COMPANION project also showed the benefits of close communication between vehicles to reduce the likelihood of braking too hard and causing traffic jams further back.

‘There is enough evidence to show that using such a system can have a noticeable impact, so it would be good to get it into production as soon as possible,’ Adolfson said. The researchers have extended their collaboration to working with the Swedish authorities on possible implementation.

Rutger Beekelaar, a project manager at Dutch-based research organisation TNO, says that researchers need to demonstrate how automated cars can work safely together in order to increase their popularity.

‘Collaboration is essential to ensure vehicles can work together,’ he said. ‘We believe that in the near future, there will be more and more automation in traffic, in cars and trucks. But automated driving is not widely accepted yet.’

To tackle this, Beekelaar led a group of researchers in the EU-funded i-GAME project, which developed technology that uses wireless communication that contributes to managing and controlling automated vehicles.

They demonstrated these systems in highway conditions in the 2016 Grand Cooperative Driving Challenge in Helmond, in the Netherlands, which put groups of real vehicles through their paces to demonstrate cooperation, how they safely negotiated an intersection crossing, and merged with another column of traffic.

Beekelaar says that their technology is now being used in other European research projects, but that researchers, auto manufacturers, policymakers, and road authorities still need to work together to develop protocols, systems and standardisation, along with extra efforts to address cyber security, ethics and particularly the issue of public acceptance.

Whether an A.I. ought to be granted patent rights is a timely question given the increasing proliferation of A.I. in the workplace. Examples: Daimler-Benz has tested self-driving trucks on public roads^[1], A.I. technology has been applied effectively in medical advancements, psycholinguistics, tourism and food preparation,^[2] a film written by an A.I. recently debuted online^[3] and A.I. has even found its way into the legal profession,^[4] and current interest in the question of whether an A.I. can enjoy copyright rights with several articles having already being published on the subject of A.I. and copyright rights.^[5]

In 2014 the U.S. Copyright Office updated its Compendium of U.S. Copyright Office Practices with, inter alia, a declaration that  the Office will not register works produced by a machine or mere mechanical process that operates randomly or automatically without any creative input or intervention from a human author.”^[6]

To grant or not to grant: A human prerequisite?

One might argue that Intellectual Property (IP) laws and IP Rights were designed to exclusively benefit human creators and inventors^[7] and thus would exclude non-humans from holding IP rights. The U.S. Copyright Office’s December 2014 update to the Compendium of U.S. Copyright Office Practices that added requirements for human authorship^[8] certainly adds weight to this view.

However, many IP laws were drafted well before the emergence of A.I. and in any case, do not explicitly require that a creator or inventor be ‘human.’ The World Intellectual Property Organization’s (WIPOs) definition of Intellectual Property talks about creations of the mind^[9] but does not specify whether it must be a human mind. Similarly, provisions in laws promoting innovation and IP rights, such as the so-called Intellectual Property Clause of the U.S. Constitution^[10], also do not explicitly mention a ‘human’ requirement. Finally, it ought to be noted that while the U.S. Copyright Office declared it would not register works produced by a machine or mere mechanical process without human creative input, it did not explicitly state that an A.I. could not have copyright rights.^[11]

Legal personhood

One might also argue that an A.I. is not human, and is therefore not a legal person and, thus, is not entitled to apply for much less be granted a patent. New Zealand’s Patents Act, for example, refers to a patent ‘applicant’ as a ‘person’.^[12]

Yet this line of argument could be countered by an assertion that a legal ‘person’ need not be ‘human’ as is the case of a corporation and there are many examples of patents assigned to corporations.^[13]

The underlying science

To answer the question of patent rights for an A.I. we need to examine how modern A.I. systems work and, as an example, consider how machine translation applications such as Google Translate function.

While such systems are marketed as if they’re “magic brains that just understand language”,^[14]the problem is that there is currently no definitive scientific description for language^[15] or language processing^[16]. Thus, such language translation systems cannot function by mimicking the processes of the brain.

Rather, they employ a scheme known as Statistical Machine Translation (SMT) whereby online systems search the Internet identifying documents that have already been translated by human translators– for example books, and organizations like the United Nations, or websites. The system scans these texts for statistically significant patterns and once the computer finds a pattern it uses the pattern to translate similar text in the future.^[17] This, as Jaron Lanier and others note, means that the people who created the translations and make translation systems possible are not paid to for their contributions^[18].

Many modern A.I. systems are largely big data models that operate by defining a real world problem that needs to be solved, then conceiving a conceptual model to solve this problem which is typically a statistical analysis that falls into one of three categories: regression, classification or missing data. Data is then fed into the model and used to refine and calibrate the model. As the model is increasingly refined it is used to guide the location of data and, after a number of rounds of refinement finally results in a model capable of some predictive functionality.^[19]

Big data models can be used to discover patterns in large data sets^[20] but also can, as in the case of translation systems, exploit statistically significant correlations in data.

None of this, however, suggests that current A.I. systems are capable of inventive or creative capacity.

Patentability?

So to get a patent, an invention must:

• Be novel in that it does not form part of the prior art^[21]
• Have an inventive step in that it not obvious to a person skilled in the art^[22]
• Be useful^[23]
• it must not fall into an excluded category that can include discoveries, presentations of information and mental processes or rules or methods for performing a mental act.^[24]

Why discoveries are not inventions is tied with the issue of obviousness and as noted by Buckley J. in Reynolds v. Herbert Smith & Co., Ltd^[25] who stated:

“Discovery adds to the amount of human knowledge, but it does so only by lifting the veil and disclosing something which before had been unseen or dimly seen. Invention also adds to human knowledge, but not merely by disclosing something. Invention necessarily involves also the suggestion of an act to be done, and it must be an act which results in a new product, or a new result, or a new process, or a new combination for producing an old product or an old result.”

Therefore in order to get a patent, an A.I. must first be capable of producing a patentable invention but, given current technology, is this even possible?

A thought exercise

Consider the following:

You believe that as a person exercises more, he/she consumes more oxygen and have tasked your A.I. with analyzing the relationship between oxygen consumption and exercise.

You provide the A.I. with a model suggesting that oxygen consumption increases with physical exertion and data that shows oxygen consumption among people performing little, moderate (e.g. walking briskly) and heavy exercise (e.g. running).

The A.I. reviews the data, refines the model, collects more data and comes up with a predictive model (e.g. when a person exercises X amount, he/she consumes Y amount of oxygen and when the person doubles his/her exertion, his oxygen consumption rate triples).

As this is essentially a statistical regression, the model will not always completely accurate in its predictions due to differences between individuals (i.e. for some persons the model will predict oxygen consumption fairly accurately, for others its results will be far off).

However, this particular model has another, more fundamental limitation – it fails to consider that a human cannot exercise beyond a certain point because his/her heart would be incapable of sustaining such levels of exertion^[26] or because over-exercise may trigger an unexpected reaction (e.g. death).^[27]

If one were to feed this model data of persons who have collapsed or died during exercise (and thus, in the latter case, not consume any oxygen), would the A.I. be able to ‘think outside its box’ and:

• Question the cause of these data discrepancies and have the initiative to conduct further investigation?
• Note and correct the limitation in the original model (which would require a significant amendment)?

Or would it simply alter the existing model by changing the slope of the regression line?

SMT and other A.I. have similar limitations, in the case of SMT, once the system is built, linguistic knowledge becomes necessary to achieve perfect translation at all grammatical levels^[28] and SMT systems presently cannot translate cultural components of the source text into the target language, provide very literal, word for word translations that do not recognize idioms, slang, and terms that are not in the machine’s memory and lack human creativity^[29] To do so would require a change to the underlying machine translation model, and the question arises whether this would have to be done by the human creators of the SMT or whether the SMT itself would be able to make the necessary corrections and adjustments to the model.

Should the SMT or, in the earlier example the A.I., be unable to improve and, in this case, innovate on the existing model does it have the creative or inventive capacity to conceive an invention is truly inventive? And if either the SMT or the AI can produce something that appears novel and inventive, given the nature of how A.I. presently operates (i.e. as big data models), would such a product be the result of an analysis of existing data to uncover hitherto unseen relationships – in other words, a discovery?

Returning to the original question about patent rights for an A.I., perhaps the question we should ask is not whether an A.I. should be able to get a patent, but whether an A.I., given current technology, can create a patentable invention in the first place and if the answer to that question is ‘no’, then the question of granting patent rights to an A.I. is moot.

References:

[1]  Jon Fingas, ‘Daimler tests a self-driving, mass-produced truck on real roads’, Engadget, Oct. 4, 2016.

[2] Sophie Curtis, ‘Cognitive Cooking: How Is A.I. Changing Foodtech?’ RE•WORK, April 19 2016.

[3] Analee Newitz, ‘Movie written by algorithm turns out to be hilarious and intense’ ArsTechnica, June 9, 2016.

[4]  Susan Beck, ‘AI Pioneer ROSS Intelligence Lands Its First Big Law Clients, The American Lawyer, May 6, 2016.

[5] See for example:

• Madeleine de Cock Buning ‘Is the EU exposed on the copyright of robot creations?‘ www.roboticslawjournal.com,Nov. 2015.
• Daniela Hernandez, ‘Could you take a robot to court if it steals your work? Fusion, Nov. 11, 2015.

[6] Copyright Office, Compendium of U.S. Copyright Office Practices (3d ed. 2014). § 313.2

[7] Hettinger argued that ‘the most powerful intuition supporting property rights is that people are entitled to the fruits of their labor’

• See: Edwin Hettinger, “Justifying Intellectual Property’, Philosophy & Public Affairs, Vol. 18, No. 1, Winder 1989, p. 31-52

[8] Copyright Office, Compendium of U.S. Copyright Office Practices (3d ed. 2014). § 306.

[9] According to WIPO, ‘Intellectual property (IP) refers to creations of the mind, such as inventions; literary and artistic works; designs; and symbols, names and images used in commerce.’

[10] Article I, Section 8, Clause 8 of the United States Constitution states: The Congress shall have Power To…. promote the Progress of Science and useful Arts, by securing for limited Times to Authors and Inventors the exclusive Right to their respective Writings and Discoveries.

[11] According to the U.S. Copyright Office, ‘Copyright exists from the moment the work is created. You will have to register, however, if you wish to bring a lawsuit for infringement of a U.S. work’

See ‘Copyright in General’,

[12] s.5(1) Patents Act 2013

[13] See for example, U.S. Patent 5,953,441 ‘Fingerprint sensor having spoof reduction features and related methods’ which was assigned to Harris Corporation.

[14] Nilagia McCoy  ‘Jaron Lanier: The Digital Economy Since Who Owns The Future?’ October 8, 2015.

[15] This was noted by Jaron Lanier who delivered the keynote address at the opening of the Conference on the Global Digital Content Market taking place from April 20-22, 2016 at WIPO Headquarters in Geneva, Switzerland.

• Catherine Saez, ‘IP Should Serve More Actors In New Ways, Keynote Speaker At WIPO Says’, Intellectual Property Watch, April 21, 2016.

[16] See for example:

• Erin Harte, ‘Language Processing In The Human Brain’, Brainworld, July 22, 2011.
• AD Friederici, The brain basis of language processing: from structure to function, Physiol Rev. 2011 Oct;91(4):1357-92.
• Jerry Ball, Mary Freiman, Stuart Rodgers & Christopher Myers, ‘Toward a Functional Model of Human Language Processing.’

[17] Inside Google Translate, July 9, 2010.

[18] Catherine Jewell, ‘Digital pioneer, Jaron Lanier, on the dangers of “free” online culture,’ WIPO Magazine, April 2016.

[19] Noah Silverman.

[20] i.e. ‘data mining’.

[21] s 6, New Zealand Patents Act 2013

[22] s 7, New Zealand Patents Act 2013

[23] s 10, New Zealand Patents Act 2013

[24] See for example:. 93(2) Hong Kong Patents Ordinance, Cap 514 or the Canadian Patent Act R.S.C., 1985, c. P-4

[25] (1903), 20 R.P.C. 123

[26] Wikipedia, ‘Heart Rate’.

[27] This may be caused by a congenital condition but some also believe that prolonged excessive exercise may exacerbate matters. See:

• Lou Schuler, ‘Death by Exercise’ Men’s Health June 8, 2003.
• Timothy D Noakes, ‘Sudden Death and Exercise’.

[28] Mireia Farrus, Marta R. Costa-Jussa, Jose B. Marino, Marc Poch, Adolfo Hernandez, Carlos Henrıque,  Jose A. R. Fonollosa, ‘Overcoming statistical machine translation limitations: error analysis and proposed solutions for the Catalan–Spanish language pair’ Language Resources & Evaluation, DOI 10.1007/s10579-011-9137-0.

[29] ‘Machine Translation vs. Human Translators’ 

See also:

• Wikipedia, ‘Statistical Machine Translation’.

Artificial intelligence (AI) already plays a major role in human economies and societies, and it will play an even bigger role in the coming years. To ponder the future of AI is thus to acknowledge that the future is AI.

This will be partly owing to advances in “deep learning,” which uses multi-layer neural networks that were first theorized in the 1980s. With today’s greater computing power and storage, deep learning is now a practical possibility, and a deep-learning application gained worldwide attention in 2016 by beating the world champion in Go. Commercial enterprises and governments alike hope to adapt the technology to find useful patterns in “Big Data” of all kinds.

In 2011, IBM’s Watson marked another AI watershed, by beating two previous champions in Jeopardy!, a game that combines general knowledge with lateral thinking. And yet another significant development is the emerging “Internet of Things,” which will continue to grow as more gadgets, home appliances, wearable devices, and publicly-sited sensors become connected and begin to broadcast messages around the clock. Big Brother won’t be watching you, but a trillion little brothers might be.

Image: CB Insights

Robotics undoubtedly has the potential to improve lives in the developing world. However, with limited budgets and expertise on the ground, putting this technology in place is no small task. Step forwards WeRobotics, a new Swiss/American NGO dedicated to meeting this goal through the creation of in-country ‘flying labs’. Co-founder Adam Klaptocz explains all.

Hey Adam. Let’s start with this: what is WeRobotics?

WeRobotics is a non-governmental organisation (NGO) dedicated to bringing developing countries access to robotics technology. Such countries don’t have access because of economics, supply chains and many other reasons. Our role is to help local people learn and use this technology, and then to deploy it for social good projects within their own communities.

We do this by establishing ‘flying labs’ in different countries. The goal of each is to serve as a hub of robotics technology, where staff host training sessions, webinars and teach people how to use technology. Each lab will have drones and software, on hand and in the cloud, thanks to our technology company partners. Labs will also function as an incubator for new, local, drone-based businesses.

WeRobotics co-founder Adam Klaptocz (right) and Nepal Flying Labs Coordinator Uttam Pudasaini, running a drone mapping training session in Kathmandu, Nepal.

And who is behind WeRobotics?

We have four co-founders; myself and Sonja Betschart are based in Switzerland, and Patrick Meier and Andrew Schroeder, who are based in the U.S. We are a 501c3 non-profit organisation in the United States and have just founded a sister organisation in Switzerland too. Besides that, all of our flying labs will be legal entities as well, to go along with the concept of building local capacities and driving local engagement.

The goal is to engage local lab coordinators, local organisations, and ideally make the local flying lab its own organisation that has its own activities and is sustainable

The goal is to engage local lab coordinators, local organisations, and ideally make the local flying lab its own organisation that has its own activities and is sustainable
The goal is to engage local lab coordinators, local organisations, and ideally make the local flying lab its own organisation that has its own activities and is sustainable

And you’re talking about unmanned robots in their most general sense, not just as a synonym for flying drones?

That’s right. At WeRobotics, we’re looking at robotics in the largest sense—it’s not just eBee drones for mapping, it’s also things like drones for delivery, marine robots, underwater robots, and terrestrial robots. Anything to do with robotics and the technical ecosystem that goes around it. That includes the software and the platforms people use to share a drone’s data because, in the end, drones are just a tool for collecting and turning the data into actionable information.

We have tech partners, such as senseFly and other drone and software companies, with whom we are spreading this technology throughout our network of labs. The labs will then run training programmes, serve as incubators for locals who want to get into the business, and host conferences where these adopters can share their experiences.

So do you already have your first lab set-up? Where is that?

Absolutely, we’re a young NGO, but we’re trying to expand quite quickly.

Our first lab was officially launched recently in Nepal. It’s called Nepal Flying Labs. We have a local lab coordinator there and we’ve partnered with Kathmandu University, ICIMOD, which is a mountain and glacier study organisation, NAXA and Medair. We’re working with government agencies as well, including the survey department of the Nepalese government. So we have a certain number of partners that we’re working with to roll out projects and run trainings.

We have projects that will be running throughout the next year in Kathmandu and the surrounding areas; a lot of which is post-earthquake reconstruction work after last year’s earthquake. Also, a lot of climate-based research work.

That must be ground zero, that region, for such scientific study?

Exactly. Nepal is basically a giant mountain so the effects of climate change are really affecting communities there. There are a lot of landslides – the Himalayas are melting, the glaciers are melting – so there is a lot of work to be done in terms of disaster prevention and also in post-disaster reconstruction.

And in Tanzania, where we are setting up more flying labs. I know there’s already been a lot of work there by senseFly, Drone Adventures [case study], The World Bank and other organisations. We’re hoping to work with these organisations and others to consolidate a lot of the work that’s been done; to make it sustainable for years to come within Tanzania.

Our third major lab that we are setting up before the end of the year is in Peru. In Peru, we will start in the Amazon. Our first pilot project there is about drone delivery, but we’ll also be spinning out drone mapping as a business and other types of robotics as well.

In the countries where you operate, is one of the groups you’re engaging with the government itself? Do you see your role to also help governments regulate the use of drones?

The mandate of each flying lab, as an NGO, isn’t to replace government, it’s to help.

So the first thing to do is to work with the government and try to help boost, for example, its capacity for mapping. That’s something that we believe in strongly. We’re already working with the survey department in Kathmandu and we’ll be working with the Lands Department in Tanzania as well to try to reinforce their capacity to serve their own people. On top of that, of course, we want to be able to work within countries – within the rules of those countries – and so we’re trying to help with regulations, by working with the regulatory authorities in all of these countries.

A lot of developing countries are looking for guidance from other countries – where robotic technology might be a little more established – to see what is happening. That’s another thing that we hope, as an international NGO with locations all around the world, we’ll be able to facilitate—helping governments to learn what works in other countries, such as those our network covers, and helping them to work together, often with the civil aviation authority and other government agencies. It’s about implementing rules that make drone use responsible, but also leave the market open so that we can incubate businesses. Like that, the economy can develop around this technology instead of being blocked.

You mentioned before we started recording that the key, underpinning what WeRobotics is about, is building capacity on the ground.

Yes, that’s the key, local capacity. It shouldn’t have to be that a bunch of foreigners come in to do this work whichever way they want, often with their own desires, with their own culture and their own needs. Basically, making themselves feel good about helping instead of actually helping.

What would be best, if this ever happens again in Nepal, is that there are enough drone companies locally that know what they’re doing, and understand the legal framework within the country, so that they can do the job instead of having to bring in foreigners. And that goes for every country. I think that, in development, the more local capacity you build, the more you can really bring the country out of poverty so that it can have its economy self-sustain itself.

That’s our goal with the flying labs: to build self-sustainable local entities that build local capacity but have global contact with other labs and with our global technology partners.

What specific UAV platforms do you operate at present and how are these being used?

I truly believe that every job has a tool that’s best adapted to it. In Nepal, right now, our flying lab has aneBee and a Parrot Bebop drone. The eBee is used because Nepal is obviously very mountainous and having the capacity to deal with 3D flight mapping is very important.

Meanwhile, in Tanzania there are larger, mainly flat zones to be mapped. For that we have a Sky-Watch Cumulus drone. This is a longer-range drone that can fly for two hours and cover very large areas. So there that’s the best adapted drone. We’re also bringing a Bebop to Tanzania, because there is a big interest in drone journalism there and for that it’s important to have something that is small, cheap and easily deployed.

Then in Peru, we’re working with hybrid drones that are made more for cargo delivery. One option isQuantum Systems, which is a German company that makes even bigger, VTOL drones with an even bigger payload capacity.

So, different drones for different jobs.

100% platform agnostic.

Yes. One of our goals as an NGO is to remain more or less independent of technology. What’s important is that the job can be done with the best fit technology, in an economically viable way. We need UAVs that are easy to use, easy to train on, and relatively mature as well.

One of our goals as an NGO is to remain more or less independent of technology. What’s important is that the job can be done with the best fit technology, in an economically viable way.

We do work with some startups, but in general what you really need, if you want to make something work in the jungles of Papua New Guinea or in the middle of the desert somewhere, is a machine that’s durable and that has existed for a little while. A platform that has some sort of customer support is needed too, so it’s important that our technology company partners are happy to be involved as well.

If you imagine a few years down the road, having achieved your short and medium term goals, how does the world of WeRobotics look then?

We see ourselves, a few years down the road, having many flying labs around the world. We’re not sure if we’ll have five, or ten, or if there’ll be 50, but the goal is that we have setup a network of many flying labs in different countries.

And you’re planning on developing some in-house engineering capacity too?

Yes, because there are many problems, like conservation in developing countries, which can be solved with existing technology. But there are also many problems that can be solved with technology, but the technology doesn’t exist yet, because the people developing the technology—whether it’s Silicon Valley, China, or Europe—aren’t necessarily aware of the problems that exist in developing countries.

Whatever works, whatever’s needed to meet your goals on the ground then?

Yes. Unfortunately, there is no lack of work in development. Poverty is not disappearing next year. It’s not a market that you have to grasp today otherwise tomorrow it has been taken over. There is a huge amount of work left to do, and there’s definitely enough to go around.

If you liked this article, you may also be interested in:

• Cargo drones deliver in the Amazon rainforest
• The Drone Center’s Weekly Roundup: 2/13/17
• The art of drone flight: Creating drone costumes
• The Monospinner: world’s mechanically simplest controllable flying machine
• Flying cars are coming, what will they mean?

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

Science fiction has already explored the theme of robot rights, such as the film Bicentennial Man. Image: Columbia Pictures

Science fiction likes to depict robots as autonomous machines, capable of making their own decisions and often expressing their own personalities. Yet we also tend to think of robots as property, and as lacking the kind of rights that we reserve for people.

But if a machine can think, decide and act on its own volition, if it can be harmed or held responsible for its actions, should we stop treating it like property and start treating it more like a person with rights?

What if a robot achieves true self-awareness? Should it have equal rights with us and the same protection under the law, or at least something similar?

These are some of the issues being discussed by the European Parliament’s Committee on Legal Affairs. Last year it released a draft report and motion calling for a set of civil law rules on robotics regulating their manufacture, use, autonomy and impact upon society.

Of the legal solutions proposed, perhaps most interesting was the suggestion of creating a legal status of “electronic persons” for the most sophisticated robots.

Approaching personhood

The report acknowledged that improvements in the autonomous and cognitive abilities of robots makes them more than simple tools, and makes ordinary rules on liability, such as contractual and tort liability, insufficient for handling them.

For example, the current EU directive on liability for harm by robots only covers foreseeable damage caused by manufacturing defects. In these cases, the manufacturer is responsible. However, when robots are able to learn and adapt to their environment in unpredictable ways, it’s harder for a manufacturer to foresee problems that could cause harm.

The report also questions about whether or not sufficiently sophisticated robots should be regarded as natural persons, legal persons (like corporations), animals or objects. Rather than lumping them into an existing category, it proposes that a new category of “electronic person” is more appropriate.

The report does not advocate immediate legislative action, though. Instead it proposes that legislation be updated if robots become more complex; if and when they develop more behavioural sophistication. If this occurs, one recommendation is to reduce the liability of “creators” proportional to the autonomy of the robot, and that a compulsory “no-fault” liability insurance could cover the shortfall.

But why go so far as to create a new category of “electronic persons”? After all, computers still have a long way to go before they match human intelligence, if they ever do.

But it can be agreed that robots – or more precisely the software that controls them – is becoming increasingly complex. Autonomous (or “emergent”) machines are becoming more common. There are ongoing discussions about the legal liability for autonomous vehicles, or whether we might be able to sue robotic surgeons.

These are not complicated problems as long as liability rests with the manufacturers. But what if manufacturers cannot be easily identified, such as if open source software is used by autonomous vehicles? Whom do you sue when there are millions of “creators” all over the world?

Artificial intelligence is also starting to live up to its moniker. Alan Turing, the father of modern computing, proposed a test in which a computer is considered “intelligent” if it fools humans into believing that the computer is human by its responses to questions. Already there are machines that are getting close to passing this test.

There are also other incredible successes, such as the computer that creates soundtracks to videos that are indistinguishable from natural sounds, the robot that can beat CAPTCHA, one that can create handwriting indistinguishable from human handwriting and the AI that recently beat some of the world’s best poker players.

Robots may eventually match human cognitive abilities and they are becoming increasingly human-like, including the ability to “feel” pain.

If this progress continues, it may not be long before self-aware robots are not just a product of fantastic speculation.

The EU report is among the first to formally consider these issues, but other countries are also engaging. Peking University’s Yueh-Hsuan Weng writes that Japan and South Korea expect us to live in a human-robot coexistence by 2030. Japan’s Ministry of Economy, Trade and Industry has created a series of robot guidelines addressing business and safety issues for next generation robots.

Electronic persons

If we did give robots some kind of legal status, what would it be? If they behaved like humans we could treat them like legal subjects rather than legal objects, or at least something in between. Legal subjects have rights and duties, and this gives them legal “personhood”. They do not have to be physical persons; a corporation is not a physical person but is recognised as a legal subject. Legal objects, on the other hand, do not have rights or duties although they may have economic value.

Assigning rights and duties to an inanimate object or software program independent of their creators may seem strange. However, with corporations we already see extensive rights and obligations given to fictitious legal entities.

Perhaps the approach to robots could be similar to that of corporations? The robot (or software program), if sufficiently sophisticated or if satisfying certain requirements, could be given similar rights to a corporation. This would allow it to earn money, pay taxes, own assets and sue or be sued independently of its creators. Its creators could, like directors of corporations, have rights or duties to the robot and to others with whom the robot interacts.

Robots would still have to be partly treated as legal objects since, unlike corporations, they may have physical bodies. The “electronic person” could thus be a combination of both a legal subject and a legal object.

The European Parliament will vote on the resolution this month. Regardless of the result, reconsidering robots and the law is inevitable and will require complex legal, computer science and insurance research.

This article was originally published on The Conversation. Read the original article.

Enter artificial intelligence (AI)

There has been much hoopla about AI-based legal systems that, some might have you believe, may soon replace lawyers (no doubt causing a degree of anxiety among some legal professionals). There is some misunderstanding among many lawyers, and much of the public, about what AI systems are presently capable of. Can a legal AI, based on current technology, actually “think” like a lawyer? No. At best, today’s AI is an incomplete substitute for a human lawyer, although it could reduce the need for some lawyers (I’ll get to all that later).

However, something we should think seriously about right now is the long-term implication of the introduction of AI into the legal environment—notably the potential loss of legal wisdom.

Why doesn’t AI think like a human?

Let’s explore why AI doesn’t actually mimic the human brain. As an example, let’s look at automated translation systems such as those available from Google, Facebook or Microsoft. Such systems might appear to work the way human translators do, but what they actually do is match patterns derived from analyses of thousands, if not millions, of pages of text found on the web, employing a technology known as statistical machine translation. For instance, if such a system wants to know how to translate the English greeting “hello” into French, it scans English and French translations on the web, statistically analyses the correlations between “hello” and various French greetings, then comes to the conclusion that the French equivalent of “hello” is “bonjour”.

Current AI is good at this kind of pattern matching, but less so at cognition and deductive reasoning. Consider the human brain: not only does it store a large number of associations, and accesses useful memories (sometimes quickly, sometimes not), it also transforms sensory and other information into generalisable representations invariant to unimportant changes, stores episodic memories and generalises learned examples into understanding. These are key cognitive capabilities yet to be matched by current AI technology.

Thus, while present AI-based legal systems might analyse judicial decisions—for example, to help litigators gain insights to a judge’s behaviour or a barrister’s track record—they do so by scrutinising existing data to reveal patterns, and not by extrapolating from the content of those decisions the way an experienced human legal professional might.

The temptation to make redundant

As AI systems become more capable, the temptation grows to use such systems not only to supplement but also to eliminate the need for some personnel. An AI system weak in cognition but strong in pattern matching probably could not replace an experienced professional in terms of drawing inferences, deductive reasoning or combining different practice areas to arrive at more comprehensive solutions. However, it could perform certain tasks such as searching for patterns of words in documents for evidence gathering that have hitherto been delegated to lower level staff—such as paralegals, trainees, and junior associates—and do so better than any human could.

While one might argue that the introduction of AI systems will lighten the workload of legal professionals and thereby improve their quality of life, it also potentially diminishes the need for junior legal staff, which would only exacerbate the oversupply problem in the legal profession.

Shrink now, suffer later?

If fewer junior legal professionals are hired, this implies a smaller population of lower level staff, thus a smaller feeder pool for more senior positions. And, as more tasks are automated, this could deprive junior legal professionals of opportunities to gain important experience—ie, get their 10,000 hours. Will this result in fewer quality, experienced legal professionals in the future?

And the future of legal AI?

There are yet two more (albeit related) things to think about.

First: the development and maintenance of a good AI system requires both technical and legal competency. Put another way, a legal AI system programmed by systems experts ignorant in the law will be seriously, if not fatally, flawed. Thus, if we want to continue to develop more capable legal AI systems, good content providers—ie, good lawyers—will be needed.

Second: as laws, the legal business and social environments in their respective jurisdictions evolve, developments that might not have been anticipated just a few years earlier will emerge. Only the very best legal and other minds will be able to cope with some of these developments—and update the relevant legal AI systems accordingly. For example, when the US passed the Leahy-Smith America Invents Act (AIA) in 2011, it introduced new review procedures for existing patents with the intent of improving patent quality. It also had the effect of introducing several unintended consequences, including the use of such procedures by hedge funds to invalidate patents for the purpose of affecting the stock price of the companies holding the patents and the negative impact the AIA has had on inventors. Updating an AI system to properly incorporate these developments requires not only a deep understanding of US patent law but also a perspective on patents, finance and the impact of patent policy and procedures on innovation—something that can only really be appreciated after years of experience. Moreover, this is something that could not have been programmed into an AI system half a decade ago, and such content could probably not have been provided by a less capable, less experienced legal professional to an AI developer.

So what, if anything, can be done?

Sadly, there are no easy answers. Graduating fewer lawyers might alleviate the problem of oversupply, but would also result in unemployment at educational institutions.

While the government (or government-backed NGO) could establish some sort of training centre for under-employed junior lawyers, where these professionals could offer services pro bono to build their experience, this also smacks of government interference in the private practice market. But we need to start thinking of solutions now.  The introduction of AI into the legal profession and the potential prospect of putting more lawyers out of work could have profound implications for legal AI systems and the profession as a whole.

What legal rights should a robot have? In this video, Polo Sant’Anna Valdera, one of the leading research centres for robotics, raises this important question. Innovation raises new legal and ethical issues. The European Parliament wants to address these issues so robotics continues to flourish in 2017.

You may also be interested in:

• RoboLaw: Why and how to regulate robotics
• Law-abiding robots? What should the legal status of robots be?
• Hot topic: Regulating robotics
• Robots Podcast #128: Ethical, social and legal issues, with Pericle Salvini
• EU needs to take the lead on regulating robots and artificial intelligence, MEPs suggest

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

In this interview, we invited Prof. Rafael Capurro to share his insights on past, current, and future trends in robot ethics.

Interviewee: Prof. Rafael Capurro, Founder, International Center for Information Ethics (ICIE) and Member of IEEE Global Initiative for Ethical Considerations in Artificial Intelligence and Autonomous Systems

Interviewer: Dr. Yueh-Hsuan Weng, Co-founder of ROBOLAW.ASIA and Assistant Professor at Frontier Research Institute for Interdisciplinary Studies, Tohoku University (from March 2017)

As a pioneer of Information Ethics, Prof. Capurro created “The Quest for Roboethics: A Survey” and since 2010 continues to update relevant information about ethical issues in robotics. He’s participated in key European projects, such as ETHICBOTS (2005-2008), ETICA (2009-2012), and CA-RoboCom, which was a candidate project of EU FET Flagship initiatives. He has also collaborated with Asian researchers from Remin University of China and University of Tsukuba, so he possesses a unique point of view about robot ethics from Eastern traditions.

WENG: Maybe we can start with your academic background. Could you please give a brief introduce of yourself and the International Center for Information Ethics (ICIE) to our readers?

CAPURRO: I have a Licentiate (University of El Salvador, Argentina, 1971) and a PhD (Düsseldorf University, Germany, 1978) in Philosophy as well as a post-doctoral degree in Ethics (Stuttgart University, Germany, 1989). I have been working in the field of Documentation/Information Science since the early seventies in Germany. I was a Professor of Information Management and Information Ethics at Stuttgart Media University from 1986 until 2009 when I retired. I was Lecturer in Philosophy/Ethics at Stuttgart University between 1989 until 2004.

In 1999 I founded the International Center for Information Ethics (ICIE). ICIE soon became a community of some 300 colleagues from different countries and fields interested in the ethical issues associated with new technologies. Jared Bielby (Canada) is ICIE Co-Chair. The ICIE (co-)organizes international meetings and has, since 2004, published an open access online journal, the International Review of Information Ethics (IRIE) of which I am the Editor-in-Chief. There is an ICIE book series published by Fink (Munich) which currently has published five volumes (including contributions in German and English). ICIE membership is free of charge. The contact with the members is through a mailing list. We inform our members about meetings and publications that are added to the website.

WENG: In 2010 you created “The Quest for Roboethics: A Survey” and keep updating relevant information in regards to Robot Ethics. As a pioneer of Information Ethics, what made you focus on this emerging subject in techno-ethics?

CAPURRO: This survey was originally a contribution to a workshop held on September 30, 2009 and organized by Cybernics, University of Tsukuba (Japan). It was published in Cybernics Technical Reports: Special Issue on Roboethics (University of Tsukuba 2011, pp. 39-59). At that time, issues dealing with ethics and robotics were relatively new but the field expanded quickly— so much so I decided to update this survey regularly—albeit without seeing it as needing to be an exhaustive overview of the field. It is just a means, for myself and maybe for others, to help to keep pace with a range of ongoing robot ethics discussions.

My interest in robotics and AI goes back to the mid-80s: I participated in a group in charge of developing a Code of Ethics for the German Society for Informatics. I read Joseph Weizenbaum‘s Computer Power and Human Reason (1977), Pamela McCorduck‘s Machines Who Think (1979), David W. Johnson and J.W. Snapper (eds.) Ethical Issues and the Use of Computers (1985), Deborah Johnson’s Computer Ethics (1985), Terry Winograd and Fernando Flores’s Understanding Computers and Cognition (1986), Hans Moravec’s Mind Children (1988), to mention just a few. My first book publication dealing
with these issues was a collection of papers with the title Living in the Information Age (1995).

WENG: In your previous presentation at University of Tsukuba you mentioned that an Eastern tradition of Buddhism to robot ethics is that “robots as one more partner in the global interaction of things”. Could you please explain this?

CAPURRO: I visited Japan for the first time while on sabbatical in 1998 as a guest of the University of Library and Information Science (ULIS), which was integrated later on into the University of Tsukuba. I had conversations on ethical issues associated with the information society with ULIS staff and students as well as on philosophy with Prof. Riuji Endo. I also met Prof. Koichiro Matsuno (Nagaoka University of Technology) with whom I exchanged views on the concept of information. Since then, I have been invited to Japan several times by Prof. Makoto Nakada (Univ. of Tsukuba) with whom I have had the honor, and the pleasure, to learn about robot ethics as they are viewed from within Eastern traditions.

Prof. Nakada and I discussed issues of privacy from an intercultural perspective. The difference associated with how robots are understood in the “Far East” and the “Far West,” to use the terms coined by French sinologist François Jullien, can be traced back to the Cartesian split which conceives the human psyche as separated from the so-called “outside world.” Buddhism and Taoism open different paths of thought, particularly with regard to the Japanese concept of Ba (situation). This dialogue on intercultural robot ethics between Prof. Nakada and myself can be found here.

I would also like to mention two other events that deal with these issues. The first is the international symposium organized by The Uehiro Foundation on Ethics and Education and The Carnegie Council for Ethics in International Affairs (held in December 2010 in Oxford). More details on this can be found in my paper “Beyond Humanisms”. The other event was the International Conference on China’s Information Ethics (organized by Renmin University of China, School of Philosophy (Prof. LI Maosen), and the International Center for Information Ethics (ICIE) held at Renmin University, Beijing, P.R. China, October 28-29, 2010). I presented a paper at the conference entitled: The Dao of the Information Society in China and the Task of Intercultural Information Ethics. The paper was translated into Chinese by Junlan Liang (Chinese Academy of Social Sciences) and published in the journal Social Sciences Abroad ( 2011, Vol. 5, pp. 83-88).

In December 2009, I was invited to participate at the Global Forum on Civilization and Peace organized by The Academy of Korean Studies in Seoul where I was asked to speak about “Digital Ethics.” It was the first time I used this terminology rather than the usual one, namely, Information Ethics. The proceedings were published by The Academy of Korean Studies (ed.): 2009 Civilization and Peace, Korea: The Academy of Korean Studies 2010.

WENG: You and Prof. Nagenborg have pointed out that “Robots are and will remain in the foreseeable future dependent on human ethical scrutiny as well as on the moral and legal responsibility of humans”. In this case, how can we regulate Robo-morality?

CAPURRO: This statement is based on our research within two European projects, namely
– ETHICBOTS (2005-2008), as well as the book edited by me and Michael Nagenborg Ethics and Robotics (Heidelberg, 2009) and ETICA (2009-2012).

To “regulate Robot-morality” means to develop different kinds of standards for robot behavior in different contexts and cultures which is not the same as a sustainable ethical scrutiny on the development and use of robots in society, which is a larger research issue. The idea that robots would some day become philosophers or ethicists — being able to critically reflect on who they are — belongs to the field of science fiction.

WENG: What is your comment to “Ethical Robots”? Do you believe that Asimov’s Three Laws of Robotics will be feasible principles to ensure the safety of physical human-robot interactions?

CAPURRO: As you know, Isaac Asimov (1920-1992) was a science-fiction author. The “laws” were published for the first time in the volume I, Robot (1950). If we look for a legal framework to ensure the safety of humans in human-robot interactions we should pay attention to the issue that humans, as well as other living beings, deserve legal protection in order to ensure their safety. It makes no sense to legally protect robots beyond seeing them as just another device that is the property of some other person or corporation. It makes also no sense to speak about “the safety of physical human-washing machine interactions” aiming for ensuring the safety of washing machines (as if they were a kind of person who deserves protection). This is in line with what Asimov imagined in the title of his book I, Robot. Essentially, we need a broad legal and ethical discussion on the uses and misuses of robots in society.

WENG: Could you please tell us more about the state of arts in Machine Ethics? Are there any progress in terms of programming machines to act with human beings in an ethical way?

CAPURRO: “Machine Ethics” means the ethical reflection on machines done by humans, not machines engaging in such reflection on themselves (and on us). Ethics is done by members of human society reflecting critically on the customs (Greek: ethos, Latin: mores) underlying their being-together. “Machine Ethics” is, from this perspective, i.e., as a critical reflection done by machines, an oxymoron. If we want machines to act “in an ethical way” then we have to provide them with some kind of moral (and legal) rules of behavior. We are responsible for their formulation, fixation and interpretation. It makes no sense to make machines “morally responsible” for their actions.

WENG: “BS 8611: Ethics design and application robots” is a standard highlighting the ethical hazards of robots. Do you think that Ethics by Design might be a crucial factor to future safety standards for service robots?

CAPURRO: Once again, when we talk about the “ethical hazards of robots” the meaning of the “of” is of the kind already explained, i.e. it is genitivus obiectivus. The “Ethics by Design” concerns moral (and legal) rules, not the ethical reflection on such rules nor their interpretation and application. Who is to blame should an accident occur? Machines are per se a-moral which does not mean that they are neutral. Nothing we invent is neutral in the sense that it is already part of human life and it changes, for better or worse, our lives. The idea that machines are neutral because we can use them for different purposes disregards this basic issue. Machines do not behave at all in the way humans deal with each other (with possibilities of respect or disrespect concerning who we are). It is up to us to reflect on the risks we want to take for ourselves and for others when we program a machine to behave in this or that way in a given situation. As it is impossible to foresee all possible situations, every fixation of rules means making a selection of situations. Dilemmas arise when fixed rules do not match unforeseeable situations. Humans can be creative in such cases as they are not bound to fixed rules. This does not mean, of course, that a human decision can end less tragically than a “decision” made by a machine. In sum, we should let robots act in situations in which we can exclude the unforeseeable as far as possible. Beyond that we should take responsibility for the solution of the dilemma the machine is facing; it is our dilemma from the very moment we fix their rules of behavior.

WENG: In your opinion what is the major challenge to develop robot ethics for the following decade?

CAPURRO: This is an international and intercultural challenge since it deals with a critical reflection on societal customs, or mores, and the different kinds of moral and legal rules that will be developed for robots. There might be some global standards of such rules, but if robots are used in specific situations then the social expectations might differ and a translation process is necessary. This can be a very practical process but if we want to question why people expect this or that, in this or that context in different societies, then we have to go deep into underlying moral and ethical traditions that were built over centuries. Moral and legal rules are a kind of “symbolic immune system” for societies according to the German philosopher Peter Sloterdijk. Symbolic immune systems, no less than biological ones, can themselves become a danger if they are not flexible enough to change according to new situations. Robot ethics is similar to immunology in the sense that it deals with monitoring the efficiency and effectiveness of symbolic immune systems when we inoculate moral (and legal) rules into robots.

If you liked this article, you may also be interested in:

• One being for two origins: A new perspective on roboethics
• Survey: Evaluate ethics of health-related privacy with care robots
• On the ethics of research in robotics, with Raja Chatila
• Technical challenges in machine ethics
• A European perspective on robot law: Interview with Mady Delvaux

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

There’s a great deal of concern over artificial intelligence; what it means for our jobs, whether robots will one day replace us in the workplace, whether it will one day lead to robot wars. But current research projects show that artificial intelligence (AI) can also be used for the greater good. Here are five global problems that machine learning could help us solve.

1. Healthcare

One of the biggest benefits of AI is its ability to trawl through massive amounts of data in record time. This helps researchers pinpoint areas of focus for their own research.

For example, a recent ground-breaking discovery on the disease Amyotrophic Lateral Sclerosis (ALS), was made through a partnership between Barrow Neurological Institute and the artificial intelligence company IBM Watson Health.

IBM Watson, the artificial intelligence computer, reviewed thousands of pieces of research and was able to identify new genes linked to ALS.

“Traditional research tools are fast becoming inadequate to help data scientists and researchers keep pace with any global problems that AI could help us solve and find relevant insights among the now billions of documents which are spread all over the world,” said the company in a press release.

“The discovery gives ALS researchers new insights that will pave the way for the development of new drug targets and therapies to combat one of the world’s most devastating and deadly diseases.”

https://youtu.be/F-qBLH6EfR8

Another promising use for AI within healthcare is its ability to predict the outcome of drug treatments. For instance, cancer patients are often given the same drug, and then monitored to see the effectiveness of that drug. AI could use data to predict which patients could benefit from using a particular drug, providing a highly personalized approach, and saving valuable time and money.

Image: Bloomberg

2. Making driving safer

Despite the crashes involving self-driving cars that have hit the headlines this year, this area of AI could dramatically reduce deaths and injuries on our roads.

According to a report by Stanford University, not only will self-driving cars reduce traffic related deaths and injuries, but they could bring about changes in our lifestyles as well. We will have more time to work or entertain ourselves during commutes, and we may have more choice over where we base ourselves:

“The increased comfort and decreased cognitive load with self-driving cars and shared transportation may affect where people choose to live,” the report says.

3. Transforming how we learn

Earlier this year, students at Georgia Tech university in the US were startled to discover that their helpful teaching assistant had in fact been a robot all along. After initial teething problems, the robot started answering the students’ questions with 97% certainty.

The university designed the robot after their research showed that one of the main factors behind students dropping out is a lack of support.

People learn differently, at different speeds and with different starting points. Artificial intelligence could usher in a future where we all learn in a much more personalised way. But no education system in the world can afford a tutor for every child, so this is where AI might be able to step in. Artificial tutors, made to look and sound as much like humans as possible, could take the lead in delivering personalised education.

4. Help us be smarter about energy

Artificial intelligence could help us be smarter about our energy consumption. In fact, this is already happening.

Image: Google DeepMind

Google and other tech giants have enormous data centres that require a massive amount of energy to run the servers and keep them cool. Google has used its artificial intelligence platform Deep Mind to predict when its data centres will get too hot. Cooling systems are only activated when required. AI has saved Google around 40% in energy costs at its server farms.

5. Helping wildlife

As in the case of healthcare, being able to analyse massive amounts of data can transform wildlife conservation.

For instance, by tracking animal movements, we can see where they go, and what habitats we need to protect. This study uses computing power to figure out the best places to create wildlife corridors for wolverines and grizzly bears in Montana. Wildlife corridors are continuous areas of protected land that link zones of biological significance that the animals can use to move safely through the wilderness.

Challenges

Using artificial intelligence is not without its challenges, however. One of the biggest of these is – how do we keep the systems safe? Algorithms are based on data, so any change to that data will change the behaviour and outcomes.

“Almost anything bad you can think of doing to a machine-learning model can be done right now,” said one expert at a recent AI conference in Spain. “And defending it is really, really hard.”

This post was originally published on WEForum.org.

You might also enjoy the following articles:

• Why artificial intelligence could be key to future-proofing the grid
• Wearable AI that can detect the tone of a conversation
• Artificial intelligence and ethics: Who does the thinking?
• Soft exosuit economies: Understanding the costs of lightening the load
• MIT Media Lab to participate in $27 million initiative on AI ethics and governance

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

How Governments Can Promote Automated Driving recommended that governments conduct “legal audits” to “identify and analyze every statute and regulation that could apply adversely or ambiguously to automated driving.” Automated Vehicles Are Probably Legal in the United States attempted this nationwide, and now the authors of Georgia’s HB 248 have produced a bill that (while not perfect) reflects a thoughtful effort to do the same in that state.

HB 248 incorporates terms and concepts from SAE J3016, maps a variety of existing legal provisions onto automated driving, and designates the manufacturer of a vehicle operated by an automated driving system as the vehicle’s sole driver.

The authors could have defined this driver as the developer of the automated driving system rather than the manufacturer of the platform vehicle. However, like other “SAVe” acts, this bill would limit its special legal framework to motor vehicle manufacturers that deploy their vehicles as part of fleets within specific geographic areas.

An open question is whether this special framework would preclude other developers of or approaches to automated driving. HB 248 need not — and should not — establish an exclusive legal framework for all automated driving applications in the state. This is because automated driving involves credible developers other than vehicle manufacturers as well as potential approaches other than truly driverless systems (even though these driverless systems remain my favorite).

Michigan actually did enact a “SAVe” law limited to manufacturers, although a companion bill relaxed the limitation somewhat, and in any case, that state’s simultaneous passage of multiple bills seems to suggest that the “SAVe” framework is not the only one under which automated driving could lawfully occur. (But it’s hard to know for sure; those Michigan laws are a collective mess.) A similar bill pending in Maryland, however, would have a clearly restrictive effect.

While you’re browsing legislation, check out Virginia’s bill on “electric personal delivery devices.” Sidewalk delivery robots are exciting in their own right — and they provide an important reminder that transportation automation is much more than just cars.

My publications are at newlypossible.org.

An Air Force airman at the control module of an MQ-9 Reaper. Credit: John Bainter/USAF

Machine ethics offers an alternative solution for artificial intelligence (AI) safety governance. In order to mitigate risks in human-robot interactions, robots will have to comply with humanity’s ethical and legal norms, once they’ve merged into our daily life with highly autonomous capability. In terms of technical challenges, there are still many open questions in machine ethics. For example, what is deontic logic and how can it be used for improving AI safety? How do we fashion the knowledge representation for ethical robots? What about the adaptiveness of the ethical governance? These are all significant questions for us to investigate.

In this interview, we invite Prof. Ronald C. Arkin to share his insights on robot ethics, with a focus on its technical aspects.

Date: February 1st 2017

Interviewee: Prof. Ronald C. Arkin, Regents’ Professor and Associate Dean in the College of Computing at the Georgia Institute of Technology and Executive Committee Member of IEEE Global Initiative for Ethical Considerations in Artificial Intelligence and Autonomous Systems

Interviewer: Dr. Yueh-Hsuan Weng, Co-founder of ROBOLAW.ASIA and Fellow at CLAST AI & Law Research Committee (Beijing) and Tech and Law Center (Milan).

Ron Arkin

Q1 WENG: Thank you for agreeing to an interview with us. Can you tell us a little about your background?

ARKIN: I have been a roboticist for 35 years, so it’s hard to tell [only] a little, but I will do my best. Currently I am a Regents’ Professor and Associate Dean in the College of Computing at the Georgia Institute of Technology. I got my Ph.D. from the University of Massachusetts in 1987, and have been working on robotics since that time. I actually have degrees in chemistry as well—a bachelor’s and a master’s in organic chemistry. So, I have a rather diverse background and followed a non-traditional path compared to most computer scientists.

Q2 WENG: What is your definition of “Ethical Robots”?

ARKIN: That’s an interesting question! I have been asked that one before. I would say an ethical robot is a robotic system that is able to comply with the ethical and social norms of human beings—in some cases in accordance with international law, in other cases in accordance with national or local law. In addition, also being able to comport with humans in a natural manner, obey, and adhere to the same principles as an ethical person.

Q3 WENG: Could you please tell us why you are particularly interested in “Embedded Ethics”? Why is this concept so important?

ARKIN: That’s why you need to read the preface of my book [Governing Lethal Behaviour in Autonomous Robots]. It’s a rather long story. But let me try to give you a short version. Again, it dates back to a meeting—the first international symposium in Roboethics in 2004—where I was exposed to international conventions, e.g., the Geneva convention. It also led me to understand the Vatican’s position on how they viewed appropriate ethical interactions. Perhaps even more important than that one experience was the fact that we roboticists that have been in the field for some time finally started to have success, and we were seeing our technology move out of the laboratory community and into the real world. As such, it wasn’t hard to see how this could go in a variety of different directions.

Some robotics technology could benefit humanity, some not so much. At that point, one has to ask, “who is responsible for this?” So I assumed that, in part, it was myself. I believe that I have to take the responsibility for the technology that I helped to create. The issue is that we, as roboticists, had actually succeeded in many ways in terms of creating technology that we only envisioned when we first started that is going to have potentially profound impact on our society and the world as a whole. As such, we should be proactive in the ways in which we move forward [in order] to get a better understanding of the consequences of the work, and the social implications of the technology we are designing.

https://www.youtube.com/watch?v=jOJwkvihYtc

Q4 WENG: The IEEE Global Initiative for Ethical Considerations in Artificial Intelligence and Autonomous Systems has published their report “Ethically Aligned Design” in December 2016. As a member of the IEEE Global Initiative, could you please tell us about your role?

ARKIN: I serve a variety of roles. First, I am on the executive committee of that particular initiative, so I work at a high-level with about 30 other people or so. We have committees that large. I also co-chair the committee in Affective Computing. Joanna Bryson is my co-chair on that.

Affective Computing is concerned with the issues surrounding the illusion of emotions we can potentially create in these artifacts, and the ways human beings can bond to them. We are exploring a variety of different things, including robot intimacy, or the notion of applying robotic nudges and moving people in certain directions subtly, and in so doing to change the way people act and behave using these technologies, while trying to understand what may or may not be appropriate guidelines based on ethical considerations.

It’s about 15-20 people in that committee. We are one of the newer committees and we only have a couple of pages in the initial design report. But actually, in the June meeting in Austin, for the next release of the document 2.0, we will have as much as the other committees do. Finally, I am also a member of the humanitarian committee. I think it is called economic and humanitarian. I don’t know the exact title, but I am interested in the humanitarian side, and potential uses of robotics for humanitarian applications.

Q5 WENG: What is “Deontic Logic”? Why does it matter to machine ethics?

ARKIN: It may or may not matter. It’s just one approach to a particular problem. Deontic logic is a logic of prohibitions, obligations and permissions. It is one type of framework that is more transparent in order to get a better understanding of the way in which systems operate in the real world. Then it explains its reasoning to a human being, as compared to say deep learning or other forms of neural networks which may indeed provide good results but provide no explanatory capability to humans after the decision has been made.

The notion of prohibitions basically tells the robot some things it cannot do. The notion of permissions describe the things it is potentially allowed to do. And the notion of obligations says things that the robot must do under specific circumstances. It provides a nice framework of being able to explore morality. Others have already explored this, like Bringsjord at RPI who has applied deontic logic in this framework. We are certainly not the first in that particular space. Much of the work we have done has dealt with first-order predicate logic as a basis. It doesn’t have to be deep because it is so explicit in many cases.

Q6 WENG: Your hybrid embedded ethics framework aims for governing lethal behavior of military robots. Can we apply the same technical framework to service robots? For example, personal care robots might have some non-lethal, but still risky autonomous behaviors that need to be regulated by machine ethics.

ARKIN: I believe so. Indeed the whole point of the architecture is trying to design the systems to make them generalizable and not ad hoc for particular sets of circumstances. We have yet to look at service robotics in a traditional sense, but we have already developed something we refer to as the intervening Ethical Governor, which we are using for health care applications—particularly for assisting the management of two human beings in a particular relationship, like a patient and their care giver.

A robot hands a medication bottle to a person. Photo credit: Keith Bujak. Source: Georgia Tech News Center

We are doing work for the National Science Foundation, joint with Tufts University, looking at how in early stage Parkinson’s there is a condition called the facial masking. The loss of muscle movements in the face of the patient can produce stigmatization, and the patient-care giver relationship deteriorates due to the loss of non-verbal communication. We just had a paper accepted at ICRA describing the architecture that we have and how it has been modified from the military version. We generalized it from the military constraint-based version to a rule-based version.

But also, instead of using the international humanitarian law, we are using occupational therapy guides as a basis for generating the responses when two human beings in the relationship step outside of acceptable bounds. So the acceptable bounds now are not defined by international humanitarian law, but they are defined by occupational therapy—such as when a patient or a care giver start to yell at each other, or one starts to leave the room. The hard part is recognizing when dignity is being abridged, particularly in the patient. So we model the moral emotions of empathy, shame and embarrassment. We tried to ensure that the empathy level in the care giver remains within acceptable bounds and that the patient shame and embarrassment doesn’t rise to unacceptable levels. So the intervening Ethical Governor intervenes when a really inappropriate event occurs. It then provides strong corrective action by the robot to encourage people to get along better. The moral adaptor component which we used in the ethical architecture—initially with the guilt model—has now been extended to other moral emotions like shame, embarrassment, guilt and empathy [in order to] try and provide more subtle non-verbal queues [to help the] robot steer the relationship in the correct way before it get out of hand.

Q7 WENG: Regarding your research on machine ethics, what kind of indicators you use to evaluate the final outputs of your project?

ARKIN: In the first one, we were designing a prototype to prove the concept of the architecture. We never make the claim that this is the best way to do it. It is just one way to do it. Then we demonstrate that particular capability through the use of simulations and other aspects. The system was not intended to be fielded, but hopefully others could take those core ideas and potentially put them to use in the real world if they so choose. For the intervening governor, we’re running human subject tests right now—not with Parkinson’s patients yet, but with the elderly population, 50 or older. We’re trying to get a better understanding of the correct sets of rules, and to correct the interventions in a normative fashion, so we can ultimately move on in the final year of the grant to work with actual Parkinson’s patients. Again, using standard HRI technics, such as recording the kinds of reactions they have and parsing the data from the sessions and using pre- and post- evaluation questionnaires.

Q8 WENG: Let’s talk about your proposed machine ethics framework. You had mentioned that it includes four main parts: (1) Ethical Governor (2) Ethical Behavior Control (3) Ethical Adaptor (4) Responsibility Advisor. First of all, what is the major difference between Ethical Governor and Ethical Behavior Control? Is it the difference between deliberative and reactive control, or something else?

ARKIN: The Ethical Governor is basically a standalone component to the architecture which is a bolt on component at the end of the existing system. In the case of military scenario: asking for permission—should it fire or not. This is an intervening stage which either grants or denies permission through the validation of permission-to-fire variable. So it is straightforward thing to be able to do. The Behavior Ethical Control is a different kind of thing as it works purely with the reactive component. What it tries to do is, for each of the individual behaviors themselves, to respond whether or not to engage a target. This is prior to dealing with the Ethical Governor making the decision; trying to make the decision correctly before it gets to the Ethical Governor, where individual behaviors are given ethical guidelines to try and ensure that the behavior is correct itself. Each of the behaviors is provided with appropriate ethical constraints. So the difference is: one is located within the reactive component in each of the individual behaviors that makes the activity of the architecture, and the Ethical Governor is at the end of the architectural chain.

Q9 WENG: What is the importance of Ethical Adaptor? What kinds of machine learning skill that you chose to improve its adaptiveness?

ARKIN: Machine learning is something we haven’t generally explored in this particular context. Especially the military context, we kind of engineer out the adaptiveness and machine learning for fear that the robot would be granted the authority to start to generalize the targets in the field, which we feel is inappropriate. Learning after action is okay. but that’s a different story. The importance of the Ethical Adaptor, and its cognitive models of guilt that we use, is the fact that even the actions the agent takes may inadvertently end up unethical. This is referred to as a “moral luck”. It could be good luck or bad luck. But if it is bad luck, you want to be able to ensure proactively that the bad action doesn’t occur again. That’s actually the functions of cognitive guilt for people as well to reduce the likelihood of reoccurrence of a behavior that resulted in bad actions. By giving this to a robot we hope that if it makes a mistake it will not repeat it. The probability of that behavior reoccurring would be reduced.

The best example is the case of battle damage assessment for a military scenario. For example, if a robot does its assessment and it looks like a released weapon wouldn’t create too much collateral damage, within acceptable limits, then it will release the weapon. But after it does it, the battle damage action is actually more severe then, it initially appeared to be. Under those circumstances, we want the robot in the field to reduce the probability of releasing the weapon again under similar circumstances. That’s, in essence, the analogue of guilt we use, and eventually, it will get to the point that the robot will not engage targets at all. Again, the software is updated to make sure the use is consistent with Geneva convention.

Q10 WENG: The Laws of War (LOW) and Rules of Engagement (ROE) are written in the form of natural language. How did you transfer these norms into machine readable formats?

ARKIN: It’s important to keep it in mind that we do not try and capture all the Rules of Engagement and all the Laws of War. We always advocate that the system should be used in very narrow circumstances and very narrow situations, such as building clearing operations or counter-sniper operations. It’s impossible to capture all human morality and all the Laws of War at this particular point of time. So, the question is what are the relevant Laws of War and Rules of Engagement for these particular narrow situations that the robot will find itself in.

Let’s take a room clearing operation. We try and do this using these constraint-based approaches. The Laws of War basically say that if you see a civilian, you should not shoot them; if you see a surrendered person, you should not shoot them. For aerial vehicles, if you are outside the kill zone (this is the simulations we did) which is designated by the Rules of Engagement, you cannot fire your weapon—explain simply why you cannot do that. If you release the weapon a toward a legitimate target but it would result in damage of cultural property, like a mosque or church or some similar circumstance, that is illegal as well. You are forbidden from doing those sort of things.

So, the initial work we did was more for aerial circumstances. We also computed the collateral damage using a battle damage assessment strategy to determine whether if it is within acceptable limits for a particular target and to restrain the weapon for proportionality. To be able to do this you have to be able to understand the military necessity of the target, which is assigned by a human being. You have to be able to understand the proportionality of the weapon. In other words: Which weapon is sufficient to take out the target but to do no more damage of the surroundings than is absolutely necessary? You have to ensure that you don’t destroy cultural property as well. Basically, adhere to all necessary principles, such as discrimination which is to be able to tell apart combatants and non-combatants. We also state we should restrict the use of these particular systems for high-intensity combat operations, or inter-state warfare, and not the kind of circumstances you find in modern counter- insurgency operations. They should be used with great care and great caution in very narrow circumstances where the predictability of their performance is far more likely to be good.

Q11 WENG: Why did you choose case-based reasoning (CBR) but not rule-based reasoning (RBR) when you designed Responsibility Advisor?
ARKIN: Historically we used case-based reasoning a lot for a variety of different reasons. Part of the reason is to gain information from previous experts. One of the examples in the past was to work with snipers in the laboratory. We can work with experts. It can be legal experts, it can be medical experts, it can be military experts. Again, the primary reason is to be able to readily use the experience of warfighters to guide us in operations.

Q12 WENG: Finally, your proposal “Governing Lethal Behavior: Embedding Ethics in a Hybrid Deliberative/Reactive Robot Architecture” was published in 2007. There have been great technological improvements in artificial intelligence in the past decade. Are there any recent updates from your robot ethics research, or anything that you want to share with us?

ARKIN: The project ended around 2010, so there is additional information. As I say, I would encourage those who are really interested to take a look at the book entitled “Governing Lethal Behavior in Autonomous Robots” . That represents the final statement and outcome for that particular project and includes the significant technical details describing that work. Recent advances include the application in other domains, such as the caregiver-patient relationship [discussed above] using the same underlying principles, including both the Moral Adaptor and Ethical Governor in a slightly different. If we were successful, it may also be useful for assisting other human-human relationships, such as a parent and a teenager who may be at odds with one another or even in marriage consulting. We hope to use robots for the benefit of mankind, whether it’s to make sure that warfare is conducted within internationally agreed upon laws, or whether it’s to ensure that people behave appropriately toward each other

If you enjoyed this article, you may also enjoy:

• How do we regulate robo-morality?
• One being for two origins: A new perspective on roboethics
• Considering robot care, ethics & future tech at #ERW2016 European Robotics Week
• On the ethics of research in robotics, with Raja Chatila
• A European perspective on robot law: Interview with Mady Delvaux-Stehres
• Open Roboethics initiative delivers statement to United Nations CCW

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

Part 2: Autonomous Systems and Transparency

In my previous post I argued that a wide range of AI and Autonomous Systems (from now on I will just use the term AS as shorthand for both) should be regarded as Safety Critical. I include both autonomous software AI systems and hard (embodied) AIs such as robots, drones and driverless cars. Many will be surprised that I include in the soft AI category apparently harmless systems such as search engines. Of course no-one is seriously inconvenienced when Amazon makes a silly book recommendation, but consider very large groups of people. If a truth (such as global warming) is – because of accidental or willful manipulation – presented as false, and that falsehood is believed by a very large number of people, then serious harm to the planet (and we humans who depend on it) could result.

I argued that the tools barely exist to properly assure the safety of AS, let alone the standards and regulation needed to build public trust, and that political pressure is needed to ensure our policymakers fully understand the public safety risks of unregulated AS.

In this post I will outline the case that transparency is a foundational requirement for building public trust in AS, based on the radical proposition that it should always be possible to find out why an AS made a particular decision.

Transparency is not one thing. Clearly your elderly relative doesn’t require the same level of understanding of her care robot as the engineer who repairs it. Nor would you expect the same appreciation of the reasons a medical diagnosis AI recommends a particular course of treatment as your doctor. Broadly (and please understand this is a work in progress) I believe there are five distinct groups of stakeholders, and that AS must be transparent to each, in different ways and for different reasons. These stakeholders are: (1) users, (2) safety certification agencies, (3) accident investigators, (4) lawyers or expert witnesses and (5) wider society.

1. For users, transparency is important because it builds trust in the system, by providing a simple way for the user to understand what the system is doing and why.
2. For safety certification of an AS, transparency is important because it exposes the system’s processes for independent certification against safety standards.
3. If accidents occur, AS will need to be transparent to an accident investigator; the internal process that led to the accident need to be traceable.
4. Following an accident lawyers or other expert witnesses, who may be required to give evidence, require transparency to inform their evidence. And
5. for disruptive technologies, such as driverless cars, a certain level of transparency to wider society is needed in order to build public confidence in the technology.

Of course the way in which transparency is provided is likely to be very different for each group. If we take a care robot as an example transparency means the user can understand what the robot might do in different circumstances; if the robot should do anything unexpected she should be able to ask the robot ‘why did you just do that?’ and receive an intelligible reply. Safety certification agencies will need access to technical details of how the AS works, together with verified test results. Accident investigators will need access to data logs of exactly what happened prior to and during an accident, most likely provided by something akin to an aircraft flight data recorder (and it should be illegal to operate an AS without such a system). And wider society would need accessible documentary-type science communication to explain the AS and how it works.

Source: ROBOTT-NET

Image: IEEE

On the 15th November 2016, the IEEE’s AI and Ethics Summit posed the question: “Who does the thinking?” In a series of key-note speeches and lively panel discussions, leading technologists, legal thinkers, philosophers, social scientists, manufacturers and policy makers considered such issues as:

• The social, technological and philosophical questions orbiting AI.
• Proposals to program ethical algorithms with human values to machines.
• The social implications of the applications of AI.

Click here to read the final report, and enjoy a selection of videos from the conference below.

Welcome: Marko Delimar
Keynote Speaker: Wojciech Wiewiórowski

Panel 1: Autonomous Systems – Managing Risk and Reward
Panel Members: Raja Chatila, Kay Firth-Butterfield, Juha Heikkilä, Jérôme Perrin

Panel 2: Programming Human Ethics: Cui Bono?
Panel Members: Joanna Bryson, Mady Delvaux, Sarah Spiekermann, Corrine Cath

Panel 3: Social Implications – Perils & Promises of AI
Panel Members: Greg Adamson, Nikolaos Mavridis, Paul Nemitz, Aurélie Pols

Closing Remarks: John C. Havens

You might also enjoy the following articles about AI and AI/robotics policy:

• MIT Media Lab to participate in $27 million initiative on AI ethics and governance
• How artificial intelligence is changing our Christmas shop
• White House report: Artificial intelligence, automation, and the economy
• White House launches public workshops on AI issues
• White House releases reports focusing on opportunities and challenges of AI

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

Part 1: Autonomous Systems and Safety

We all rely on machines. All aspects of modern life, from transport to energy, work to welfare, play to politics depend on a complex infrastructure of physical and virtual systems. How many of us understand how all of this stuff works? Very few I suspect. But it doesn’t matter, does it? We trust the good men and women (the disgracefully maligned experts) who build, manage and maintain the infrastructure of life. If something goes wrong they will know why. And (we hope) make sure it doesn’t happen again.

All well and good you might think. But the infrastructure of life is increasingly autonomous – many decisions are now made not by a human but by the systems themselves. When you search for a restaurant near you the recommendation isn’t made by a human, but by an algorithm. Many financial decisions are not made by people but by algorithms; and I don’t just mean city investments – it’s possible that your loan application will be decided by an AI. Machine legal advice is already available; a trend that is likely to increase. And of course if you take a ride in a driverless car, it is algorithms that decide when the car turns, brakes and so on. I could go on.

Algorithmic trading is now commonplace

These are not trivial decisions. They affect lives. The real world impacts are human and economic, even political (search engine results may well influence how someone votes). In engineering terms these systems are safety critical. Examples of safety critical systems that we all rely on from time to time include aircraft autopilots or train braking systems. But – and this may surprise you – the difficult engineering techniques used to prove the safety of such systems are not applied to search engines, automated trading systems, medical diagnosis AIs, assistive living robots, delivery drones, or (I’ll wager) driverless car autopilots.

Why is this? Well, it’s partly because the field of AI and autonomous systems is moving so fast. But I suspect it has much more to do with an incompatibility between the way we have traditionally designed safety critical systems, and the design of modern AI systems. There is I believe one key problem: learning. There is a very good reason that current safety critical systems (like aircraft autopilots) don’t learn. Current safety assurance approaches assume that the system being certified will never change, but a system that learns does – by definition – change its behaviour, so any certification is rendered invalid after the system has learned.

And as if that were not bad enough, the particular method of learning which has caused such excitement – and rapid progress – in the last few years is based on Artificial Neural Networks (more often these days referred to as Deep Learning). A characteristic of ANNs is that after the ANN has been trained with datasets, any attempt to examine its internal structure in order to understand why and how the ANN makes a particular decision is impossible. The decision making process of an ANN is opaque. Alphago’s moves were beautiful but puzzling. We call this the black box problem.

Safety critical systems such as aircraft autopilots do not learn

Does this mean we cannot assure the safety of learning autonomous/AI systems at all? No it doesn’t. The problem of safety assurance of systems that learn is hard but not intractable, and is the subject of current research*. The black box problem may be intractable for ANNs, but could be avoided by using approaches to AI that do not use ANNs.

But – here’s the rub. This involves slowing down the juggernaut of autonomous systems and AI development. It means taking a much more cautious and incremental approach, and it almost certainly involves regulation (that, for instance, makes it illegal to run a driverless car unless the car’s autopilot has been certified as safe – and that would require standards that don’t yet exist). Yet the commercial and political pressure is to be more permissive, not less; no country wants to be left behind in the race to cash in on these new technologies.

This is why work toward AI/Autonomous Systems standards is so vital, together with the political pressure to ensure our policymakers fully understand the public safety risks of unregulated AI.

In my next blog post I will describe one current standards initiative, towards introducing transparency in AI and Autonomous Systems based on the simple principle that it should always be possible to find out why an AI/AS system made a particular decision.

The next few years of swimming against the tide is going to be hard work. As Luke
Muehlhauser writes in his excellent essay on transparency in safety-critical systems “…there is often a tension between AI capability and AI transparency. Many of AI’s most powerful methods are also among its least transparent”.

*some, but nowhere near enough. See for instance Verifiable Autonomy.

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

The population of the scenic ski-resort Davos, nestled in the Swiss Alps, swelled by nearly +3,000 people between the 17^th and 20^th of January. World leaders, academics, business tycoons, press and interlopers of all varieties were drawn to the 2017 World Economic Forum (WEF) Annual Meeting. The WEF is the foremost creative force for engaging the world’s top leaders in collaborative activities to shape the global, regional and industry agendas for the coming year and beyond. Perhaps unsurprisingly given recent geopolitical events, the theme of this year’s forum was Responsive and Responsible Leadership.

With the onset of the fourth industrial revolution, increasingly discontented segments of society not experiencing congruous economic and social progress are in danger of existential uncertainty and exclusion. Responsive and Responsible Leadership entails inclusive development and equitable growth, both nationally and globally. It also involves working rapidly to close generational divides by exercising shared stewardship of those systems that are critical to our prosperity.

In the end, leaders from all walks of life at the Annual Meeting 2017 must be ready to react credibly and responsibly to societal and global concerns that have been neglected for too long.”

Developing last year’s theme—“The fourth industrial revolution”—this year’s luminaries posited questions, among many others, concerning incipient robotics and artificial intelligence technologies set to have a pronounced impact on the global economy and global consciousness alike. What can we learn from the first wave of AI? How can the humanitarian sector benefit from big data algorithms? How will drone technology change the face of warfare? Can AI and computational tech help foster responsive and responsible leadership? What are the downsides of technology in the fourth industrial revolution?

Enjoy a selection of tech-themed videos below.

And a bit about global science including big data, open source science and education.

EU rules for the fast-evolving field of robotics, for example, compliance with ethical standards and liability for accidents involving driverless cars, should be put forward by the EU Commission, urged the Legal Affairs Committee.

Rapporteur Mady Delvaux (S&D, LU) said: “A growing number of areas of our daily lives are increasingly affected by robotics. In order to address this reality and to ensure that robots are and will remain in the service of humans, we urgently need to create a robust European legal framework”. Her report, approved by 17 votes to 2, with 2 abstentions, looks at robotics-related issues such as liability, safety and changes in the labour market.

MEPs stress that EU-wide rules are needed to fully exploit the economic potential of robotics and artificial intelligence and guarantee a standard level of safety and security. The EU needs to take the lead on regulatory standards, so as not to be forced to follow those set by third states, argues the report.

A new European Agency for robotics and a Code of Ethical Conduct

MEPs urge the Commission to consider creating a European agency for robotics and artificial intelligence to supply public authorities with technical, ethical and regulatory expertise.

They also propose a voluntary ethical conduct code to regulate who would be accountable for the social, environmental and human health impacts of robotics and ensure that they operate in accordance with legal, safety and ethical standards.

For example, this code should recommend that robot designers include “kill” switches so that robots can be turned off in emergencies, they add.

Liability rules

MEPs note that harmonised rules are urgently needed, especially for self-driving cars. They call for an obligatory insurance scheme and a fund to ensure victims are fully compensated in cases of accidents caused by driverless cars.

In the long-term, the possibility of creating a specific legal status of “electronic persons” for the most sophisticated autonomous robots, so as to clarify responsibility in cases of damage, should also be considered, MEPs say.

Social impact

The development of robotics could also result in big societal changes, including the creation and loss of jobs in certain fields, says the text. It urges the Commission to follow these trends closely, including new employment models and the viability of the current tax and social system for robotics.

Request for legislation

This legislative initiative invites the Commission to present a legislative proposal. It is not obliged to do so but must state its reasons if it refuses.

The full house will vote on draft proposals in February and needs an absolute majority.

If you like this article, you may also want to read:

• American law needs a reboot when it comes to robots
• Law-abiding robots? What should the legal status of robots be?
• A European perspective on robot law: Interview with Mady Delvaux
• The Robot Economy: Interview with MEP Mady Delvaux

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

We’re beginning to see more and more jobs being performed by machines, even creative tasks like writing music or painting can now be carried out by a computer.

But how and when will machines be able to explain themselves? Should we be worrying about an artificial intelligence taking over our world or are there bigger and more imminent challenges that advances in machine learning are presenting here and now?

Join Professor Brian Cox, the Royal Society Professor of Public Engagement, as he brings together experts on AI and machine learning to discuss key issues that will shape our future. Panelists include:

• Professor Jon Crowcroft FRS, Marconi Professor of Networked Systems at the University of Cambridge
• Professor Joanna Bryson, Reader in AI Ethics, University of Bath and Affiliate, Center for Information Technology Policy, Princeton University
• Professor Sabine Hauert, Lecturer in Robotics at the University of Bristol, and President and Co-Founder of Robohub.org
• Professor Murray Shanahan, Professor of Cognitive Robotics at Imperial College London
• Professor Peter Donnelly FRS FMedSci, Director, Wellcome Trust Centre for Human Genetics, and Professor of Statistical Science, University of Oxford

https://www.youtube.com/watch?v=IunNpGGt3H8

You might also enjoy the following articles about AI and AI/robotics policy:

• MIT Media Lab to participate in $27 million initiative on AI ethics and governance
• How artificial intelligence is changing our Christmas shop
• White House report: Artificial intelligence, automation, and the economy
• White House launches public workshops on AI issues
• White House releases reports focusing on opportunities and challenges of AI

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

Image: Gerd Altmann

The MIT Media Lab and the Berkman Klein Center for Internet and Society at Harvard University will serve as the founding anchor institutions for a new initiative aimed at bridging the gap between the humanities, the social sciences, and computing by addressing the global challenges of artificial intelligence (AI) from a multidisciplinary perspective.

“Artificial intelligence agents will impact every part of our lives in every society on Earth. Technology and commerce will see to that,” says Alberto Ibargüen, president and CEO of the John S. and James L. Knight Foundation, which is among those supporting the initiative.

Initially funded with $27 million from the Knight Foundation; LinkedIn co-founder Reid Hoffman; the Omidyar Network; the William and Flora Hewlett Foundation; and Jim Pallotta, founder of the Raptor Group, the Ethics and Governance of Artificial Intelligence Fund’s mission is to catalyze global research that advances AI for the public interest, with an emphasis on applied research and education. The fund will also seek to advance public understanding of AI.

“AI’s rapid development brings along a lot of tough challenges,” explains Joi Ito, director of the MIT Media Lab. “For example, one of the most critical challenges is how do we make sure that the machines we ‘train’ don’t perpetuate and amplify the same human biases that plague society? How can we best initiate a broader, in-depth discussion about how society will co-evolve with this technology, and connect computer science and social sciences to develop intelligent machines that are not only ‘smart,’ but also socially responsible?”

What makes this new initiative different and necessary is that it’s aimed at transcending barriers and breaking down silos among disciplines. As founding academic institutions, the Media Lab and Berkman Klein Center, along with other potential collaborators from the public and private sectors, will act as a mechanism to reinforce cross-disciplinary work and encourage intersectional peer dialogue and collaboration.

The fund — projected to operate with a phased approach over the next several years — will complement and collaborate with existing efforts and communities, such as the upcoming public symposium “AI Now,” which is scheduled for July 10 at the MIT Media Lab. The fund will also oversee an AI fellowship program, identify and provide support for collaborative projects, build networks out of the people and organizations currently working to steer AI in directions that help society, and also convene a “brain trust” of experts in the field.

A collaborative network

The Media Lab and the Berkman Klein Center for Internet and Society will leverage a network of faculty, fellows, staff, and affiliates who will collaborate on unbiased, sustained, evidenced-based, solution-oriented work that cuts across disciplines and sectors. This research will include questions that address society’s ethical expectations of AI, using machine learning to learn ethical and legal norms from data, and using data-driven techniques to quantify the potential impact of AI, for example, on the labor market.

Work of this nature is already being undertaken at both institutions. The Media Lab has been exploring some of the moral complexities associated with autonomous vehicles in the Scalable Cooperation group, led by Iyad Rahwan. And the Personal Robots group, led by Cynthia Breazeal, is investigating the ethics of human-robot interaction.

“AI could be as big a disruptor to the world as the Industrial Revolution was in the 18th and 19th centuries,” Rahwan says. He cites transportation systems and employment as among the areas that will likely be affected by automation and AI. “What we need is something that puts our entire society in the control loop of these systems … technologists, engineers, the public, ethicists, cognitive scientists, economists, legal scholars, anthropologists, faith leaders, government regulators — everyone crucial to protecting the public interest.”

“Artificial Intelligence provides the potential for deeply personalized learning experiences for people of all ages and stages,” says Breazeal, who emphasizes the need for AI to reach people in developing nations and underserved populations. But she adds that it is also “a kind of double-edged sword. What should it be learning and adapting to benefit you? And what should it do to protect your privacy and your security?”

Shared goals and governance

The Berkman Klein Center has been working to develop public interest-oriented solutions to many of the challenges of the digital age, incubating programs such as Creative Commons and the Digital Public Library of America. And, it is currently collaborating with the Media Lab on the “Assembly” program, which gathers high-level developers and tech industry professionals for a rigorous three-week course at Harvard University, followed by a 12-week collaborative development period to explore difficult problems in cybersecurity.

“The thread running through these otherwise disparate phenomena is a shift of reasoning and judgment away from people,” says Jonathan Zittrain, co-founder of the Berkman Klein Center and professor of law and computer science at Harvard University. “Sometimes that’s good, as it can free us up for other pursuits and for deeper undertakings. And sometimes it’s profoundly worrisome, as it decouples big decisions from human understanding and accountability. A lot of our work in this area will be to identify and cultivate technologies and practices that promote human autonomy and dignity rather than diminish it.”

The Ethics and Governance of Artificial Intelligence Fund will be governed by a small board, consisting of leadership from each participating foundation and institution. Ito says that convening all perspectives now is essential. “Instead of setting up an institution, we’ve decided to create a dynamic network. There are a number of areas where the deployment of machine learning is already starting to pose questions that are best answered in an interdisciplinary way. This project that we’re all embarking on is just the beginning.”

You might also enjoy the following articles about AI and AI/robotics policy:

• How artificial intelligence is changing our Christmas shop
• White House report: Artificial intelligence, automation, and the economy
• White House launches public workshops on AI issues
• White House releases reports focusing on opportunities and challenges of AI
• White House economic report looks to robotics for the future
• We the Geeks: Whitehouse hangouts celebrate robots, maker culture and more

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

A robot hands a medication bottle to a person. Photo credit: Keith Bujak. Source: Georgia Tech News Center

The Ethical, Legal and Social Aspects of Social Robots in Healthcare and Education Workshop (also called ELS Workshop) was held in Yokohama the 14^th Nov 2016 during the JSAI-isAI Conference. The workshop was twinned with another workshop in the New Friends Conference in Barcelona the 2^nd Nov 2016.

The workshop aimed at addressing the concerns that the use of social robots in therapy end education pose in the legal, ethical and societal (ELS) domain in a constructive and proactive manner. The workshop undertook ELS challenges that arise from the introduction of robots in therapy and education. A booklet is available to anyone who wishes to find out more, and can be found here.

The ELS Workshop first dealt with aspects of human dignity and the questions of how social robots might endanger an individual’s dignity, for example, by implying an overly simplistic model of human agency or by discriminating against certain demographics. Secondly, the workshop revolved around the topic of privacy. Third, the topic of liability was addressed.

These three overarching ELS issues – dignity, privacy, and liability – were discussed in an open discussion workshop format. The workshop focussed on defining the benefits, conflicts, and possible solutions to the conflicts that such new technologies pose. Researchers from all disciplines, as well as practitioners, actively engaged in the discussions – in particular, those participants who submitted an abstract for the workshop.

Here follows a summery of the issues raised by, and findings of the workshop.

Challenges and issues of social robots in therapy and education

• Public participation: Users are not taken into account during the design process of the robot and that is why “user-centered” design is, at the current stage, not a true statement.
• Public awareness: Connected with the user-centered idea, citizens opinion is not taken into account during the policy-making process. At the same time, however, it seems that people do not read the government website and are not very engaged with the government. Some participants mentioned that in Korea individual privacy is not perceived to have much value and that the government does not promote public awareness concerning it.
• Public security: It was explained in the workshop that in Korea there are lots of security issues in the country, and a case concerning a data leak was explained. If the proliferation of robots in the marketplace is to increase, it will be crucial to address all the security issues, especially if elderly and disabled people are involved.
• Privacy postmortem: Should informed consent include privacy after the death of the person?
• Legal management: The case of the Henna Hotel was introduced as an example of how robotic technology is already in the market. While the industry has already taken steps towards the introduction of robotic technology to the market, there has been no legal accompaniment, especially in the case of educational and therapeutic robots.
• Standards: Due to the lack of this legal management, it is not very clear what standards apply to educational and therapeutic robots. They simply do not exist in the industry.
• Legal uncertainty: Unless there is a problem, it seems that the Law is not providing any pro-active measure towards how to ensure a correct design/use of robot technology.
• Immigrant discrimination: Japan is investing in robotic technology instead of hiring foreigners due to language difficulties as well as cultural differences. Elderly prefer to be treated by Japanese caregivers or by robot technology rather than by immigrants.
• Employment of robot technology: The immigrant discriminatory scenario is aggravated by the low wages Japanese caregivers receive.
• Dehumanizing practice: In Japan, it is normally the daughter or the daughter-in-law that takes care of the mother/grandmother. Dehumanizing the practice of care could bring more free time to the caregiver (that is in this case a relative) but could bring about the decrease of human-human interaction from the care-receiver side.
• Multiple legislations: There is an urgent need to address the ELS issues if this robotic technology is going to be shipped all over the world. Compliance with each national legislation will bring about many conflicts that challenge the insertion of these robots to the market.
• Multiple robots: Each robot is different and the context where they are inserted too. Therefore, it is difficult to standardize case-by-case scenarios.
• Substitution of humans: Some of the participants wondered why humans need to surrender to the robot taking-over scenario. and who is in charge of deciding this. This brought about the following issue: technology is not people-driven.
• Robot obligations: If academia is talking about the possibility of providing robot rights, this highlights the importance of also deciding obligations.
• Unexpected consequences: Some of the robots that are entering the market have not been tested in real contexts and unexpected consequences can easily arise.
• Agency: connected with the unexpected consequences, it is not very clear whether the agency comes from the context, or if the agency comes from the designers. Who is entitled to decide the agency of their robot?
• New agency: The literature has been comparing robots to animals, corporations and other things… should the robot be considered a new kind of agent?
• Religion: Agency is perceived differently depending on the religion. Some comments on how inanimate things have spirit were highlighted, as well as how Hinduism also welcomes low-agency objects to be important, e.g. a book cannot be touched by the feet. What is not clear is whether this is a problem of religion or the individual.
• Human behavior: An experiment in Japan found resulted in children beating a robot in a mall. This experiment was explained in the workshop in order to avoid the replica of the behavior in the human-robot interaction.
• Education: Not a lot of schools have access to robotic technology, although it seems to be important to educate the population on the correct use of robotics technology.
• New law: do robots challenge the legal system in a different manner than other agents do?

Addressing the Challenges / Finding Solutions

• Legal certainty: it is important to have knowledge about domestic laws. A concept similar to “regulation by design” should be implemented.
• Regulatory prohibition: Depending on the use of robots we should ban the producing of robots to avoid unfortunate scenarios. The majority agreed that military robots should be banned.
• Public participation: the creation of a random jury, as we have in the legal system, could help on the participation of the general public on the decision-making process, as well as on the design of the robot technology. Some information and materials could be given to the citizens and they could provide their feedback.
• Employment of technology: increasing the salary of the workers could make the caregiver profession more attractive and increase efficiency. Working less hours can also increase efficiency although it is not clear how it could be modeled with care professions.
• Unexpected consequences: If we could predict and model an agent’s behavior then we could avoid unexpected consequences. Building robot strategies within the robot itself could also be of help.
• Education: it seems very difficult to know how to teach creativity in Japan, where the system is based on memorization. Helping this could help the acceptance of robotic technology as well as promoting the active participation on the design process of the robots.
• Transparency: The robot should be able to show or explain how it is using personal data, in order to promote transparency and user awareness. The use of black boxes could provide more information on the robot usage. It is not clear, however, what extent the government should have access to it.
• Regulated-by-design: This hybrid regulatory model should be embedded into the system to avoid any violations with domestic and international laws.
• Pro-activity: The system of Tokku in Japan should be spread all over the world in order to have Testing Zones for regulatory purposes.
• Regulatory model: we need a hybrid model that can cope with robotic technologies as well as with other technologies, as the delivery of legal counseling is changing rapidly – from having divorce apps to chatbots that deal with parking tickets.
• Regulatory model: connected to this idea, each project that involves robotic technology could be designed in a way that knowledge can be extracted from it for policy purposes.
• Case-by-case: the regulatory model should be created in a way that can cope with the particular needs of every robot and every context where this is inserted, as not all the robots possess the same degree of intelligence or the same capabilities.
• Robot Agency: the creation of an agency that deals with robots as it occurs in Japan could help having better management of all the ELS issues.
• Open international discussions: there is the need to open the debate to more people and to more countries.
• Replacing humans: robots should be conceived as helpers of the humans not a replacement of them.
• Clarity on the capabilities classification could bring about intelligibility on what is/should be permitted and what should not.
• This clarity relates also to the vision and expectations of the different countries on robotic technology, e.g. international future robotic trends.
• Decision-making of the robot: in the case of an accident, everyone could be assigned different values/importance in order the robot to choose the best target. This could raise discrimination issues at the same time.
• Harmonization: There is the need to harmonize what impact means, what is risk, and what are the legal issues concerning this type of robotic technology. There is also the need to have a common understanding.
• Alternative solutions: if the technology allows, the robot should be privacy friendly using other technologies, e.g. removing the cameras in the case of drones; or using vibrations of the floor to know that the patient has fallen.
• Privacy: a system similar to the noise that cameras in Korea make when they take a picture could be adopted in order to spread awareness among the users. The problem with this solution is that there are available apps that allow users to take a picture without making noise.
• Ethics could be addressed by common sense rules, although common sense varies between countries and individuals.
• The creation of a data bank for scientific innovations could help avoiding the non authorized uses of data.
• An international expert committee could be created to draft group reports

Main Issues (Recurring Topics)

• Regulatory model: There are no clear rules on the use of robotic technology in therapy and education. Law is not pragmatic.
• All the parties want to avoid responsibility of harm occurrence
• Growing need of the importance of ELS issues when the human-robot interaction involves vulnerable parts of the society.
• Solutions: each solution can bring about negative consequences. Therefore, an analysis of all the pros and cons will have to be made in order to mitigate any risk posed by the solutions
• The future of work (involving the promotions of co-working spaces)
• Third uses of data (involving privacy post mortem)
• The need for an international set of rules and basic principles.
• Robots will change the way we conceive this world.

Future Steps

• The legal system needs to
  • be disrupted
  • be pro-active
  • be dynamic
  • be more accessible
• There is the need to run more ELS workshops in different countries with different target participants: industry, laymen, legal scholars, etc.
• A great effort has to be done in order to change how people learn as everything is converging in this world. The term people in this case involves any person from any age.
• There is the need to share the knowledge on robotic technology among legal scholars.
• A clear classification on the capabilities of robots (educational, therapeutic) could be of help.
• There is the need to speak with main organizations, e.g. WTO, about this topic.
• We do need an international legal framework that can cope with all these ELS issues in the use of robotic technology, especially if they work with vulnerable parts of the society.
• There should be placed the idea that robotic technology are a supplement of the human’s work

2016 was a busy and abundant year for seed, crowd, series A,B,C,D and VC funding of robotics-related startups. 128 companies got funded, some multiple times. $1.95 billion, 50% more than 2015 which was also a phenomenal year with over $1.32 billion  funded.

Velodyne LiDAR got the most money in 2016, $150 million, with Zymergen and UBTech also getting over $100 million each.

In monthly recaps by The Robot Report, fundings grew until they peaked in August and then dipped in December. August was the month when Velodyne got their $150 million and Quanergy got $90 million. The next highest in August was $35 million for FormLabs.

The colorful chart on the left recaps the number of companies receiving funding by application area. 25 unmanned aerial systems companies got the biggest number of fundings followed by 15 agricultural robotics startups, service robotics for businesses, service robots for personal use, vision systems providers, self-driving systems and mobile robotics and AGVs companies, plus a whole bunch of smaller categories. Investments in robotic solutions for the ag industry were noted by Rob Leclerc of AgFunder who said:

“The number of deals grew 7% year-over-year, as we recorded 307 deals this half compared to 286 during the first half of 2015. The number of investors coming into the sector climbed 52% from 280 in the first half of 2015 compared with 425 in the first half of 2016, which suggests that investors are getting more comfortable with the sector.”

The list of 128 companies funded in 2016

The following companies were also funded but didn’t provide details: Raptor Maps, OnFarm, Appolo Shield, Aarav Unmanned Systems, AIO Robotics, Aloi Material Handling, Aurora Flight Sciences, Kimera Systems, NVBots, OPS-Ingersoll, OptoForce and Square Robot.

If you have information about robotics-related fundings, or corrections or additions to that which is reported above, please email it to info@therobotreport.com.

• Jibo look-alike appears on Chinese websites
• Robots may save the global economy
• US auto industry installed 135,000 robots and added 230,000 jobs
• What coachmen from the 1920s can tell us about robots and jobs
• Stop talking about bringing jobs back from China
• ‘Robots or jobs’ is key focus of #ERF2015
• Obama connects robots to jobs in State of the Union address

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

For all of us who participated in the Jibo IndieGoGo crowdfunding and are anxiously awaiting delivery – which has been delayed again – this Chinese knock-off came as quite a surprise.

This little Jibo look-alike appears under the brand name Oding with a corresponding website (www.odingbot.com) and a parent company, EDGbot.com. Both companies are registered under a third: HiChina Zhicheng Technology. The odingbot.com website provides a lot of information about their product which is strikingly similar to the content on Jibo’s website

HiChina is registered as a wholesale supplier of electronics, apparel, home & garden, and gifts to customers in China.

Neither of the three sites indicate where to purchase a new Oding nor when they will become available or at what price.

Knock-offs and intellectual property theft

The Oding bot looks a lot like the original Jibo prototype which had a button up front and a slightly wider base than Jibo’s present iteration yet, in an email from Cynthia Breazeal, she affirmed that Odingbot has absolutely no connection with Jibo.

A few years ago Bloomberg Businessweek magazine ran this cover along with a series of stories about stolen technology often copied from plans sent to Chinese contract manufacturers. Those stories appear to be just as true today.

No one should really be surprised. Asian versions of Amazon’s Echo and Google’s Home have been launched and are already being sold in China. Now there’s a Jibo knock-off.

Jibo, the inventor and leader of the pack, just needs to ship. They obviously underestimated what it would take. The cancellation of their developer edition in November is particularly troublesome because no new date was offered. And regarding the consumer edition, for which I am in line waiting, who knows when those will ship.

As Tandy Trower of Hoaloha Robotics, the developer of software and services to support new “socially assistive” robot designs to help people manage day-to-day activities restricted by disease, injury, or aging, said: “This is why I don’t show my robot before its ready.”

Click here to visit The Robot Report.

If you enjoyed this article about robots and jobs, you may also want to read:

• Robots may save the global economy
• US auto industry installed 135,000 robots and added 230,000 jobs
• What coachmen from the 1920s can tell us about robots and jobs
• Stop talking about bringing jobs back from China
• ‘Robots or jobs’ is key focus of #ERF2015
• Obama connects robots to jobs in State of the Union address

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

Having been involved in roboethics for some years, I was delighted when the IEEE launched its initiative on Ethical Considerations in AI and Autonomous Systems, early this year. Especially so because of the reach and traction that the IEEE has internationally. (Up until now most ethics initiatives have been national efforts – with the notable exception of the 2006 EURON roboethics roadmap.)

Even better, this is an initiative of the IEEE standards association – the very same that gave the world Wi-Fi (aka IEEE 802.11) 19 years ago. So when I was asked to get involved I jumped at the chance and became co-chair of the General Principles committee. I found myself in good company; many great people I knew but more I did not, and it was a real pleasure when we met face to face in The Hague at the end of August.

Most of our meetings were conducted by phone and it was a very demanding timetable. From nothing to our first publication: Ethically Aligned Design a few days ago is a remarkable achievement, which I think wouldn’t have happened without the extraordinary energy and enthusiasm of the initiative’s executive director John Havens.

I’m not going to describe what’s in that document here; instead I hope you will read it – and return comments. This document is not set in stone, it is – in the best traditions of the RFCs which defined the Internet – a Request for Input

But there are a couple of aspects I will highlight. Like its modest but influential predecessor, the EPSRC/AHRC principles of robotics, the IEEE initiative is hugely multi-disciplinary. It draws heavily from industry and academia, and includes philosophers, ethicists, lawyers, social scientists – as well as engineers and computer scientists – and significantly a number of diplomats and representatives from transnational bodies like the United Nations, US state department and the WEF. This is so important – if the work of this initiative is to make a difference it will need influential advocates. Equally important is that this is not a group dominated by old white men. There are plenty of those for sure, but I reckoned 40% women (should be 50% though!) and plenty of post-docs and PhD students too.

Equally important, the work is open. The publications are released under the creative commons licence. Likewise active membership is open. If you care about the issues and think you could contribute to one or more of the committees – or even if you think there’s a whole area of concern missing that needs to a new committee – get in touch!

Source: Uber

Uber is testing its self-proclaimed “self-driving” vehicles on California roads without complying with the testing requirements of California’s automated driving law. California’s Department of Motor Vehicles says that Uber is breaking that law; Uber says it’s not. The DMV is correct.

Uber’s argument is textually plausible but contextually untenable. It exploits a drafting problem that I highlighted first when Nevada was developing its automated driving regulations and again when California enacted a statute modeled on those regulations. California’s statute defines “autonomous technology” as “technology that has the capability to drive a vehicle without the active physical control or monitoring by a human operator”—and yet the purpose of testing such a technology is to develop or demonstrate that consistent capability. Indeed, the testing provisions of the statute even require a human to actively monitor a vehicle that, by definition, doesn’t need active human monitoring.

This linguistic loophole notwithstanding, the testing requirements of California’s law were intended to apply to aspirationally automated driving. If not, then those requirements would not reach any vehicle being tested when the law was enacted, any vehicle being tested today, or any “test” vehicle whatsoever.

Uber understandably analogizes its activities to the deployment of Tesla’s Autopilot. In some ways, the two are similar: In both cases, a human driver is (supposed to be) closely supervising the performance of the driving automation system and intervening when appropriate, and in both cases the developer is collecting data to further develop its system with a view toward a higher level of automation.

In other ways, however, Uber and Tesla diverge. Uber calls its vehicles self-driving; Tesla does not. Uber’s test vehicles are on roads for the express purpose of developing and demonstrating its technologies; Tesla’s production vehicles are on roads principally because their occupants want to go somewhere.

The line between testing and deployment is blurry—and will only become more so as over-the-air updates and pilot projects become more common. Nonetheless, Uber’s activities are comfortably (or, for Uber, uncomfortably) on the side of testing.

If I were advising Uber, I’d ask what its end game is and how its current posture in California advances that end game. Is it trying to create facts on the ground to which law may eventually conform? Establish a legal foundation for an argument about the legality in California of remotely monitored vehicles? Signal once again that it won’t be bound by conventional norms (even legal norms)?

Uber has noted that “real world testing on public roads is essential … to gain public trust,” and yet California’s law was in large part about building this trust—in the technologies and their developers and among regulators and the public they serve. The statutory and regulatory testing provisions are far from perfect, but the registration and reporting requirements that Uber has eschewed seek the transparency through which trust can be earned. In contrast, Uber’s current posture is not building trust in its technologies, its practices, or its philosophy.

That posture is also antagonizing California’s DMV and Office of the Attorney General. The state’s lawyers may well ask a judge for an injunction ordering Uber and its employees to stop their testing in the state. If a judge were to issue a temporary or permanent injunction, then Uber and any employees to whom the injunction applied would be in held in contempt if they nonetheless continued to test. That could subject them to fines, jail, and a misdemeanor charge.

Remedies beyond an injunction may also be available to the state (and to others with an interest in Uber’s activities). The DMV, Highway Patrol, and other state agencies could conceivably consider how a variety of statutory provisions apply (beyond those specific to automated driving), including revocation of vehicle registration, the obligation to comply with a peace officer’s lawful order, the prohibition against reckless driving, the prohibition against violating any provision of the vehicle code (including the automated driving provisions), the prohibition against causing unlawful operation, the prohibition against driving with certain video displays active, and even the (dubious) penal code prohibition against conspiring to “commit any act injurious to the public health, to public morals, or to pervert or obstruct justice, or the due administration of the laws.”

(A side note: Violations of the vehicle code are punishable in part through driving license points, and the testing regulations at issue here prohibit test drivers with more than one point on their license.)

California directs that provisions of its penal code are to be “construed according to the fair import of their terms, with a view to effect its objects and to promote justice.” If the same construction is applied to the vehicle code, then some of the above statutory provisions may be a poor fit for Uber’s conduct—but then so too is Uber’s strictly textual argument.

A law is often susceptible to a range of reasonable interpretations. Existing law in most states, for example, is probably consistent with many forms of automated driving—but the legal interpretations on which that conclusion rests can depend as much on the public perception of automated driving as they do on the specific language of the relevant laws. (Even so, Michigan in particular should take note here of the pitfalls of muddled laws.) Here, too, trust matters.

Against this flexible background, laws specific to automated driving can actually be more restrictive than permissive. This is certainly true in California, and although I’ve criticized the regulations that unduly limit automated driving testing, that is the state’s prerogative. Uber does not need to test in California, but if it does, it must follow California’s laws.

In April 2016, British Standards Institution (BSI) published the world’s first ethical standard for the design, production, sale, and usage of social robots. “BS 8611: 2016 Robots and robotic devices” gives guidelines to identify ethical hazards and reduce associated risks to acceptable levels. BS 8611 could be crucial in determining the nature of emerging human-robot relationships. However, the publication also raises many new questions. Are there any conflicts with ISO 13482 when incorporating BS 8611 into the design of safer robots? Should we consider regulating robot ethics? If so, how?

In this interview, we invite Prof. Joanna Bryson to share her insights on how to regulate future human-robot relationships.

Date: December 9^th 2016.

Interviewee: Prof. Joanna J. Bryson, Reader at Department of Computer Science, University of Bath, and affiliate of the Center for Information Technology and Policy, Princeton University.

Interviewer: Dr. Yueh-Hsuan Weng, Co-founder of ROBOLAW.ASIA and Fellow at CLAST AI & Law Research Committee (Beijing) and Tech and Law Center (Milan).

Prof. Joanna Bryson. Image: University of Bath

Q1 WENG: Thank you for agreeing to an interview with us. Can you tell us a little about your background?

BRYSON: My primary academic interest is comparative intelligence.  I was particularly interested in animal behavior as a student, and took a liberal arts degree from Chicago with a major in the Behavioural Sciences.  I now have two degrees in Artificial Intelligence (including my PhD at MIT) and another masters degree in Psychology (both masters are from Edinburgh.)  I’m currently an Associate Professor at Bath where I founded the Intelligent Systems group, and organized Artificial Models of Natural Intelligence, which is basically my own laboratory.

During my PhD I was working on a humanoid robot project and was astonished that people thought they had moral obligations to the robot, even though it did not function at all.  It was just a bunch of motors welded together in the shape of a human.  I began publishing about this problem, thinking that people didn’t know enough about AI, but in recent years I’ve realised they don’t understand enough about ethics.  They are confused about what is really the root of ethical obligation, and they do not understand the nature of artefacts.  They do not understand that we can construct both AI and ethics so that we either are or aren’t obliged to AI.  I have become well-known for arguing that we are ethically obliged to construct AI we are not ethically obliged to.

 Q2 WENG: What is the meaning of “ethical hazards”? Except deception and anthropomorphism, what are other ethical hazards in BS 8611: 2016?

BRYSON: I have to admit I haven’t memorised that document and don’t have time to go back to read it now.  Maybe you are talking about a moral hazard?  A moral hazard is something likely to make you do the wrong thing.  In particular, I have argued that attributing ethical status to AI is a moral hazard, because it allows us to escape the responsibilities we ourselves have as the manufacturers, builders and operators of AI.  This is particularly obvious in the case of military weapons – we have enough trouble with distributing blame between soldiers, their commanders, and the government that commissions them.  Making weapons themselves “sinks” for guilt is not honest or beneficial.  But there are also moral hazards associated with owning “friends” and “lovers” – if children relate mostly with things they own, how will they grow to be able to be equal partners with their spouse or coworkers, or members of a democracy?

Q3 WENG: Some members from Bristol Robotics Laboratory are particularly interested in “embedded intelligence”? Why is this concept important? Does it have any impact on BS 8611: 2016?

BRYSON:  I’m sorry, I don’t think it matters particularly whether AI is embedded in an artefact or “channelled” through the cloud, except obviously there are privacy issues when we connect AI onto the Internet or into any shared digital memory.  In fact, one of the ways I recommend ensuring that we have no ethical obligation to robots or other AI is to ensure there is never a unique copy: that it should be backed up to remote sites at all times.

Q4 WENG: Prof. John Holland said, “..an internal model allows a system to look ahead to the future consequences of current actions, without actually committing itself to those actions”. Can you tell us about the concept of “internal model”? Does a passive dynamic robot have its own internal model as well?

BRYSON:  Ha, that’s a great question!  The idea of an internal model is just a representation that allows, as Prof Holland says, a means for planning around and anticipating the world.  Rodney Brooks famously recommended that AI should not have a model, should use the world as it’s own best model, but in fact that is not practical. There’s not enough information in a sensory signal to disambiguate a world sufficiently to create animal-like intelligence, we need prior expectations to interpret the sensory input.  Still, Brooks was a genius of AI and the field moved forward much faster when we realised we could use minimalist representations; just those necessary for action.  We realised that humans “flush” our memory quickly if sensory information contradicts it, and operate on partial knowledge all the time.  Previously we though AI had to have perfect knowledge so it could make perfect plans, but now we understand that that isn’t tractable.

So does a passive dynamic have an internal model? No.  It is a model of an animal walking system, but to some extent it embodies expectations about for example gravity, but by and large it can do no planning or control. It arrives only where it goes and never knows that it has gotten there or been anywhere else, so certainly cannot anticipate anything.  But its design anticipates particular regularities about the world, so its designers had models that are partly embodied in the walker.

Q5 WENG: I conducted an empirical case study on humanoid robots and regulation with researchers from Waseda University. We found that inside “Tokku” Special Zones there were strong demands for regulating robotics in road traffic, privacy, tele-communication, safety, tax, highway traffic, etc… However, it is not clear that whether robot ethics should be regulated. Do you have any comments on this?

BRYSON: Unfortunately I also haven’t had time to read your study yet, but I can talk a bit about regulation.  There are two important things to recognise.  First, government should not be thought of as an imposition, particularly in a democracy, but even in an autocracy government only functions to the extent it is tolerated.  Government and regulation is the way we citizens coordinate our desires and vision.  Many people in technology seem to think of regulation only as limits on their freedom, and indeed even the word compromise can be used in both a negative and a positive way.  But technologists need to understand that when people come together and work together, they often create something of mutual benefit, public goods that are greater than the summed contribution of those who are cooperating.  Regulations can include those that mandate investment of government resources (our taxes) in innovation.  They can be liberating as well as constraining. Coming to agreements with fellow citizens about essential matters like privacy and the distribution of wealth that AI has huge impact on is essential, and yes, agreements require compromise, but it is through these that we create peace, stability, and affluence.

Having said all that, what I get from surveys of citizens is not information about what regulations would really work.  Governance and regulation are extremely complex and have been studied for centuries; it is as crazy to suggest that ordinary citizens construct regulations as that they construct nuclear weapons or that random people be drafted to represent a country in the World Cup or the Olympics.  We need to respect expertise as well as the desires of ordinary citizens.  What the fears and goals of citizens tell us is how easy or hard it would be to implement particular regulations, how likely they are to be accepted and respected.  Most people on the planet believe that their ancestors still have concerns with their own behaviour, but I wouldn’t want to build policy relying on dead ancestors as actors.  But it does make sense to know people think this if you are governing.

 Q6 WENG: What’s your definition of “ethical robots”? Should we teach them right from wrong?

BRYSON:  I think it makes more sense to program than to teach. We have enough trouble teaching citizens and governments to do good.  Most actions have ethical consequences and there is no problem in building AI to recognise and reason about these, but I would never recommend holding the AI as morally responsible, even though I think AI will necessarily take actions with moral consequences.  As the British EPSRC Principles of Robotics make clear, the responsibility for commercial products lies either in their manufacturer (if they fail to meet an advertised specification) or in their owner / operator (if they are improperly used.)  There are ethical implementations of AI, for example protecting the privacy of the individual, or limiting the damage an individual can do with an autonomous vehicle, but the AI itself should not be designated the responsible party in any circumstance.

 Q7 WENG: Do you believe that Asimov’s Three Laws of Robotics will be guiding principles to ensure the safety and security of human-robot interactions?

BRYSON:  Asimov’s laws certainly impact how people think about AI ethics; the first three (of five) Principles of Robotics reflect (and correct) Asimov’s laws.  But we are working hard to make people understand that these laws are by no means necessary or sufficient for AI ethics.  First of all, Asimov’s laws are intractable. It’s computationally impossible to implement them as described in the books, we can never know all the consequences of our actions.  Second, the books themselves show repeatedly that the laws are insufficient to create truly moral behaviour, that’s the fun of the short stories.  And third, Asimov like most science fiction writers uses AI as a stand-in for humans, for examining what it is to be human. The laws describe the robot as the responsible party, and that’s as I said earlier a mistake.

 Q8 WENG: Do you agree that robots should not be legal persons? Why?

BRYSON:  I don’t know for sure whether people shall make them legal persons, but I am certain that they should not, and in the long run will not be.  A legal person is something that is subject to human justice, and I do not believe it is possible in a well-designed system to create the kind of suffering that is intrinsic to human punishment.  There is no necessity for robots or AI to know shame, pain, regret for the passage of time.  These things are completely intrinsic to being human, we do not consider people with deficits in these concerns to be mentally healthy.  This is because we have evolved to be social organisms and there is no way to extract these properties from our intelligence.  But well-designed AI is modular, anything that is deliberately added by a designer can be removed.  So that’s one reason it would be incoherent for AI to be a legal person.  Another reason is that there is no discrete unit of AI or robots like there is for humans.  We know what it is to be a human being, though we are sometimes a little challenged by people who are brain dead or conjoined twins.  But AI cannot be neatly counted like humans, and therefore the idea of taxing a robot like a person is ludicrous.  And, again, it is a moral hazard.  The companies who decide to employ robotics should be the ones who have legal and economic liability for whatever their robots do.  We should not let them wiggle out of their responsibility because of our love for science fiction or our deep desire to construct perfect, obedient, cyber “children”.

Q9 WENG: What is the definition of “well-designed robots” in your opinion?

BRYSON: A robot is well-designed if the company that produces it is willing to take full responsibility for its actions, and further if governments are willing to license it as safe for human society.  That second requirement is necessary because some corporations have more resources than many countries, so may be willing to accept risks that individual citizens would prefer not to be subject to.

Q10 WENG: Finally, what will be the biggest challenge to developing a human-robot co-existence Society in the following decade?

BRYSON:  We still have many challenges.  I think we will solve dexterity and physical safety before we solve cyber security.  I don’t think we have a full grasp yet about what impact we are already having on human society and human autonomy.  It’s difficult to measure this because so many things are changing at once; we also have the problems of climate change, of massive increases in human numbers and access to communication.  We are currently in a period of grotesque income inequality and political polarisation – some people blame these on AI, but in fact this happened only a century ago, and probably contributed to the two world wars and the great depression. So getting on top of understanding how we are influencing each other and compromising our health with our planets’ is essential to answering this question.

If you enjoyed this article, you may also also enjoy:

• One being for two origins: A new perspective on roboethics
• Considering robot care, ethics & future tech at #ERW2016 European Robotics Week
• On the ethics of research in robotics, with Raja Chatila
• A European perspective on robot law: Interview with Mady Delvaux-Stehres
• Open Roboethics initiative delivers statement to United Nations CCW

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

The European Robotics Week in 2016 took place from 18 to 22 November in several countries including The Netherlands, Austria, Lithuania, Norway, Portugal, Serbia and many more. This event has been occurring since 2011 to spread public awareness of robotics applications and brings together industry, researchers and policymakers. The central event this year was held in Amsterdam. I attended one of the 5 days of activities in the Maritime Museum. The theme was ‘Robots at your service – empowering healthy aging’ which encompassed a variety of activities arranged over the 5 day duration, including debates, open sessions where you could network while interacting with different kinds of robots, workshops for children and a 2 day hackathon. I attended the robot expo and two of the debate sessions which I will summarise below.

Robot Exhibition

Credits: Visual Outcasts

Although it’s clearly still early days for general consumer robotics in terms of the price-value ratio, there are ever more options available for enthusiasts and for very specific applications. The exhibition had a good selection, including these lovely bots:

• Inmoov open source 3d printed robot
• KUKA Robotics human safe industrial robot with fancy torque sensors
• Tiago Pal, ROS-based mobile manipulator
• Paro the therapeutic seal robot
• Pleo the dinosaur pet
• Softbank’s Pepper and Nao robots, Pepper being hosted by Decos, which uses Pepper as a receptionist
• Meccano’s Meccanoid G15 robot
• Robokind’s Milo robot, for teaching children with autism

Panel Discussion: Roboethics

This discussion was about ethics in robotics but it touched a really wide variety of aspects around this, including some philosophy. The speakers were of a very high standard and from a wide variety of backgrounds which gave the discussion its great breadth.

Here are some highlights:

Robots in care: good or bad?

• How should robots in care evolve?

• Robots should be applied to care because the need for care increases as populations in developed countries age, while the labour force interested in care shrinks. But are robots really the answer to this sensitive problem?
• The distinction was made that a wide range of activities qualify as ‘care’, ranging from assisting people to care for themselves, to caring for them, to providing psychological and emotional support in times of depression, distress or loneliness. Are robots suitable for all of these kinds of activities or only a spectrum? Before you judge, consider this example given, of a Nao robot used in interacting with children with diabetes, in the PAL project (Personal Assistant for health Lifestyle). The robot interacts with the children to educate them about their illness and help them track it. The children confide in the robot more easily than adults and hospital attendance in children with diabetes goes up. This is an example of using robots to build a relationship and put people at ease – something that the robot does more easily in this case than a human. When is a robot more trustworthy than a human? When is the human touch really needed?
• Should we want robots to be more human when they are used for care? In some cases we do, when there is a need to soothe and connect, to comfort. But in other cases a more impersonal and less present robot might be desirable. For instance, if you needed help going to the toilet or rising from bed for the rest of your life, would you need a lot of human interaction around that, or prefer it to blend seamlessly into your life to enable you to be as independent as possible?

Robo ethics and liability.

• Responsibility vs liability of robots for their misdeeds

• This question always comes up in a discussion about robot ethics – people feel uncomfortable with the idea of a robot’s accountability for crimes. For instance, if an autonomous car runs over a pedestrian, who would be responsible for that, the car, owner or manufacturer?
• A good point was raised by philosophy professor Vincent Muller to take this argument further. If a child throws a stone through a window, she is responsible for the action but the parent is liable for damages. In the same way, a robot can be responsible for doing something wrong, but another entity, like the owner, might be liable for damages caused.
• When discussing that a robot can be held liable for a crime, we imply it can understand that it’s actions were wrong and did it anyway. But robots as yet have no understanding of what they are doing, thus the conclusion was that a robot cannot be meaningfully convicted of a crime.

Panel Discussion: Our Robotic Future

In this next session, a general discussion on the future of robotics ensued, followed by each speaker giving their wish and hope for this future. There was a strong Euro-centric flavour to this discussion, which gave a fascinating insight into the European search for identity in this time of change – in the robotics and AI revolution, who are we and what do we stand for? How will we respond to the threats and oppourtunities? How can we lead and usher in a good outcome? The panel itself fell on the optimistic side of the debate, looking forward to positive outcomes.

Robotics Reasearch.

• The debate started off underlining the need to share information and research so that we can progress quickly with these high potential technologies.
• A distinction was made between American and European methods of research in AI and robotics

• American research is funded by defence budgets and not shared openly
• In the US, research is done by large corporations like Facebook and Google, and is often funded by DARPA.
• In Europe, research is funded by the European Commission, and often is performed through startups which means less brute strength, but more agility to bring research to fruition. However because these startups are so small, they can be reluctant to share their intellectual property which might be their only business case.

Accepting the robot revolution

• There is a lot of hype about robots and AI in the media which stirs people’s imaginations and fears – how can we usher in all the benefits of the robot revolution?

• Part of the fear is that people don’t like to accept that the last distinction we have of being the smartest on Earth could be lost.
• There is a growing economic divide caused by this technological revolution
• We should not create a system that creates advantage for only a select group, but aim for an inclusive society that allows all to benefit from abundance of robotics.
• People can be less afraid of robots if the value they add is made clear, for example a robot surgeon which is more accurate than a human surgeon can be viewed positively instead of as a threat.
• Technological revolutions are fuelled by an available workforce which can pick up the skills needed to usher them in. For example, in the industrial revolution, in which agricultural workers could be retrained to work in factories, and later a large labour force was available to fuel the IT revolution which, again, would have put many out of work by automating manual tasks. There is a concern that with STEM graduates on the decline, we will lack the skilled resources to build momentum for AI and Robotics.
• Investors in robotics and AI are discouraged by the growing stigma around these technologies.
• Communication policies on this topic are designed around getting people to understand the science, but this thinking must shift. The end users who currently fear and lack understanding must become the center of communication in the future – AI and robotics must enable them and they should have the tools to judge and decide on it’s fate.

Conclusion

This is certainly an exciting time to be alive, as there is so much to still determine and discover in the growth of the AI and Robotics industries and disciplines. Such an event also highlights how far we are from this reality – 10 year horizons were discussed for AI and Robotics to become commodities in our homes. It is very encouraging to see the clear thinking and good intentions that go into making these technologies mainstream. In the coming months I’d like to dig into the topics of ethics, regulation, EU funding, and what the future of AI and robotics could bring.

Click here to visit Thosha Moodley’s blog.

If you enjoyed this article, you may also enjoy:

• Considering robot care, ethics & future tech at #ERW2016 European Robotics Week
• Survey: Evaluate ethics of health related privacy with care robots
• Care-O-bot 4 celebrates its premiere as a shopping assistant
• Robot companions are coming into our homes — how human should they be?

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

The concept One Being for Two Origins, from the teachings of Buddha, considers the unification of technology, nature, and human beings. It emerges from the theory of the three aspects: The values of the “Good”, the “Bad”, and “Muki” (Avyakata). The theory of One Being for Two Origins follows the theory of the three aspects and explains a mechanism for how values, such as the good and the bad, emerge in reality using the three aspects. The theory has a contradictory structure, hence it can be difficult for those with a scientific background to fully comprehend it. In this interview, Nagoya University’s Associate Professor Hiroko Kamide shares with us her insights on robot ethics, with a focus on One Being for Two Origins.

Date: December 5^th 2016

Interviewee: Prof. Hiroko Kamide, Designated Associate Professor, Nagoya University, Japan

Interviewer: Dr. Yueh-Hsuan Weng, Co-founder of ROBOLAW.ASIA and Fellow at CLAST AI & Law Research Committee (Beijing) and Tech and Law Center (Milan)

Q1 WENG: Thank you for agreeing to an interview with us. Can you tell us a little about yourself and your background?

KAMIDE: My background is social psychology. During my doctoral course, I studied close relationships and well-being. After receiving my PhD, I started researching human-robot interaction and collaborated with engineers to help realize so-called “human-friendly robots”. We established psychological methods to evaluate humanoids, and subsequently developed human-friendly behaviors for robots. I still collaborate with engineers to consider the adaptive way of introducing advanced technology, such as robots and self-driving cars, from an interdisciplinary approach.

 Q2 WENG: Currently you are also in charge of a special interest group on robots and philosophy at The Robotics of Japan (RSJ). Can you tell us something about your RSJ Special Interest Group on Philosophy in Robotics?

KAMIDE: In the RSJ group, we discuss the approach to  technology from an interdisciplinary standpoint. The members are from psychology, philosophy, law, sociology, and engineering. Psychological evaluation, like technological advancement, is important to realize robots that your average person can accept in their daily-life. This collaborative work between psychology and engineering is crucial to developing technologies that can satisfy a human’s needs and expectations. But sometimes we experience terrible events that we cannot control: nuclear accidents, wrongful use of robots, etc. Therefore we started to investigate what the appropriate approach is when implementing and dealing with the cutting-edge technology. It’s a the responsibility of researchers to convey information to ordinary users about how we should deal with technology in order to maintain harmony between technology, nature, and humans.

Q3 WENG: What is the theory One Being for Two Origins? Why is it important to the unification of humanity and nature?

KAMIDE: The theory of One Being for Two Origins has a contradictory structure, and it’s difficult for those with a scientific background to fully understand. At the first step, we begin with a theory of values—the theory of the three aspects—as an introduction to the theory of One Being for Two Origins. The theory of three aspects explains the mechanism of how values, such as good and bad, emerge from the three concepts of the good, the bad, and the “Muki”. Then the theory of One Being for Two Origins follows the theory of three aspects as the fundamental mechanism of order. Through the theory of One Being for Two Origins, we become able to recognize the truth and biases behind language, and as such can avoid conflicts between technology, nature and human-beings.

Q4 WENG: Your paper “One Being for Two Origins – A necessary awakening for the future of robotics” has been presented at the 2016 IEEE ARSO Workshop in Shanghai, China. The co-author is Professor Masahiro Mori, who is a world famous roboticist because of his “uncanny valley” theory. How come you decided to choose this topic? Does the concept One Being for Two Origins have any relationship with “uncanny valley”?

KAMIDE: His theory of uncanny valley has a very thoughtful and groundbreaking perspective, therefore many researchers in the world still refer to his theory. As for my impression, he developed a very clear sense of perspectives that leads us to understand the theory of uncanny valley. His method of observing the truth of the world is in line with the teachings of the Buddha.

Q5 WENG: Regarding regulation for robotics, you pointed out that there is widespread misunderstanding from regulators. What is the misunderstanding? What kind of legal crisis will materialize if they don’t change their way of thinking? How can your proposed theory help solve this legal crisis?

KAMIDE: As the theory of three aspects explains, the value of good and bad are just derived from Muki, that transcends such values and is a true quality of the technology. Useful technology is positive. On the other hand, it would be bad if technology harms people. Technology is fundamentally neither good nor bad. It’s the manner in which technology is applied and operated by humans that is either good or bad. It is humans themselves that attribute these aspects to the technology.

Regulations are attempts to eliminate the bad aspects of technology. However, it is not correct to assume that only the good remains when the bad is eliminated, because technologies themselves are the Muki. And if bad did not exist, good would not exist either, as one is parasitic on the other. To eliminate the bad is to interfere in the working of the Muki in producing these values. The same occurs robots.

Our mindset determines how we approaches the Muki. If we are not mentally prepared, Muki will transform into bad. If we are mentally prepared, we can treat the Muki as good. Bad can transform into good then, of course, good can also transform into bad. And the more good qualities a robot possesses, the stronger its bad qualities will be, following the transformation. Sharp scalpels save lives, but sharp daggers kill people. This is because the working of the Muki is powerful.

In all cases, the mindset of humans is crucial. So, the responsibility of us in robotics is to ensure that technology is not used in bad ways. We must both establish methods of using robots, and work to refine users’ mindsets.

Q6 WENG: Robotics is like a double-edged sword, it can either help or kill people. Robotic lethal weapons present a dilemma. Should they be banned or not? Do you have any comment on this?

KAMIDE: The problem is not in robotics but in ourselves—we who raise such dualistic questions. As I mentioned, the technology of robotics doesn’t have any values. Robotics is not double edged sword at all; our minds are double edged. People easily attribute these values to the physical entity but it is not true. We have to try and observe the fact that we see not what we see, but what we want to see.

Q7 WENG: Do you agree with using dualism as the meta-principle to teach robots right from wrong? If not, do you think it is possible to adopt your theory into creating ethical robots’?

KAMIDE: The “right” and “wrong” is always in the eye of the beholder, and there are no absolute standards. As long as humans accept that they are subject to subjective mechanisms of perceiving the world, it won’t be troublesome to develop ethical robots. But if humans see the “right” and “wrong” as existing independently, objective, it will be definitely problematic.

Q8 WENG: What are major challenges for implementing One Being for Two Origins into robot ethics?

KAMIDE: Clarifying the importance of applying wisdom, not just knowledge. In this theory, knowledge refers to the ability to divide the phenomena into detailed classifications in order to understand the difference between things. On the other hand, wisdom is necessary to discover the meaning in the different things. Knowledge is developed through education, usually, but wisdom is not taught explicitly. Therefore, we are accustomed to dualistic thinking based on knowledge, that divides the world into its constituent parts, but we have difficulty uniting the parts using wisdom, in a way of monism. This is one of reason that we often raise dualistic questions, and eventually develop conflicts.

Q9 WENG: Professor Shigeo Hirose gave an insight called “Morality Engineering” in his short review “A Dialog on Robots” Vol. 7, No. 4, JRSJ. Do you think that One Being for Two Origins might be helpful in implementing his proposal of “Morality Engineering”?

KAMIDE: His article is very interesting. Opinions from both sides who are optimistic or pessimistic to a robotized future society are quite realistic and thoughtful. Recently many philosophers have started to think about morality in robotics. His “Morality Engineering” suggests implementing “rational egoism”, and I agree with the importance of this approach. One Being for Two Origins is the methodology to transcendent and unify opposite aspects of the phenomena to see the truth. So it is helpful to consider how “rational egoism” truly functions in the interactions between humans and robots.

Click here to read Hiroko Kamide’s paper “One being for two origins — A necessary awakening for the future of robotics”.

If you enjoyed this article, you may also also enjoy:

• Considering robot care, ethics & future tech at #ERW2016 European Robotics Week
• On the ethics of research in robotics, with Raja Chatila
• A European perspective on robot law: Interview with Mady Delvaux-Stehres
• Open Roboethics initiative delivers statement to United Nations CCW

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

U.S. Sen. Ted Cruz (R-Texas), chairman of the Subcommittee on Space, Science, and Competitiveness will convene a hearing today, 30 November, at 2:30 p.m. EST on “The Dawn of Artificial Intelligence.” The hearing will conduct a broad overview of the state of artificial intelligence, including policy implications and effects on commerce.

On October 12, 2016, the White House Office of Science and Technology Policy released a report entitled, Preparing for the Future of Artificial Intelligence. The report outlined opportunities for artificial intelligence, including recommendations on how the technology can be used to advance social good and improve government operations. The report also proposed 23 policy recommendations and a companion strategic plan that identified priorities for federally-funded research and development in artificial intelligence.

At today’s Senate hearing, which will be livestreamed, five experts will be present:

• Dr. Eric Horvitz, Interim Co-Chair, Partnership on Artificial Intelligence; Managing Director, Microsoft Research Lab
• Dr. Andrew Moore, Dean, School of Computer Science, Carnegie Mellon University
• Dr. Andrew Futreal, Professor, Department of Genomic Medicine, University of Texas MD Anderson Cancer Center
• Mr. Greg Brockman, Cofounder and Chief Technology Officer, OpenAI
• Dr. Steve Chien, Senior Research Scientist, Autonomous Space Systems and Technical Group Supervisor, Artificial Intelligence Group, NASA Jet Propulsion Laboratory, California Institute of Technology

* Witness list subject to change

The common, and recurring, view of the latest breakthroughs in artificial intelligence research is that sentient and intelligent machines are just on the horizon. Machines understand verbal commands, distinguish pictures, drive cars and play games better than we do. How much longer can it be before they walk among us?

The new White House report on artificial intelligence takes an appropriately skeptical view of that dream. It says the next 20 years likely won’t see machines “exhibit broadly-applicable intelligence comparable to or exceeding that of humans,” though it does go on to say that in the coming years, “machines will reach and exceed human performance on more and more tasks.” But its assumptions about how those capabilities will develop missed some important points.

As an AI researcher, I’ll admit it was nice to have my own field highlighted at the highest level of American government, but the report focused almost exclusively on what I call “the boring kind of AI.” It dismissed in half a sentence my branch of AI research, into how evolution can help develop ever-improving AI systems, and how computational models can help us understand how our human intelligence evolved.

The report focuses on what might be called mainstream AI tools: machine learning and deep learning. These are the sorts of technologies that have been able to play “Jeopardy!” well, and beat human Go masters at the most complicated game ever invented. These current intelligent systems are able to handle huge amounts of data and make complex calculations very quickly. But they lack an element that will be key to building the sentient machines we picture having in the future.

We need to do more than teach machines to learn. We need to overcome the boundaries that define the four different types of artificial intelligence, the barriers that separate machines from us – and us from them.

Type I AI: Reactive machines

The most basic types of AI systems are purely reactive and have the ability neither to form memories nor to use past experiences to inform current decisions. Deep Blue, IBM’s chess-playing supercomputer, which beat international grandmaster Garry Kasparov in the late 1990s, is the perfect example of this type of machine.

Deep Blue can identify the pieces on a chess board and know how each moves. It can make predictions about what moves might be next for it and its opponent. And it can choose the most optimal moves from among the possibilities.

But it doesn’t have any concept of the past, nor any memory of what has happened before. Apart from a rarely used chess-specific rule against repeating the same move three times, Deep Blue ignores everything before the present moment. All it does is look at the pieces on the chess board as it stands right now, and choose from possible next moves.

This type of intelligence involves the computer perceiving the world directly and acting on what it sees. It doesn’t rely on an internal concept of the world. In a seminal paper, AI researcher Rodney Brooks argued that we should only build machines like this. His main reason was that people are not very good at programming accurate simulated worlds for computers to use, what is called in AI scholarship a “representation” of the world.

The current intelligent machines we marvel at either have no such concept of the world, or have a very limited and specialized one for its particular duties. The innovation in Deep Blue’s design was not to broaden the range of possible movies the computer considered. Rather, the developers found a way to narrow its view, to stop pursuing some potential future moves, based on how it rated their outcome. Without this ability, Deep Blue would have needed to be an even more powerful computer to actually beat Kasparov.

Similarly, Google’s AlphaGo, which has beaten top human Go experts, can’t evaluate all potential future moves either. Its analysis method is more sophisticated than Deep Blue’s, using a neural network to evaluate game developments.

These methods do improve the ability of AI systems to play specific games better, but they can’t be easily changed or applied to other situations. These computerized imaginations have no concept of the wider world – meaning they can’t function beyond the specific tasks they’re assigned and are easily fooled.

They can’t interactively participate in the world, the way we imagine AI systems one day might. Instead, these machines will behave exactly the same way every time they encounter the same situation. This can be very good for ensuring an AI system is trustworthy: You want your autonomous car to be a reliable driver. But it’s bad if we want machines to truly engage with, and respond to, the world. These simplest AI systems won’t ever be bored, or interested, or sad.

Type II AI: Limited memory

This Type II class contains machines can look into the past. Self-driving cars do some of this already. For example, they observe other cars’ speed and direction. That can’t be done in a just one moment, but rather requires identifying specific objects and monitoring them over time.

These observations are added to the self-driving cars’ preprogrammed representations of the world, which also include lane markings, traffic lights and other important elements, like curves in the road. They’re included when the car decides when to change lanes, to avoid cutting off another driver or being hit by a nearby car.

But these simple pieces of information about the past are only transient. They aren’t saved as part of the car’s library of experience it can learn from, the way human drivers compile experience over years behind the wheel.

So how can we build AI systems that build full representations, remember their experiences and learn how to handle new situations? Brooks was right in that it is very difficult to do this. My own research into methods inspired by Darwinian evolution can start to make up for human shortcomings by letting the machines build their own representations.

Type III AI: Theory of mind

We might stop here, and call this point the important divide between the machines we have and the machines we will build in the future. However, it is better to be more specific to discuss the types of representations machines need to form, and what they need to be about.

Machines in the next, more advanced, class not only form representations about the world, but also about other agents or entities in the world. In psychology, this is called “theory of mind” – the understanding that people, creatures and objects in the world can have thoughts and emotions that affect their own behavior.

This is crucial to how we humans formed societies because they allowed us to have social interactions. Without understanding each other’s motives and intentions, and without taking into account what somebody else knows either about me or the environment, working together is at best difficult, at worst impossible.

If AI systems are indeed ever to walk among us, they’ll have to be able to understand that each of us has thoughts and feelings and expectations for how we’ll be treated. And they’ll have to adjust their behavior accordingly.

Type IV AI: Self-awareness

The final step of AI development is to build systems that can form representations about themselves. Ultimately, we AI researchers will have to not only understand consciousness, but build machines that have it.

This is, in a sense, an extension of the “theory of mind” possessed by Type III artificial intelligences. Consciousness is also called “self-awareness” for a reason. (“I want that item” is a very different statement from “I know I want that item.”) Conscious beings are aware of themselves, know about their internal states, and are able to predict feelings of others. We assume someone honking behind us in traffic is angry or impatient, because that’s how we feel when we honk at others. Without a theory of mind, we could not make those sorts of inferences.

While we are probably far from creating machines that are self-aware, we should focus our efforts toward understanding memory, learning and the ability to base decisions on past experiences. This is an important step to understand human intelligence on its own. And it is crucial if we want to design or evolve machines that are more than exceptional at classifying what they see in front of them.

This article was originally published on The Conversation. Read the original article.

If you enjoyed this article, you may also want to read these articles about AI: 

• President Obama discusses artificial intelligence with Media Lab Director Joi Ito
• What artificial intelligence will look like in 2030
• Artificial intelligence expedites breast cancer risk prediction
• Robohub roundtable: Public perceptions of robotics and AI, what’s your pet peeve?
• Why watching Westworld’s robots should make us question ourselves

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.