At this year’s International Conference on Machine Learning (ICML2025), Jaeho Kim, Yunseok Lee and Seulki Lee won an outstanding position paper award for their work Position: The AI Conference Peer Review Crisis Demands Author Feedback and Reviewer Rewards. We hear from Jaeho about the problems they were trying to address, and their proposed author feedback mechanism and reviewer reward system.

Could you say something about the problem that you address in your position paper?

Our position paper addresses the problems plaguing current AI conference peer review systems, while also raising questions about the future direction of peer review.

The imminent problem with the current peer review system in AI conferences is the exponential growth in paper submissions driven by increasing interest in AI. To put this with numbers, NeurIPS received over 30,000 submissions this year, while ICLR saw a 59.8% increase in submissions in just one year. This huge increase in submissions has created a fundamental mismatch: while paper submissions grow exponentially, the pool of qualified reviewers has not kept pace.

Submissions to some of the major AI conferences over the past few years.

This imbalance has severe consequences. The majority of papers are no longer receiving adequate review quality, undermining peer review’s essential function as a gatekeeper of scientific knowledge. When the review process fails, inappropriate papers and flawed research can slip through, potentially polluting the scientific record.

Considering AI’s profound societal impact, this breakdown in quality control poses risks that extend far beyond academia. Poor research that enters the scientific discourse can mislead future work, influence policy decisions, and ultimately hinder genuine knowledge advancement. Our position paper focuses on this critical question and proposes methods on how we can enhance the quality of review, thus leading to better dissemination of knowledge.

What do you argue for in the position paper?

Our position paper proposes two major changes to tackle the current peer review crisis: an author feedback mechanism and a reviewer reward system.

First, the author feedback system enables authors to formally evaluate the quality of reviews they receive. This system allows authors to assess reviewers’ comprehension of their work, identify potential signs of LLM-generated content, and establish basic safeguards against unfair, biased, or superficial reviews. Importantly, this isn’t about penalizing reviewers, but rather creating minimal accountability to protect authors from the small minority of reviewers who may not meet professional standards.

Second, our reviewer incentive system provides both immediate and long-term professional value for quality reviewing. For short-term motivation, author evaluation scores determine eligibility for digital badges (such as “Top 10% Reviewer” recognition) that can be displayed on academic profiles like OpenReview and Google Scholar. For long-term career impact, we propose novel metrics like a “reviewer impact score” – essentially an h-index calculated from the subsequent citations of papers a reviewer has evaluated. This treats reviewers as contributors to the papers they help improve and validates their role in advancing scientific knowledge.

Could you tell us more about your proposal for this new two-way peer review method?

Our proposed two-way peer review system makes one key change to the current process: we split review release into two phases.

The authors’ proposed modification to the peer-review system.

Currently, authors submit papers, reviewers write complete reviews, and all reviews are released at once. In our system, authors first receive only the neutral sections – the summary, strengths, and questions about their paper. Authors then provide feedback on whether reviewers properly understood their work. Only after this feedback do we release the second part containing weaknesses and ratings.

This approach offers three main benefits. First, it’s practical – we don’t need to change existing timelines or review templates. The second phase can be released immediately after the authors give feedback. Second, it protects authors from irresponsible reviews since reviewers know their work will be evaluated. Third, since reviewers typically review multiple papers, we can track their feedback scores to help area chairs identify (ir)responsible reviewers.

The key insight is that authors know their own work best and can quickly spot when a reviewer hasn’t properly engaged with their paper.

Could you talk about the concrete reward system that you suggest in the paper?

We propose both short-term and long-term rewards to address reviewer motivation, which naturally declines over time despite starting enthusiastically.

Short-term: Digital badges displayed on reviewers’ academic profiles, awarded based on author feedback scores. The goal is making reviewer contributions more visible. While some conferences list top reviewers on their websites, these lists are hard to find. Our badges would be prominently displayed on profiles and could even be printed on conference name tags.
Example of a badge that could appear on profiles.

Long-term: Numerical metrics to quantify reviewer impact at AI conferences. We suggest tracking measures like an h-index for reviewed papers. These metrics could be included in academic portfolios, similar to how we currently track publication impact.

The core idea is creating tangible career benefits for reviewers while establishing peer review as a professional academic service that rewards both authors and reviewers.

What do you think could be some of the pros and cons of implementing this system?

The benefits of our system are threefold. First, it is a very practical solution. Our approach doesn’t change current review schedules or review burdens, making it easy to incorporate into existing systems. Second, it encourages reviewers to act more responsibly, knowing their work will be evaluated. We emphasize that most reviewers already act professionally – however, even a small number of irresponsible reviewers can seriously damage the peer review system. Third, with sufficient scale, author feedback scores will make conferences more sustainable. Area chairs will have better information about reviewer quality, enabling them to make more informed decisions about paper acceptance.

However, there is strong potential for gaming by reviewers. Reviewers might optimize for rewards by giving overly positive reviews. Measures to counteract these problems are definitely needed. We are currently exploring solutions to address this issue.

Are there any concluding thoughts you’d like to add about the potential future
of conferences and peer-review?

One emerging trend we’ve observed is the increasing discussion of LLMs in peer review. While we believe current LLMs have several weaknesses (e.g., prompt injection, shallow reviews), we also think they will eventually surpass humans. When that happens, we will face a fundamental dilemma: if LLMs provide better reviews, why should humans be reviewing? Just as the rapid rise of LLMs caught us unprepared and created chaos, we cannot afford a repeat. We should start preparing for this question as soon as possible.

About Jaeho

Jaeho Kim is a Postdoctoral Researcher at Korea University with Professor Changhee Lee. He received his Ph.D. from UNIST under the supervision of Professor Seulki Lee. His main research focuses on time series learning, particularly developing foundation models that generate synthetic and human-guided time series data to reduce computational and data costs. He also contributes to improving the peer review process at major AI conferences, with his work recognized by the ICML 2025 Outstanding Position Paper Award.

Read the work in full

Position: The AI Conference Peer Review Crisis Demands Author Feedback and Reviewer Rewards, Jaeho Kim, Yunseok Lee, Seulki Lee.

Copyright by Intrinsic.

Finally, Intrinsic (a spin-off of Google-X) has revealed the product they have been working with the help of the Open Source Robotics Corporation team (among others): Flowstate!

What is Flowstate?

Introducing Intrinsic Flowstate | Intrinsic (image copyright by Intrinsic)

Flowstate is a web-based software designed to simplify the creation of software applications for industrial robots. The application provides a user-friendly desktop environment where blocks can be combined to define the desired behavior of an industrial robot for specific tasks.

Good points

• Flowstate offers a range of features, including simulation testing, debugging tools, and seamless deployment to real robots.
• It is based on ROS, so we should be able to use our favorite framework and all the existing software to program on it, including Gazebo simulations.
• It has a behavior tree based system to graphically control the flow of the program, which simplifies the way to create programs by just moving blocks around. But it is also possible to switch to expert mode to manually touch the code.
• It has a library of already existing robot models and hardware ready to be added, but you can also add your own.
• Additionally, the application provides pre-built AI skills that can be utilized as modules to achieve complex AI results without the need for manual coding.
• One limitiation (but I actually consider a good point) is that the tool is thought for industrial robots not for service robots in general. This is good because it provides a focus for the product, specially for this initial release

Flowstate | Intrinsic (image copyright by Intrinsic)

Based on the official post and the keynote released on Monday, May 15, 2023 (available here), this is the information we have gathered so far. However, we currently lack a comprehensive understanding of how the software works, its complete feature set, and any potential limitations. To gain more insights, we must wait until July of this year, hoping that I will be among the lucky participants selected for the private beta (open call to the beta still available here).

Unclear points

Even if I find interesting the proposal of Intrinsic, I have identified three potential concerns regarding it:

1. Interoperability across different hardware and software platforms poses a challenge. The recruitment of the full OSRC team by Intrinsic appears to address this issue, given that ROS is currently the closest system in the market to achieve such interoperability. However, widespread adoption of ROS by industrial robot manufacturers is still limited, with only a few companies embracing it.

   Ensuring hardware interoperability necessitates the adoption of a common framework by robot manufacturers, which is currently a distant reality. What we, ROS developers, aim right now is to be able to have somebody build the ROS drivers for the robotic arm we want to use (like for example the manufacturers of the robot, or the team of ROS Industrial). However, manufacturers generally hesitate to develop ROS drivers due to potential business limitations and their aims for customer lock-in. Unless a platform dedicates substantial resources to developing and maintaining drivers for supported robots, the challenge of hardware interoperability cannot be solved by a platform alone (actually, that is one of the goals that ROS-Industrial is trying to achieve).

   Google possesses the potential to unite hardware companies towards this goal, as Wendy Tan White, the CEO of Intrinsic mentioned, “This is an ecosystem effort” However, it is crucial for the industrial community to perceive tangible benefits and value in supporting this initiative beyond merely assisting others in building their businesses. The specific benefits that the ecosystem stands to gain by supporting this initiative remain unclear.



Flowstate | Intrinsic (image copyright by Intrinsic)

2. The availability of pre-made AI skills for robots is a complex task. Consider the widely used skills in ROS, such as navigation or arm path planning, exemplified by Nav2 and MoveIt, which offer excellent functionality. However, integrating these skills into new robots is not as simple as plug-and-play. In fact, dedicated courses exist to teach users how to effectively utilize the different components of navigation within a robot. This highlights the challenges associated with implementing such skills for robots in general. Thus, it is reasonable to anticipate similar difficulties in developing pre-made skills within Flowstate.
3. A final point that I don’t see clear (because it was not addressed in the presentation) is how the company is going to do business with Flowstate. This is a very important point for every robotics developer because we don’t want to be locked into proprietary systems. We understand that companies must have a business, but we want to understand clearly what the business is so we can decide if that is convenient or not for us, both in the short and the long run. For instance, Robomaker from Amazon did not gain much traction because forced the developers to pay for the cloud while running Robomaker, when they could do the same thing (with less fancy stuff) in their own local computers for free

Conclusion

Overall, while Flowstate shows promising, further information and hands-on experience are required to assess its effectiveness and address potential challenges.

I have applied to the restricted beta. I hope to be selected so I can have a first hand experience and report about it.

Please make sure to read the original post by Wendy Tan White and the keynote presentation, both can be found at the web of Intrinsic.

Flowstate | Intrinsic (image copyright by Intrinsic)

Is Rosie the Robot Maid from the Jetsons here yet? Several different types of humanoid are currently deployed commercially or in trials. We’ve come along way since the DARPA Robotics Challenge of 2015/2016, where the most popular footage was the blooper reels of robots falling over and failing to open doors or climb stairs.

The Avatar XPrize of 2019-2022 showcased some extremely sophisticated humanoids that certainly advanced the state of the art but the holy grail of humanoid robots is combining incredible sophistication into a sub $50,000 package. Why $50,000? Wouldn’t some companies pay a lot more? Then again, can’t we buy a car, also a very sophisticated device capable of partial autonomy that is 5 times the size of a humanoid, for less than $50,000? Why is this the benchmark for humanoids?

$50,000 is the annual wage for a single shift of labor at slightly more than $18/hour or minimum wage in every low wage industry. There is a terrible labor shortage and it is the dirty dull and dangerous jobs that are hardest for employers to fill. Companies that can afford to run two or more shifts a day also have more alternatives when it comes to filling their labor gaps. It’s the small to medium size enterprise that is suffering the most in our current economic and demographic conditions.

We don’t need a Six Million Dollar Man.

We need a $50,000 humanoid.

The roll out of sophisticated new robots and how we integrate them into society is at the heart of my early research and my current roles as the Managing Director of Silicon Valley Robotics (explain), VP of Global Robotics for AMT (explain) and the VP of Industrial Activities for the IEEE Robotics and Automation Society (explain).

As more and more companies announce their work towards the affordable humanoid robot, I wanted to create a reference chart for myself, and realized that it might be of interest to others as well. The ranking system is just my own opinion and it will be fascinating to see who succeeds and progresses over the next few years. Enjoy this overview and make up your own minds as to which humanoid robot is really the best.

Who’s in the running? (in alphabetical order by company not robot)

• 1x – Eve
• Aeolus Robotics – Eva
• Agility Robotics – Digit
• Apptronik – Astra
• Boston Dynamics – Atlas
• Comma.ai – body
• Devanthro – Robody
• Engineered Arts – Ameca
• Figure – Figure01
• Giant.ai – Universal Worker
• IIT – ErgoCub
• PAL – Reem-C
• Prosper Robotics – Alfie
• Sanctuary – Phoenix
• Tesla – Optimus
• Toyota – T-HR3

Who isn’t in the running?

Hollywood Humanoids
Hollywood Humanoids are one off robots for the purpose of entertainment, like Sophia from Hanson Robotics, Xoxe from AI Life, or Beonmi from Beyond Imagination. ….

Chinese robots
It’s too hard for me to validate that they exist, work as advertized, and what the specifications are.

Research robots
Love them but they have a different purpose. Only robots with commercial deployment plans, and ideally, a price tag and a date in 2023 or 2024 when they’ll be available for purchase, if they aren’t already being sold.

Not humanoid
I also love robots that work like a humanoid but don’t look human-like. We saw some examples in the DARPA Robotics Challenge, most notably RoboSimian. Once we go down that route, all quadrupeds, and multi-armed robots or wheeled humanlike robots, would qualify. Who knew there were so many robots!

Who have I missed?

I’m hoping to crowdsource some more great robots :)

Read the original article on Substack.

Robotic head, 3D illustration (frank60/Shutterstock)

By Jonathan Roberts (Professor in Robotics, Queensland University of Technology)

With generative artificial intelligence (AI) systems such as ChatGPT and StableDiffusion being the talk of the town right now, it might feel like we’ve taken a giant leap closer to a sci-fi reality where AIs are physical entities all around us.

Indeed, computer-based AI appears to be advancing at an unprecedented rate. But the rate of advancement in robotics – which we could think of as the potential physical embodiment of AI – is slow.

Could it be that future AI systems will need robotic “bodies” to interact with the world? If so, will nightmarish ideas like the self-repairing, shape-shifting T-1000 robot from the Terminator 2 movie come to fruition? And could a robot be created that could “live” forever?

Energy for ‘life’

Biological lifeforms like ourselves need energy to operate. We get ours via a combination of food, water, and oxygen. The majority of plants also need access to light to grow.

By the same token, an everlasting robot needs an ongoing energy supply. Currently, electrical power dominates energy supply in the world of robotics. Most robots are powered by the chemistry of batteries.

An alternative battery type has been proposed that uses nuclear waste and ultra-thin diamonds at its core. The inventors, a San Francisco startup called Nano Diamond Battery, claim a possible battery life of tens of thousands of years. Very small robots would be an ideal user of such batteries.

But a more likely long-term solution for powering robots may involve different chemistry – and even biology. In 2021, scientists from the Berkeley Lab and UMAss Amherst in the US demonstrated tiny nanobots could get their energy from chemicals in the liquid they swim in.

The researchers are now working out how to scale up this idea to larger robots that can work on solid surfaces.

Repairing and copying oneself

Of course, an undying robot might still need occasional repairs.

Ideally, a robot would repair itself if possible. In 2019, a Japanese research group demonstrated a research robot called PR2 tightening its own screw using a screwdriver. This is like self-surgery! However, such a technique would only work if non-critical components needed repair.

Other research groups are exploring how soft robots can self-heal when damaged. A group in Belgium showed how a robot they developed recovered after being stabbed six times in one of its legs. It stopped for a few minutes until its skin healed itself, and then walked off.

Another unusual concept for repair is to use other things a robot might find in the environment to replace its broken part.

Last year, scientists reported how dead spiders can be used as robot grippers. This form of robotics is known as “necrobotics”. The idea is to use dead animals as ready-made mechanical devices and attach them to robots to become part of the robot.

The proof-of-concept in necrobotics involved taking a dead spider and ‘reanimating’ its hydraulic legs with air, creating a surprisingly strong gripper. Preston Innovation Laboratory/Rice University

A robot colony?

From all these recent developments, it’s quite clear that in principle, a single robot may be able to live forever. But there is a very long way to go.

Most of the proposed solutions to the energy, repair and replication problems have only been demonstrated in the lab, in very controlled conditions and generally at tiny scales.

The ultimate solution may be one of large colonies or swarms of tiny robots who share a common brain, or mind. After all, this is exactly how many species of insects have evolved.

The concept of the “mind” of an ant colony has been pondered for decades. Research published in 2019 showed ant colonies themselves have a form of memory that is not contained within any of the ants.

This idea aligns very well with one day having massive clusters of robots that could use this trick to replace individual robots when needed, but keep the cluster “alive” indefinitely.

Ant colonies can contain ‘memories’ that are distributed between many individual insects. frank60/Shutterstock

Ultimately, the scary robot scenarios outlined in countless science fiction books and movies are unlikely to suddenly develop without anyone noticing.

Engineering ultra-reliable hardware is extremely difficult, especially with complex systems. There are currently no engineered products that can last forever, or even for hundreds of years. If we do ever invent an undying robot, we’ll also have the chance to build in some safeguards.

Jonathan Roberts is Director of the Australian Cobotics Centre, the Technical Director of the Advanced Robotics for Manufacturing (ARM) Hub, and is a Chief Investigator at the QUT Centre for Robotics. He receives funding from the Australian Research Council. He was the co-founder of the UAV Challenge – an international drone competition.

Here’s my annual summary of the top stories of the prior year. This time the news was a strong mix of bad and good.

Read the text story on Forbes.com at Robocars 2022 year in review.

And see the video version here:

elwynn/Shutterstock

By Paul Cureton (Senior Lecturer in Design (People, Places, Products), Lancaster University) and Ole B. Jensen (Professor of Urban Theory and Urban Design, Aalborg University)

Drones are already shaping the face of our cities – used for building planning, heritage, construction and safety enhancement. But, as studies by the UK’s Department of Transport have found, swathes of the public have a limited understanding of how drones might be practically applied.

It’s crucial that the ways drones are affecting our future are understood by the majority of people. As experts in design futures and mobility, we hope this short overview of five ways drones will affect building design offers some knowledge of how things are likely to change.

Infographic showcasing other ways drones will influence future building design. Nuri Kwon, Drone Near-Futures, Imagination Lancaster, Author provided

1. Creating digital models of buildings

Drones can take photographs of buildings, which are then used to build 3D models of buildings in computer-aided design software.

These models have accuracy to within a centimetre, and can be combined with other data, such as 3D scans of interiors using drones or laser scanners, in order to provide a completely accurate picture of the structure for surveyors, architects and clients.

Using these digital models saves time and money in the construction process by providing a single source thaOle B. Jensent architects and planners can view.

2. Heritage simulations

Studio Drift are a multidisciplinary team of Dutch artists who have used drones to construct images through theatrical outdoor drone performances at damaged national heritage sites such as the Notre Dame in Paris, Colosseum in Rome and Gaudí’s Sagrada Familia in Barcelona.

Drones could be used in the near-future in a similar way to help planners to visualise the final impact of restoration or construction work on a damaged or partially finished building.

3. Drone delivery

The arrival of drone delivery services will see significant changes to buildings in our communities, which will need to provide for docking stations at community hubs, shops and pick-up points.

Wingcopter are one of many companies trialling delivery drones. Akash 1997, CC BY-SA

There are likely to be landing pads installed on the roofs of residential homes and dedicated drone-delivery hubs. Research has shown that drones can help with the last mile of any delivery in the UK, Germany, France and Italy.

Architects of the future will need to add these facilities into their building designs.

4. Drones mounted with 3D printers

Two research projects from architecture, design, planning, and consulting firm Gensler and another from a consortium led by Imperial College London (comprising University College London, University of Bath, University of Pennsylvania, Queen Mary University of London, and Technical University of Munich) named Empa have been experimenting with drones with mounted 3D printers. These drones would work at speed to construct emergency shelters or repair buildings at significant heights, without the need for scaffolding, or in difficult to reach locations, providing safety benefits.

Gensler have already used drones for wind turbine repair and researchers at Imperial College are exploring bee-like drone swarms that work together to construct blueprints. The drones coordinate with each other to follow a pre-defined path in a project called Aerial Additive Manufacturing. For now, the work is merely a demonstration of the technology, and not working on a specific building.

In the future, drones with mounted 3D printers could help create highly customised buildings at speed, but how this could change the workforce and the potential consequences for manual labour jobs is yet to be understood.

5. Agile surveillance

Drones offer new possibilities for surveillance away from the static, fixed nature of current systems such as closed circuit television.

Drones with cameras and sensors relying on complex software systems such as biometric indicators and “face recognition” will probably be the next level of surveillance applied by governments and police forces, as well as providing security monitoring for homeowners. Drones would likely be fitted with monitoring devices, which could communicate with security or police forces.

Drones used in this way could help our buildings become more responsive to intrusions, and adaptable to changing climates. Drones may move parts of the building such as shade-creating devices, following the path of the sun to stop buildings overheating, for example.

Earlier this month, Candy Crush celebrated its decade birthday by hosting a free party in lower Manhattan. The climax culminated with a drone light display of 500 Unmanned Ariel Vehicles (UAVs) illustrating the whimsical characters of the popular mobile game over the Hudson. Rather than applauding the decision, New York lawmakers ostracized the avionic wonders to Jersey. In the words of Democratic State Senator, Brad Hoylman, “Nobody owns New York City’s skyline – it is a public good and to allow a private company to reap profits off it is in itself offensive.” The complimentary event followed the model of Macy’s New York fireworks that have illuminated the Hudson skies since 1958. Unlike the department store’s pyrotechnics that release dangerous greenhouse gases into the atmosphere, drones are a quiet climate-friendly choice. Still, Luddite politicians plan to introduce legislation to ban the technology as a public nuisance, citing its impact on migratory birds, which are often more spooked by skyscrapers in Hoylman’s district.

Beyond aerial tricks, drones are now being deployed in novel ways to fill the labor gap of menial jobs that have not returned since the pandemic. Founded in 2018, Andrew Ashur’s Lucid Drones has been power-washing buildings throughout the United States for close to five years. As the founder told me: “I saw window washers hanging off the side of the building on a swing stage and it was a mildly windy day. You saw this platform get caught in the wind and all of a sudden the platform starts slamming against the side of the building. The workers were up there, hanging on for dear life, and I remember having two profound thoughts in this moment. The first one, thank goodness that’s not me up there. And then the second one was how can we leverage technology to make this a safer, more efficient job?” At the time, Ashur was a junior at Davidson College playing baseball. The self-starter knew he was on to a big market opportunity.

Each year, more than 160,000 emergency room injuries, and 300 deaths, are caused by falling off of ladders in the United States. Entrepreneurs like Ashur understood that drones were uniquely qualified to free humans from such dangerous work. This first required building a sturdy tethered quadcopter, capable of a 300 psi flow rate, connected to a tank for power and cleaning fluid for less than the cost of the annual salary of one window cleaner. After overcoming the technical hurdle, the even harder task was gaining sales traction. Unlike many hardware companies that set out to disrupt the market and sell directly to end customers; Lucid partnered with existing building maintenance operators. “Our primary focus is on existing cleaning companies. And the way to think about it is we’re now the shiniest tool in their toolkit that helps them do more jobs with less time and less liability to make more revenue,” explains Ashur. This relationship was further enhanced this past month with the announcement of a partnership with Sunbelt Rentals, servicing its 1,000 locations throughout California, Florida, and Texas. Lucid’s drones are now within driving distance of the majority of the 86,000 facade cleaning companies in America.

According to Commercial Buildings Energy Consumption Survey, there are 5.9 million commercial office buildings in the United States, with an average height of 16 floors. This means there is room for many robot cleaning providers. Competing directly with Lucid are several other drone operators, including Apellix, Aquiline Drones, Alpha Drones, and a handful of local upstarts. In addition, there are several winch-powered companies, such as Skyline Robotics, HyCleaner, Serbot, Erlyon, Kite Robotics, and SkyPro. Facade cleaning is ripe for automation as it is a dangerous, costly, repetitive task that can be safely accomplished by an uncrewed system. As Ashur boasts, “You improve that overall profitability because it’s fewer labor hours. You’ve got lower insurance on ground cleaner versus an above ground cleaner as well as the other equipment.” His system being tethered, ground-based, and without any ladders is the safest way to power wash a multistory office building. He elaborated further on the cost savings, “It lowers insurance cost, especially when you look at how workers comp is calculated… we had a customer, one of their workers missed the bottom rung of the ladder, the bottom rung, he shattered his ankle. OSHA classifies it as a hazardous workplace injury. Third workers comp rates are projected to increase by an annual $25,000 over the next five years. So it’s a six-figure expense for just that one business from missing one single bottom rung of the ladder and unfortunately, you hear stories of people falling off a roof or other terrible accidents that are life changing or in some cases life lost. So that’s the number one thing you get to eliminate with the drone by having people on the ground.”

Ghost Robotics Vision 60 Q-UGV. US Space Force photo by Senior Airman Samuel Becker

By Toby Walsh (Professor of AI at UNSW, Research Group Leader, UNSW Sydney)

You might suppose Hollywood is good at predicting the future. Indeed, Robert Wallace, head of the CIA’s Office of Technical Service and the US equivalent of MI6’s fictional Q, has recounted how Russian spies would watch the latest Bond movie to see what technologies might be coming their way.

Hollywood’s continuing obsession with killer robots might therefore be of significant concern. The newest such movie is Apple TV’s forthcoming sex robot courtroom drama Dolly.

I never thought I’d write the phrase “sex robot courtroom drama”, but there you go. Based on a 2011 short story by Elizabeth Bear, the plot concerns a billionaire killed by a sex robot that then asks for a lawyer to defend its murderous actions.

The real killer robots

Dolly is the latest in a long line of movies featuring killer robots – including HAL in Kubrick’s 2001: A Space Odyssey, and Arnold Schwarzenegger’s T-800 robot in the Terminator series. Indeed, conflict between robots and humans was at the centre of the very first feature-length science fiction film, Fritz Lang’s 1927 classic Metropolis.

But almost all these movies get it wrong. Killer robots won’t be sentient humanoid robots with evil intent. This might make for a dramatic storyline and a box office success, but such technologies are many decades, if not centuries, away.

Indeed, contrary to recent fears, robots may never be sentient.

It’s much simpler technologies we should be worrying about. And these technologies are starting to turn up on the battlefield today in places like Ukraine and Nagorno-Karabakh.

A war transformed

Movies that feature much simpler armed drones, like Angel has Fallen (2019) and Eye in the Sky (2015), paint perhaps the most accurate picture of the real future of killer robots.

On the nightly TV news, we see how modern warfare is being transformed by ever-more autonomous drones, tanks, ships and submarines. These robots are only a little more sophisticated than those you can buy in your local hobby store.

And increasingly, the decisions to identify, track and destroy targets are being handed over to their algorithms.

This is taking the world to a dangerous place, with a host of moral, legal and technical problems. Such weapons will, for example, further upset our troubled geopolitical situation. We already see Turkey emerging as a major drone power.

And such weapons cross a moral red line into a terrible and terrifying world where unaccountable machines decide who lives and who dies.

Robot manufacturers are, however, starting to push back against this future.

A pledge not to weaponise

Last week, six leading robotics companies pledged they would never weaponise their robot platforms. The companies include Boston Dynamics, which makes the Atlas humanoid robot, which can perform an impressive backflip, and the Spot robot dog, which looks like it’s straight out of the Black Mirror TV series.

This isn’t the first time robotics companies have spoken out about this worrying future. Five years ago, I organised an open letter signed by Elon Musk and more than 100 founders of other AI and robot companies calling for the United Nations to regulate the use of killer robots. The letter even knocked the Pope into third place for a global disarmament award.

However, the fact that leading robotics companies are pledging not to weaponise their robot platforms is more virtue signalling than anything else.

We have, for example, already seen third parties mount guns on clones of Boston Dynamics’ Spot robot dog. And such modified robots have proven effective in action. Iran’s top nuclear scientist was assassinated by Israeli agents using a robot machine gun in 2020.

Collective action to safeguard our future

The only way we can safeguard against this terrifying future is if nations collectively take action, as they have with chemical weapons, biological weapons and even nuclear weapons.

Such regulation won’t be perfect, just as the regulation of chemical weapons isn’t perfect. But it will prevent arms companies from openly selling such weapons and thus their proliferation.

Therefore, it’s even more important than a pledge from robotics companies to see the UN Human Rights council has recently unanimously decided to explore the human rights implications of new and emerging technologies like autonomous weapons.

Several dozen nations have already called for the UN to regulate killer robots. The European Parliament, the African Union, the UN Secretary General, Nobel peace laureates, church leaders, politicians and thousands of AI and robotics researchers like myself have all called for regulation.

Australian is not a country that has, so far, supported these calls. But if you want to avoid this Hollywood future, you may want to take it up with your political representative next time you see them.

Toby Walsh does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

This article appeared in The Conversation.

By Wafa Johal (Senior Lecturer, Computing & Information Systems, The University of Melbourne)

In August 2021, Tesla CEO Elon Musk announced the electric car manufacturer was planning to get into the robot business. In a presentation accompanied by a human dressed as a robot, Musk said work was beginning on a “friendly” humanoid robot to “navigate through a world built for humans and eliminate dangerous, repetitive and boring tasks”.

Musk has now unveiled a prototype of the robot, called Optimus, which he hopes to mass-produce and sell for less than US$20,000 (A$31,000).

At the unveiling, the robot walked on a flat surface and waved to the crowd, and was shown doing simple manual tasks such as carrying and lifting in a video. As a robotics researcher, I didn’t find the demonstration very impressive – but I am hopeful it will lead to bigger and better things.

Why would we want humanoid robots?

Most of the robots used today don’t look anything like people. Instead, they are machines designed to carry out a specific purpose, like the industrial robots used in factories or the robot vacuum cleaner you might have in your house.

So why would you want one shaped like a human? The basic answer is they would be able to operate in environments designed for humans.

Unlike industrial robots, humanoid robots might be able to move around and interact with humans. Unlike robot vacuum cleaners, they might be able to go up stairs or traverse uneven terrain.

And as well as practical considerations, the idea of “artificial humans” has long had an appeal for inventors and science-fiction writers!

Room for improvement

Based on what we saw in the Tesla presentation, Optimus is a long way from being able to operate with humans or in human environments. The capabilities of the robot showcased fall far short of the state of the art in humanoid robotics.

The Atlas robot made by Boston Dynamics, for example, can walk outdoors and carry out flips and other acrobatic manoeuvres.

And while Atlas is an experimental system, even the commercially available Digit from Agility Robotics is much more capable than what we have seen from Optimus. Digit can walk on various terrains, avoid obstacles, rebalance itself when bumped, and pick up and put down objects.

Bipedal walking (on two feet) alone is no longer a great achievement for a robot. Indeed, with a bit of knowledge and determination you can build such a robot yourself using open source software.

There was also no sign in the Optimus presentation of how it will interact with humans. This will be essential for any robot that works in human environments: not only for collaborating with humans, but also for basic safety.

It can be very tricky for a robot to accomplish seemingly simple tasks such as handing an object to a human, but this is something we would want a domestic humanoid robot to be able to do.

Sceptical consumers

Others have tried to build and sell humanoid robots in the past, such as Honda’s ASIMO and SoftBank’s Pepper. But so far they have never really taken off.

Amazon’s recently released Astro robot may make inroads here, but it may also go the way of its predecessors.

Consumers seem to be sceptical of robots. To date, the only widely adopted household robots are the Roomba-like vacuum cleaners, which have been available since 2002.

To succeed, a humanoid robot will need be able to do something humans can’t to justify the price tag. At this stage the use case for Optimus is still not very clear.

Hope for the future

Despite these criticisms, I am hopeful about the Optimus project. It is still in the very early stages, and the presentation seemed to be aimed at recruiting new staff as much as anything else.

Tesla certainly has plenty of resources to throw at the problem. We know it has the capacity to mass produce the robots if development gets that far.

Musk’s knack for gaining attention may also be helpful – not only for attracting talent to the project, but also to drum up interest among consumers.

Robotics is a challenging field, and it’s difficult to move fast. I hope Optimus succeeds, both to make something cool we can use – and to push the field of robotics forward.

Wafa Johal receives funding from the Australian Research Council.

This article appeared in The Conversation.

RoboCup is an international scientific initiative with the goal of advancing the state of the art of intelligent robots, AI and automation. The annual RoboCup event returned to an in-person format for 2022, taking place from 13-17 July in Bangkok. RoboCup comprises a number of leagues, with perhaps the most well-known being the soccer leagues.

In the Humanoid League, autonomous robots with a human-inspired body plan and senses play soccer against each other. We spoke to Jasper Güldenstein, a member of the technical committee, about the competition at RoboCup 2022, and also about the Humanoid League Virtual Season. As a biweekly virtual competition taking place between the physical RoboCup events it provides teams with an opportunity to test their ideas and keep in touch throughout the year.

Could you give us an overview of the Humanoid League competition at RoboCup this year?

This year we had the first in-person event after a three year break. It was really good to have the majority of the teams coming back together and working on their robots again. Although a lot of teams came with fewer participants than they usually do, we still managed to have 12 teams in the kid size league and three teams in the adult size. Unfortunately, some teams could not participate due to travel restrictions, but we hope to see them back next year.

Humanoid league finalists with their robots

What was the format for the competition?

At the beginning we had a drop-in round, which is where we play games with one robot from each team participating in a joint team. So, we have eight robots playing from eight different teams. That is useful for finding out which teams are likely to perform the best in the competition. Next, the results from the drop-in games were used as seeding for the round robin phase of the regular competition, with the strongest teams separated into different groups. After the round robin, we have a knockout competition. The seeding method means that we can hopefully avoid the situation where very good teams get kicked out early. We saw that the most interesting games were towards the end of the competition when the teams performed really well.

Have you noticed improvements since the last physical competition?

I’d say definitely that one big thing that has improved for a lot of teams is the localisation. A lot of teams are more easily able to localise themselves on the field, and they don’t run off randomly. They are more certain that they are in the correct position.

Furthermore, I think the kicking has improved. The robots kick the ball much further than they used to. People have been tuning their kicking motions to increase the distance.

In terms of computer vision, this has definitely improved quite a bit. Something we did differently this time, which was inspired by what we did in the virtual season, is that we had a set of six different balls, all from previous FIFA competitions. For each game a ball was drawn randomly, so the teams couldn’t really prepare for all the balls. Although they were visually quite different, the teams didn’t really have any problems detecting the ball. We’ve seen, in general, that computer vision approaches have improved and these improvements have been transferred to the RoboCup competition. I think that almost all teams are using a neural network to detect the ball. This is a change from three, four, five years ago, where many teams used hand-tuned classical computer vision algorithms.

To talk a bit more about ball detection, it will be interesting to see what happens if we move to an environment with natural and/or varying light conditions. This year we were in a convention hall with uniform lighting. I believe next year, in Bordeaux, there is going to be some form of natural light coming in, and perhaps even fields that are outside. It’s still at the planning stage but we are looking forward to that. It will be a challenge and I strongly believe that the participants will find approaches to make their vision approach robust against these varying conditions.

The setup and testing for the Humanoid League competition at RoboCup 2022, Bangkok.

Thinking about the transfer from the simulation to the real world, are there any specific elements that lend themselves well to being transferred?

In terms of computer vision, we had a bit of transfer. In the virtual season we concentrated a lot on changing the lighting conditions and having varied backgrounds, to be able to emulate the real world a bit better. I think a few teams used their vision approaches from the virtual season in the real world.

However, I think the strongest part is behaviour. Teams were able to test their strategies in the virtual competition and adapt every other week. For example, CIT Brains, which won the virtual season and the physical competition, made quite a few changes to their strategy and they had robust systems running. Their strategy worked really well, and in the final they managed to score several goals win against the previous world champions (Rhoban Football Club).

How did the competition go for your team (Hamburg Bit-Bots)?

We actually had quite a few hardware problems, especially on the mechanics side. The motors are wearing out and warped due to wear, and flexed more than we expected. This meant we had difficulties walking stably. And, if you can’t walk in a stable manner that defeats the purpose of everything else. It’s a really integrated system – if one component breaks, you are out of luck as you are very restricted in what you can change during the competition as you don’t have much spare equipment with you.

However, what was good for us was that we had a lot of software up and running, and a lot of it had been tested really well during the virtual league. We had to try and find a way round the walking problem algorithmically, to try to find walking parameters that were more stable. We also switched from [robot operating system] ROS 1 to ROS 2 which brought with it many challenges. We actually did a write up on the issues we faced, with some tricks and tips.

Will there be a new virtual league season this year?

Yes, we’ve discussed this in our technical committee and we plan on doing it again. The last event was successful and the teams enjoyed it. We plan on making some changes, such as logging the games to extract some interesting metrics and doing some analysis on those.

Another thing we want to do is domain randomisation, making the environment a bit more varied. This means that the approaches have to be more robust. The hope is that, when they are more robust, they can be transferred more easily to the real world. We were thinking about making the terrain slightly uneven. Another approach could be to modify the models of the robots such that the joints emulate a bit of wear, so they simulated actuators might be a bit weaker or stronger randomly, and teams have to find robust approaches to deal with that.

We won’t do everything at the beginning. We’ll move through the season and talk to the teams and form some organising groups to develop the simulation further, to run the games and to organise the competition itself. We are always happy to have input and we always talk to the teams to see what they think. It’s a competition but it’s something we build together.

Humanoid robots at RoboCup 2022, Bangkok.

Could you tell us about the satellite event that took place at RoboCup?

This was a discussion about how to get teams more interested in participating and how to bridge the junior leagues and the major leagues.

We know that some people who participated in RoboCup Junior specifically selected a University that has a RoboCup team so that they could join that team. It would be awesome for more people to do this, and for more juniors to know what the major league is about.

To bridge the gap between the junior and major leagues we don’t want to introduce another league, but we want some form of events where the two groups can meet, and where the juniors can show off how well they are doing. It would be good to have more interaction between the leagues, although we haven’t decided on the exact format yet.

About Jasper

Jasper Güldenstein is a PhD student at University of Hamburg. His research focuses on humanoid navigation. He has been participating in RoboCup as a member of the Hamburg Bit-Bots since 2016 where his focus is developing not only the software of the team but also the electronics of the robot platform. In his recent master thesis he evaluated using reinforcement learning to perform path planning and execution for humanoid robots.

Robots: humanoids, teleoperated reconfigurable robots, swarms.

Recommendation: Read this classic hard sci-fi novel and expand your horizons about robots, teleoperation, and swarms.

Stanislaw Lem was one of the most read science fiction authors in the world in his day, especially the 70s and 80s, though not in America because there were rarely translations from his native Polish to English. Europeans could parse the French translations, we couldn’t even parlez vous francais. Lem famously did not like American science fiction, with a very few exceptions. One being Philip K. Dick- and it is no wonder since Lem’s 1987 novel Peace on Earth shares many of the same themes that Dick covered: militarization of robots, people losing their memory or not being what they seem, and government conspiracies. In some ways Peace on Earth is like the longer, more detailed, and, actually, *better* version of Dick’s 1953 short story Second Variety (which was basis for the Peter Weller movie Screamers).

Peace on Earth has a sort of a Battlestar Galatica (reboot) backstory. Mankind has put all their military robots on the moon to do whatever military robots do. The rapidly evolving, super smart robots can continue to use simulations and machine learning to improve or work out alternatives to Clauswitz style of warfare but out of the way so that it can’t impact humans.

Or can it?

Except after a couple of decades no one has heard from the robots. This is not unexpected, but people, being people, are beginning to wonder if the robots are still up there. Or maybe the robots have evolved into something peaceful. Or into some supreme intelligence that might want to take over the Earth. Or maybe the robots have run out of things to shot at up there and the winners are now thinking about shooting at Earth. Oooops. Maybe we should send someone to check in on them, just in case…

The story is told from the viewpoint of the astronaut, Ijon Tichy, sent to check in on the robots. The book starts with his return on Earth with brain damage that has severed his corpus callosum, left him with major memory loss as to what happened and why he is on the run. We are in Christopher Nolan Momento territory (without the tattoos) or Jonathan Nolan’s/HBO’s Westworld out of sequence story telling as Tichy tries to figure out what happened on the Moon and what it means.

Along the way we get some interesting descriptions of telerobotics and telepresence as well as swarm and distributed robotics. Lem was a hard science ficition writer, who had gone to medical school before switching to physics. He was very much into the science component of his books and in this case more of the ideas of biological evolution. He posits that biological evolution has been about the evolution of small to large— from viruses and bacteria to single cells to animals and people, but that robotics evolution will be from large to small. We started with big robots improving, then getting smaller with miniaturization of sensors and actuators, then smaller computation as a single robot would not need to carry all its computation onboard but could rely on distributed computation, and the trend will continue finally a robot becomes a collection of tiny, simple robots that can cast itself into a larger shape with greater intelligence— the idea behind Michael Crichton’s novel Prey. These swarms of what we would now call nanorobots would provide the ultimate flexibility in reconfigurable robots. Of course, Lem hand waves over limiting factors such as power and communication. But that aside, it’s a thought-provoking idea and a radically different take than Dick’s on how military robots would evolve.

One of the interesting scientific themes in Peace on Earth is Tichy’s use of teleoperation robots to land on the Moon and attempt to check out the robots in the different sectors of the Moon. Eventually Tichy quits using humanoid robots and begins using a reconfigurable robot body that can transform into different animal shapes so as to move more effectively through the different structures built by the robots.

Teleoperated robots are sometimes called avatars, though the term avatar was originally restricted to software simulations- James Cameron changed that connotation with his movie. There is increasing interest in telecommuting (and telesex) through robots, so much so, there is now a XPrize competition on avatars.

My favorite shape that Tichy’s teleoperated robot took on was that of a dachshund. And here is where Lem underestimated the scientific challenges of teleoperation. Lem focused on the physical science— how the avatar might reconfigure into a new shape. He assumed that Tichy would have little difficulty adjusting to the new shape because Tichy would be wearing a suit that sensed his body movements. Except this ignores the human-robot interaction component— how does Tichy know to move like a dog and synthesize perception from angles much lower than a human? The degrees of freedom are different, the movement patterns are different, the location of sensors are different. Operators get rapidly fatigued with humanoid robots where there is a one-to-one correspondence between the human and robot and there is no change in size. The cognitive load for trying to control a four legged animal would be huge. It is hard to imagine that Tichy would be successful without an intermediary intelligent assistance program that would translate his intent into the appropriate motions for the current shape.

And that type of assistive AI is a hard, open research question.

The XPrize ANA Avatar competition is making a similar assumption, that if you can build a humanoid avatar, it will be easy and natural for a human to control. That hasn’t been supported by decades of research in telerobotics and the humanoid robots in the DARPA robotics challenge often required multiple operators.

But back to Peace on Earth. It’s a very readable book jam packed with scientific ideas that were ahead of its time, combined with a serious jab at the stupidity of the nuclear arms race that was in progress at the time.

More importantly, Lew foresaw a world in which robots could be a threat, though politicians were a bigger threat, and were a solution to the threat. A refreshing take on robotics and the New World Order. What a shame Lem has been relatively unknown in the US.

You really should read this one, especially if you like hard science fiction like Arthur C. Clarke or if you want to get beyond the US viewpoint of sci-fi.

For an audio version of this review, click below…

Original article posted in Robotics Through Science Fiction blog.

Who wouldn’t want a robot to handle all the household drudgery? Skathi/iStock via Getty Images

By Ayonga Hereid (Assistant Professor of Mechanical and Aerospace Engineering, The Ohio State University)

With recent advances in artificial intelligence and robotics technology, there is growing interest in developing and marketing household robots capable of handling a variety of domestic chores.

Tesla is building a humanoid robot, which, according to CEO Elon Musk, could be used for cooking meals and helping elderly people. Amazon recently acquired iRobot, a prominent robotic vacuum manufacturer, and has been investing heavily in the technology through the Amazon Robotics program to expand robotics technology to the consumer market. In May 2022, Dyson, a company renowned for its power vacuum cleaners, announced that it plans to build the U.K.’s largest robotics center devoted to developing household robots that carry out daily domestic tasks in residential spaces.

Despite the growing interest, would-be customers may have to wait awhile for those robots to come on the market. While devices such as smart thermostats and security systems are widely used in homes today, the commercial use of household robots is still in its infancy.

As a robotics researcher, I know firsthand how household robots are considerably more difficult to build than smart digital devices or industrial robots.

Robots that can handle a variety of domestic chores are an age-old staple of science fiction.

Handling objects

One major difference between digital and robotic devices is that household robots need to manipulate objects through physical contact to carry out their tasks. They have to carry the plates, move the chairs and pick up dirty laundry and place it in the washer. These operations require the robot to be able to handle fragile, soft and sometimes heavy objects with irregular shapes.

The state-of-the-art AI and machine learning algorithms perform well in simulated environments. But contact with objects in the real world often trips them up. This happens because physical contact is often difficult to model and even harder to control. While a human can easily perform these tasks, there exist significant technical hurdles for household robots to reach human-level ability to handle objects.

Robots have difficulty in two aspects of manipulating objects: control and sensing. Many pick-and-place robot manipulators like those on assembly lines are equipped with a simple gripper or specialized tools dedicated only to certain tasks like grasping and carrying a particular part. They often struggle to manipulate objects with irregular shapes or elastic materials, especially because they lack the efficient force, or haptic, feedback humans are naturally endowed with. Building a general-purpose robot hand with flexible fingers is still technically challenging and expensive.

It is also worth mentioning that traditional robot manipulators require a stable platform to operate accurately, but the accuracy drops considerably when using them with platforms that move around, particularly on a variety of surfaces. Coordinating locomotion and manipulation in a mobile robot is an open problem in the robotics community that needs to be addressed before broadly capable household robots can make it onto the market.

A sophisticated robotic kitchen is already on the market, but it operates in a highly structured environment, meaning all of the objects it interacts with – cookware, food containers, appliances – are where it expects them to be, and there are no pesky humans to get in the way.

They like structure

In an assembly line or a warehouse, the environment and sequence of tasks are strictly organized. This allows engineers to preprogram the robot’s movements or use simple methods like QR codes to locate objects or target locations. However, household items are often disorganized and placed randomly.

Home robots must deal with many uncertainties in their workspaces. The robot must first locate and identify the target item among many others. Quite often it also requires clearing or avoiding other obstacles in the workspace to be able to reach the item and perform given tasks. This requires the robot to have an excellent perception system, efficient navigation skills, and powerful and accurate manipulation capability.

For example, users of robot vacuums know they must remove all small furniture and other obstacles such as cables from the floor, because even the best robot vacuum cannot clear them by itself. Even more challenging, the robot has to operate in the presence of moving obstacles when people and pets walk within close range.

Keeping it simple

While they appear straightforward for humans, many household tasks are too complex for robots. Industrial robots are excellent for repetitive operations in which the robot motion can be preprogrammed. But household tasks are often unique to the situation and could be full of surprises that require the robot to constantly make decisions and change its route in order to perform the tasks.

The vision for household humanoid robots like the proposed Tesla Bot is of an artificial servant capable of handling any mundane task. Courtesy Tesla

Think about cooking or cleaning dishes. In the course of a few minutes of cooking, you might grasp a sauté pan, a spatula, a stove knob, a refrigerator door handle, an egg and a bottle of cooking oil. To wash a pan, you typically hold and move it with one hand while scrubbing with the other, and ensure that all cooked-on food residue is removed and then all soap is rinsed off.

There has been significant development in recent years using machine learning to train robots to make intelligent decisions when picking and placing different objects, meaning grasping and moving objects from one spot to another. However, to be able to train robots to master all different types of kitchen tools and household appliances would be another level of difficulty even for the best learning algorithms.

Not to mention that people’s homes often have stairs, narrow passageways and high shelves. Those hard-to-reach spaces limit the use of today’s mobile robots, which tend to use wheels or four legs. Humanoid robots, which would more closely match the environments humans build and organize for themselves, have yet to be reliably used outside of lab settings.

A solution to task complexity is to build special-purpose robots, such as robot vacuum cleaners or kitchen robots. Many different types of such devices are likely to be developed in the near future. However, I believe that general-purpose home robots are still a long way off.

Ayonga Hereid does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

This article appeared in The Conversation.

About 5 years ago we proposed that all robots should be fitted with the robot equivalent of an aircraft Flight Data Recorder to continuously record sensor and relevant internal status data. We call this an ethical black box (EBB). We argued that an ethical black box will play a key role in the processes of discovering why and how a robot caused an accident, and thus an essential part of establishing accountability and responsibility.

Since then, within the RoboTIPS project, we have developed and tested several model EBBs, including one for an e-puck robot that I wrote about in this blog, and another for the MIRO robot. With some experience under our belts, we have now drafted an Open Standard for the EBB for social robots – initially as a paper submitted to the International Conference on Robots Ethics and Standards. Let me now explain first why we need a standard, and second why it should be an open standard.

Why do we need a standard specification for an EBB? As we outline in our new paper, there are four reasons:

1. A standard approach to EBB implementation in social robots will greatly benefit accident and incident (near miss) investigations. 
2. An EBB will provide social robot designers and operators with data on robot use that can support both debugging and functional improvements to
   the robot. 
3. An EBB can be used to support robot ‘explainability’ functions to allow, for instance, the robot to answer ‘Why did you just do that?’ questions from its user. And,
4. a standard allows EBB implementations to be readily shared and adapted for different robots and, we hope, encourage manufacturers to develop and market general purpose robot EBBs.

And why should it be an Open Standard? Bruce Perens, author of The Open Source Definition, outlines a number of criteria an open standard must satisfy, including:

• “Availability: Open standards are available for all to read and implement.
• Maximize End-User Choice: Open Standards create a fair, competitive market for implementations of the standard.
• No Royalty: Open standards are free for all to implement, with no royalty
  or fee.
• No Discrimination: Open standards and the organizations that administer them do not favor one implementor over another for any reason other than the technical standards compliance of a vendor’s implementation.
• Extension or Subset: Implementations of open standards may be extended, or offered in subset form.”

These are *good* reasons.

The most famous and undoubtedly the most impactful Open Standards are those that were written for the Internet. They were, and still are, called Requests for Comments (RFCs) to reflect the fact that they were – especially in the early years, drafts for revision. As a mark of respect we also regard our draft 0.1 Open Standard for an EBB for Social Robots, as an RFC. You can find draft 0.1 in Annex A of the paper here.

Not only is this a first draft, it is also incomplete, covering only the specification of the data and its format, that should be saved in an EBB for social robots. Given that the EBB data specification is at the heart of the EBB standard, we feel that this is sufficient to be opened up for comments and feedback. We will continue to extend the specification, with subsequent versions also published on arXiv.

Let me know encourage comments and feedback. Please feel free to either submit comments to this blog post – this way everyone can see the comments – or by contacting me directly via email. All constructive comments that result in revisions to the standard will be acknowledged in the standard.

Valentine’s Day is approaching… Do want to sneak in a robot movie to watch on date night? Do you wonder about whether robots and love is possible? Here are five recommendations for sci-fi movies with a discussion of the related real-world robotics science. And remember to check out Learn AI and Human-Robot Interaction from Asimov’s I, Robot Stories– it’s a great primer on social interactions!

Can roboticists make the perfect partner? The original 1975 The Stepford Wives argues “yes”– if your definition of the perfect partner is limited to their appearance and willingness to selflessly perform subservient tasks. The movie, and book it is based on, extrapolated the advances in animatronics at Disney which had opened the Hall of Presidents attraction in 1971 to great acclaim. The assumption was that the hard part in creating a human substitute is creating robots that looked and moved like humans.

Mimicking human movement and facial expressiveness is certainly a challenge for the mechanics and control of a robot, think Hanson’s Sophia and Ishiguro’s Geminoid series of ‘bots. But physical fidelity is not the same as creating the artificial general intelligence needed to avoid the Uncanny Valley or hold a meaningful conversation. See more about the science of making life-like robots here.

Creating the perfect sex partner is a real issue in robotics, with major capital investments in the sexbot industry. This presents real legal and ethical issues. See the RTSF interview with Dr. Van Wynsberghe of the The Foundation for Responsible Robotics here. The FRR has been working to get governments to set policies— see their report Our Sexual Future with Robots. As a woman, I appreciate The Campaign Against Sex Robots which argues that acting out sexual fantasies with sexbots that would make even Takashi Kovacs in Altered Carbon blush have toxic gender, cultural, and mental health implications. Check out the great article on the legal status of sexbots in the US here.

Nominally a horror movie, The Stepford Wives is likely to make you both grateful for whatever positive relationship you have with each other. But get the 1975 original, not the 2004 remake.

Can roboticists make a robot that is better at social interactions, including love, than we are? In the 1987 movie Making Mr. Right, a neuroatypical scientist, played by John Malkovich, builds an android, also played by John Malkovich, that is much more socially competent than he is. A press agent is assigned to handle public relations and teach it to be more emotional. Love ensues.

Social interaction skills are a hot topic in human-robot research, see the large number of papers in venues such as the annual IEEE/ACM Conference on Human-Robot Interaction. But, sadly, most of the touted skills are social engineering tricks that make us think the robots have human skills. Noel Sharkey has a great set of articles on this type of advance/parlor trick in robotics.

Making Mr. Right is a romantic comedy, not a particularly good one, but it *is* the opposite of The Stepford Wives and, well, a romcom. It might inspire some snuggles.

Can’t roboticists make it where we can just download our brains to speed up the process of making robots more human like? In science fiction, that never works and three movies illustrates how it could lead to some tainted love. Saturn 3 and Eve of Destruction, and Demon Seed (FYI, the book is waaay better than the movie) are delightfully, MST3K worthy movies. All three have robot creators who download their brains, apparently missing the class on Freud and the Id. Ooops! Each movie has top actors, including Harvey Keitel, Kirk Douglas, Farrah Fawcett, Gregory Hines, and Julie Christie, who probably regret their decision to participate.

In the real world, downloading probably won’t work either, even if we can edit out our Id. There is some work on transfer learning and brain-computer interfaces but that work is more about motor skills and control, not abstract reasoning and memories.

If togetherness means throwing popcorn at a big screen TV and shouting out derisive comments then any of these three movies are terrific choices to watch with your special someone! My favorite is Saturn 3.

Here’s another link to a slideshow discussing emotions in robots and the general topic page. Whatever you decide to watch, enjoy and keep learning about robots!

Humanitarian groups have been calling for a ban on autonomous weapons. Wolfgang Kumm/picture alliance via Getty Images

By James Dawes

Autonomous weapon systems – commonly known as killer robots – may have killed human beings for the first time ever last year, according to a recent United Nations Security Council report on the Libyan civil war. History could well identify this as the starting point of the next major arms race, one that has the potential to be humanity’s final one.

The United Nations Convention on Certain Conventional Weapons debated the question of banning autonomous weapons at its once-every-five-years review meeting in Geneva Dec. 13-17, 2021, but didn’t reach consensus on a ban. Established in 1983, the convention has been updated regularly to restrict some of the world’s cruelest conventional weapons, including land mines, booby traps and incendiary weapons.

Autonomous weapon systems are robots with lethal weapons that can operate independently, selecting and attacking targets without a human weighing in on those decisions. Militaries around the world are investing heavily in autonomous weapons research and development. The U.S. alone budgeted US$18 billion for autonomous weapons between 2016 and 2020.

Meanwhile, human rights and humanitarian organizations are racing to establish regulations and prohibitions on such weapons development. Without such checks, foreign policy experts warn that disruptive autonomous weapons technologies will dangerously destabilize current nuclear strategies, both because they could radically change perceptions of strategic dominance, increasing the risk of preemptive attacks, and because they could be combined with chemical, biological, radiological and nuclear weapons themselves.

As a specialist in human rights with a focus on the weaponization of artificial intelligence, I find that autonomous weapons make the unsteady balances and fragmented safeguards of the nuclear world – for example, the U.S. president’s minimally constrained authority to launch a strike – more unsteady and more fragmented. Given the pace of research and development in autonomous weapons, the U.N. meeting might have been the last chance to head off an arms race.

Lethal errors and black boxes

I see four primary dangers with autonomous weapons. The first is the problem of misidentification. When selecting a target, will autonomous weapons be able to distinguish between hostile soldiers and 12-year-olds playing with toy guns? Between civilians fleeing a conflict site and insurgents making a tactical retreat?

Killer robots, like the drones in the 2017 short film ‘Slaughterbots,’ have long been a major subgenre of science fiction. (Warning: graphic depictions of violence.)

The problem here is not that machines will make such errors and humans won’t. It’s that the difference between human error and algorithmic error is like the difference between mailing a letter and tweeting. The scale, scope and speed of killer robot systems – ruled by one targeting algorithm, deployed across an entire continent – could make misidentifications by individual humans like a recent U.S. drone strike in Afghanistan seem like mere rounding errors by comparison.

Autonomous weapons expert Paul Scharre uses the metaphor of the runaway gun to explain the difference. A runaway gun is a defective machine gun that continues to fire after a trigger is released. The gun continues to fire until ammunition is depleted because, so to speak, the gun does not know it is making an error. Runaway guns are extremely dangerous, but fortunately they have human operators who can break the ammunition link or try to point the weapon in a safe direction. Autonomous weapons, by definition, have no such safeguard.

Importantly, weaponized AI need not even be defective to produce the runaway gun effect. As multiple studies on algorithmic errors across industries have shown, the very best algorithms – operating as designed – can generate internally correct outcomes that nonetheless spread terrible errors rapidly across populations.

For example, a neural net designed for use in Pittsburgh hospitals identified asthma as a risk-reducer in pneumonia cases; image recognition software used by Google identified Black people as gorillas; and a machine-learning tool used by Amazon to rank job candidates systematically assigned negative scores to women.

The problem is not just that when AI systems err, they err in bulk. It is that when they err, their makers often don’t know why they did and, therefore, how to correct them. The black box problem of AI makes it almost impossible to imagine morally responsible development of autonomous weapons systems.

The proliferation problems

The next two dangers are the problems of low-end and high-end proliferation. Let’s start with the low end. The militaries developing autonomous weapons now are proceeding on the assumption that they will be able to contain and control the use of autonomous weapons. But if the history of weapons technology has taught the world anything, it’s this: Weapons spread.

Market pressures could result in the creation and widespread sale of what can be thought of as the autonomous weapon equivalent of the Kalashnikov assault rifle: killer robots that are cheap, effective and almost impossible to contain as they circulate around the globe. “Kalashnikov” autonomous weapons could get into the hands of people outside of government control, including international and domestic terrorists.

The Kargu-2, made by a Turkish defense contractor, is a cross between a quadcopter drone and a bomb. It has artificial intelligence for finding and tracking targets, and might have been used autonomously in the Libyan civil war to attack people. Ministry of Defense of Ukraine, CC BY

High-end proliferation is just as bad, however. Nations could compete to develop increasingly devastating versions of autonomous weapons, including ones capable of mounting chemical, biological, radiological and nuclear arms. The moral dangers of escalating weapon lethality would be amplified by escalating weapon use.

High-end autonomous weapons are likely to lead to more frequent wars because they will decrease two of the primary forces that have historically prevented and shortened wars: concern for civilians abroad and concern for one’s own soldiers. The weapons are likely to be equipped with expensive ethical governors designed to minimize collateral damage, using what U.N. Special Rapporteur Agnes Callamard has called the “myth of a surgical strike” to quell moral protests. Autonomous weapons will also reduce both the need for and risk to one’s own soldiers, dramatically altering the cost-benefit analysis that nations undergo while launching and maintaining wars.

Asymmetric wars – that is, wars waged on the soil of nations that lack competing technology – are likely to become more common. Think about the global instability caused by Soviet and U.S. military interventions during the Cold War, from the first proxy war to the blowback experienced around the world today. Multiply that by every country currently aiming for high-end autonomous weapons.

Undermining the laws of war

Finally, autonomous weapons will undermine humanity’s final stopgap against war crimes and atrocities: the international laws of war. These laws, codified in treaties reaching as far back as the 1864 Geneva Convention, are the international thin blue line separating war with honor from massacre. They are premised on the idea that people can be held accountable for their actions even during wartime, that the right to kill other soldiers during combat does not give the right to murder civilians. A prominent example of someone held to account is Slobodan Milosevic, former president of the Federal Republic of Yugoslavia, who was indicted on charges of crimes against humanity and war crimes by the U.N.’s International Criminal Tribunal for the Former Yugoslavia.

But how can autonomous weapons be held accountable? Who is to blame for a robot that commits war crimes? Who would be put on trial? The weapon? The soldier? The soldier’s commanders? The corporation that made the weapon? Nongovernmental organizations and experts in international law worry that autonomous weapons will lead to a serious accountability gap.

To hold a soldier criminally responsible for deploying an autonomous weapon that commits war crimes, prosecutors would need to prove both actus reus and mens rea, Latin terms describing a guilty act and a guilty mind. This would be difficult as a matter of law, and possibly unjust as a matter of morality, given that autonomous weapons are inherently unpredictable. I believe the distance separating the soldier from the independent decisions made by autonomous weapons in rapidly evolving environments is simply too great.

The legal and moral challenge is not made easier by shifting the blame up the chain of command or back to the site of production. In a world without regulations that mandate meaningful human control of autonomous weapons, there will be war crimes with no war criminals to hold accountable. The structure of the laws of war, along with their deterrent value, will be significantly weakened.

A new global arms race

Imagine a world in which militaries, insurgent groups and international and domestic terrorists can deploy theoretically unlimited lethal force at theoretically zero risk at times and places of their choosing, with no resulting legal accountability. It is a world where the sort of unavoidable algorithmic errors that plague even tech giants like Amazon and Google can now lead to the elimination of whole cities.

In my view, the world should not repeat the catastrophic mistakes of the nuclear arms race. It should not sleepwalk into dystopia.

This is an updated version of an article originally published on September 29, 2021.

James Dawes does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

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.

Sure the average video gamer is 34 years old, but the most active group is boys under 18, a group famously resistant to reading. Here is the RTSF Top 10 recommendations of books that have robots plus enough world building to rival Halo or Doom and lots of action or puzzles to solve. What’s even cooler is that you can cleverly use the “Topics” links to work in some STEM talking points by asking things like: do you think it would be easy reprogram cars to hit pedestrians instead of avoiding them? How would you fool a security drone? or Do you think robots should have the same rights as animals? But you may want to read them too, the first six on the list are books that I routinely recommend to general audiences and people tell me how much they loved them.

Head On – The rugby-like game in the book, Hilketa, played with real robots, is the best multiplayer game that never was. And paralyzed people have an advantage! (FYI: a PG-13 discussion of tele-sex through robots). Good for teachable moments about teleoperation.

Robopocalypse– Loved World War Z and read the book? They’ll love this more and it’s largely accurate about robots. Good for teachable moments about robot autonomy.

The Murderbot Diaries (series)- Delightfully snarky point of view of a security robot trying to save clueless scientists from Aliens-like corpos and creatures. Good for teachable moments about software engineering and whether intelligence systems would need a governor to keep them in line.

The Electric State– This is sort of a graphic novel the way Hannah Gadsby is sort of a comedian- it transcends the genre. Neither the full page illustrations nor the accompanying text tell the whole story of the angry teenage girl and her robot trying to outrun the end of the world. Like an escape room, you have to put the text and images together to figure out what is going on. Good for teachable moments about autonomy.

Tales from the Loop– the graphic novels, two in the series, are different from the emo Amazon streaming series. The books are much more suited to a teenage audience who love world building and surprising twists. Good for teachable moments about bounded rationality.

Kill Decision– Scarily realistic description of killer drones, with cool Spec Ops guy who has two ravens with call out to Norse mythology. Good for teachable moments about swarms (aka multi-robot systems).

Robots of Gotham– It’s sort of Game Lit without being based on a video game. Excellent discussion of how computer vision/machine learning works. Good for teachable moments about computer vision and machine learning.

The Andromeda Evolution– Helps if they’ve seen or read the original Andromeda Strain movie, but it can be read as a stand-alone. This commissioned sequel is a worthy addition. Good for teachable moments about drones and teleoperation.

Machinehood – A pro-Robots Rights group is terrorizing the world, nice discussion of ethics amid a lot of action- no boring lectures. Good for teachable moments about robot ethics.

The Themis Files– A earnest girl finds an alien Pacific Rim robot and learns to use it to fight evil giant piloted mecha invaders while shadowy quasi-governmental figures try to uncover its origins. Good for teachable moments about exoskeletons.

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 illustration, NASA’s Ingenuity Mars Helicopter stands on the Red Planet’s surface as NASA’s Perseverance rover (partially visible on the left) rolls away. Credits: NASA/JPL-Caltech

The difference between robotics and automation is almost nonexistent and yet has a huge difference in everything from trade shows, marketing, publications to academic conferences and journals. This week, the difference was expressed as an opportunity in the Dear Colleague Letter below from Professor Ken Goldberg, CITRIS CPAR and UC Berkeley, who suggested that students whose papers were rejected from ICRA, revise them for CASE, the Conference on Automation Science and Engineering. This opportunity was expressed beautifully in the title quote from Professor Raja Chatila, ex President of IEEE Robotics and Automation Society and current President of IEEE Global Society on Ethics of Autonomous and Intelligent Systems. “One robot on Mars is robotics, ten robots on Mars is automation.”

Dear Colleagues,

Over 2000 papers were declined by ICRA today, including many that can be
effectively revised for another conference such as IEEE CASE (deadline 15
March).

IEEE CASE, the annual Conference on Automation Science and Engineering, is
a major IEEE conference that is one of three fully-supported IEEE
conferences in our field (with ICRA and IROS).

In 2021 CASE will be held 23-27 August.  It will be hybrid, with a live
component in Lyon France and an online component:
https://case2021.sciencesconf.org/

IEEE CASE was founded in 2006 so is smaller but growing quickly.  The
acceptance rate for the last CASE was about 56%, higher than ICRA 2021
(48%), IROS, or RSS.  I consider this a feature not a bug: it is an
excellent venue for exploratory and novel projects.

IEEE CASE continues the classic conference model of featuring a 10-15 min
oral presentation of each paper in contrast to poster sessions.  This is
particularly exciting for students, who get the valuable experience of
lecturing and fielding questions in front of an audience of peers.

IEEE CASE also has a tradition of spotlighting papers nominated for awards
such as Best Paper, Best Student Paper, etc.  Each nominated paper is
presented in special single session track on Day 1, where everyone at the
conference attends and there is a lively Q&A led by judges.

IEEE CASE emphasizes Automation.  Automation is very closely related to
Robotics. There is substantial overlap, but Automation emphasizes
efficiency, robustness, durability, safety, cost effectiveness. Automation
also includes topics such as optimization and applications such as
transportation and mfg. I like how RAS President Raj Chatila summed up the
relationship 10 years ago: “One robot on Mars is robotics, ten robots on
Mars is automation.”

In China there are over 100 university departments
focused on Automation.  The impact factor for the IEEE Transactions on
Automation Science and Engineering (T-ASE) this year is on par with T-RO
and higher than IJRR. Automation is important to put robotics into
practice.

Ken Goldberg

Professor, Industrial Engineering and Operations Research

William S. Floyd Jr. Distinguished Chair in Engineering, UC Berkeley

Director, CITRIS People and Robots Lab

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

It’s Ada Lovelace Day and once again we’re delighted to introduce you to “30 women in robotics you need to know about”! From 13 year old Avye Couloute to Bala Krishnamurthy who worked alongside the ‘Father of Robotics’ Joseph Engelberger in the 1970s & 1980s, these women showcase a wide range of roles in robotics. We hope these short bios will provide a world of inspiration, in our eighth Women in Robotics list! 

In 2020, we showcase women in robotics in China, Japan, Malaysia, Israel, Australia, Canada, United States, United Kingdom, Switzerland, Israel, Norway, Spain, The Netherlands, India and Iran. There are researchers, industry leaders, and artists. Some women are at the start of their careers, while others have literally written the book, the program or the standards.

CAPE CANAVERAL, Fla. – Members of the Kennedy Space Center government-industry team rise from their consoles within the Launch Control Center to watch the Apollo 11 liftoff through a window. Photo credit: NASA

We publish this list because the lack of visibility of women in robotics leads to the unconscious perception that women aren’t making newsworthy contributions. We encourage you to use our lists to help find women for keynotes, panels, interviews etc. Sadly, the daily experience of most women in robotics still looks like this famous NASA control room shot from the 1969 Apollo 11 moon landing, with one solitary woman in the team. It has taken sixty years for the trailblazers like Joann Morgan, Katherine G. Johnson, Dorothy Vaughan, Mary Jackson and Poppy Northcutt to become well known. And finally now we have a woman, Charlie Blackwell-Thompson, serving as Launch Director for the upcoming Artemis Mission, and another, Gwynne Shotwell, serving as President and COO of SpaceX.

In celebration of Women’s History Month, the “Women of Launch Control” working in Exploration Ground Systems take time out of their Artemis I launch planning to pose for a photo in Firing Room 1 of the Launch Control Center at NASA’s Kennedy Space Center in Florida on March 4, 2020. Artemis I will be the first integrated flight test of the Orion spacecraft and Space Launch System rocket, the system that will ultimately land the first woman and the next man on the Moon. Photo credit: NASA/Glenn Benson

In 2019, women still accounted for less than a quarter (23.6%) of those working in natural and applied sciences and related occupations. In these occupations, women earned, on average, \$0.76 to every \$1.00 earned by men in annual wages, salaries, and commissions in 2018. [ref Catalyst.org ]

This issue is even more pervasive and devastating if you are a person of color. We have always strived to showcase a wide range of origins and ethnicities in our annual list, and this year, as well as four African American roboticists, our list includes the first African American female CEO of a company valued over $1Billion USD. This is just a small step forward, but we’re pleased to announce the recent launch of the Black in Robotics organization, as well as greater recognition of the citation problem.

The citation problem is expected to significantly disadvantage women and people of color due to the historical lack of women followed by the recent growth of large scientific teams, multiplying exclusion. For example, Nature recently published a paper on the impact of NumPy, a significant scientific resource. NumPy was originally developed by many contributors. But the authoritative citation is likely to belong to this description paper, which has 26 authors, all male. [ref Space Australia]

On a positive note, many individuals and organizations intentionally try to reverse this bias. For example, Tulane University just published a guide to help you calculate how much of your reading list includes female authors and a citation guide, similar to the CiteHer campaign from BlackComputeher.org.  And as Bram Vanderborght, editor of IEEE Robotics and Automation magazine pointed out in the March 2020 issue, “Scientists are starting to consider how gender biases materialize in physical robots. The danger is that robot makers, consciously or not, may reinforce gender stereotypes and inadvertently create even greater deterrents for young, underrepresented people interested in joining our field.”

We hope you are inspired by these profiles, and if you want to work in robotics too, please join us at Women in Robotics. We are now a 501(c)(3) non-profit organization, but even so, this post wouldn’t be possible if not for the hard work of volunteers; Andra Keay, Hallie Siegel, Sabine Hauert, Sunita Pokharel, Ioannis Erripis, Ron Thalanki and Daniel Carrillo Zapata.

Fatemeh Pahlevan Aghababa CTO and co-founder at Intelize | Technical Committee Head of International Robocup Federation | Technical Lead at Farakav co. Fatemeh Pahlevan Aghababa is CTO and co-founder of Intelize startup which provides AI-powered solutions such as Ribona whose mission is to make a difference in Industry 4.0 via novel technologies. She is an active researcher in intelligent systems and cognitive robotic fields, and received the Silvia Coradeschi Robocup Award for “the young female scientists with distinguishing research in AI and robotics”. She is the Technical Committee Head of the International Robocup Federation, and has won the RoboCup World Championship for two consecutive years in Rescue Simulation League. Pahlevan was named among Rising Stars in VentureBeat’s Women in AI Awards.

Ariel Anders Roboticist – Robust.AI Ariel Anders is a black feminist roboticist who enjoys spending time with her family and artistic self-expression. Anders is the first roboticist hired at Robust.AI, an early stage robotics startup building the world’s first industrial grade cognitive engine. Anders received a BS in Computer Engineering from UC Santa Cruz and her Doctorate in Computer Science from MIT, where she taught project-based collaborative robotics courses, developed an iOS app for people with vision impairment, and received a grant to install therapy lamps across campus. Her research focused on reliable robotic manipulation with the vision of enabling household helpers.

Carlotta Berry Professor of Electrical and Computer Engineering – Rose-Hulman Institute of Technology Carlotta Berry, professor at Rose-Hulman Institute of Technology, helped found and co-directs the Rose Building Undergraduate Diversity (ROSE-BUD) program with a goal of recruitment, retention and professional development of historically marginalized and minoritized populations in computer science, computer, electrical and computer engineering. Berry also worked with faculty to create Rose-Hulman’s first multidisciplinary minor in robotics and has judged and organized FIRST Robotics competitions. Berry’s research is focused on educational mobile robotics, human-robot interaction and interfaces. Berry has also mentored and organized robotics programs for Girl Scout groups and visited elementary, middle, and high schools to motivate student’s interests in STEM.

Avye Couloute Gen Arm 2Z Ambassador/ Founder of Girls into Coding Avye Couloute is a multitalented 13-year-old, with a passion for coding from the age of 7. Avye founded Girls into Coding which is a volunteer event series empowering girls with the tools and skills to get in the tech industry. She has facilitated online and offline robotics workshops as well as raising money to provide textbooks and computer starter kits. Avye, a Gen Arm 2Z Ambassador, is also the recipient of The Diana Award’s Legacy Award and the winner of the 2020 FDM Everywoman in Technology Awards.

Yael Edan Professor, Head of ABC Robotics Lab – Ben-Gurion University of the Negev (BGU) Yael Edan, professor at Ben-Gurion University of the Negev is Head of the Agricultural, Biological, and Cognitive Robotics Initiative. Her research is specifically focused on agricultural robotics, human-robot cooperation, systems engineering of robotics systems and adaptive sensor selection and fusion. Edan has made major contributions in the introduction and application of intelligent automation and robotic systems to the field of agriculture with several patents. Edan has been involved in many international projects, is a member of IEEE RAS and a member of ASABE where she was the Chair of the Flexible Automation and Robotics/Mechatronics and BioRobotics committees.

Autumn Edwards Professor – Western Michigan University Autumn Edwards researches how ontological considerations, or people’s beliefs about the nature of communicators and of communication, both shape and are shaped by their interactions with social robots, as an emergent class of digital interlocutor. She is co-founder/co-director of the Communication and Social Robotics Labs (combotlab.org), Professor of Communication at Western Michigan University, and founding Editor-in-Chief of the journal Human-Machine Communication. She inspires passionate support from students and fellow staff, receiving the University Distinguished Teaching Award (2014), the Kim Giffin Research Award from the University of Kansas and was designated a Claude Kantner Research Fellow at Ohio University.

Marwa ElDiwiny Researcher – University of Twente Marwa ElDiwiny is a PhD researcher at Vrije Universiteit Brussel whose current research focus is on modelling and simulating self-healing soft materials for industrial applications. Her master thesis was UAV anti-stealth technology for safe operation. She has worked as a research engineer at Inria, Lille nord Europe, research scholar at Tartu Institute of Technology and lecturer in the Mechatronics and Industrial Robotics Program at Minia university, Egypt. Eldiwiny hosts the IEEE RAS Soft Robotics Podcast where researchers from both academia and industry are interviewed about the soft robotics field, with over 94 episodes available.

Aicha Evans CEO – Zoox Aicha Evans is the CEO of Zoox, the company developing purpose-built, zero-emission autonomous vehicles with end to end autonomy software stack. Evans is a former senior vice president and chief strategy officer at Intel Corporation where she drove Intel’s long-term strategy to transform from a PC-centric company to a data-centric company. Evans spent 10 years in various engineering management positions at Rockwell Semiconductors, Conexant, and Skyworks, before Intel. Evans is also on the Supervisory Board of SAP and is the first AfricanAmerican female CEO of a billion dollar company.

Elena Garcia Armada Co-Founder – Marsi Bionics. Senior Researcher – Centre for Automation and Robotics Elena Garcia Armada is a tenured researcher at the Spanish National Research Council and co-founder of Marsi Bionics. She is an expert in pediatric exoskeletons optimized for active gait rehabilitation of children suffering from neuromuscular diseases. She has won over 30 awards, is a member of the Innovation and Knowledge Transfer Working Group of the Ministry of Science, Innovation and Universities, a member of the Industrial Activities Board of IEEE Robotics and Automation Society, and on the jury of the Princess of Asturias Scientific and Technical Research Awards. She is passionate about helping children walk again.

Keiko Homma Senior Researcher Service Robotics Research Team, Robot Innovation Research Center – AIST Keiko Homma is a senior researcher at AIST in the Assistive Robotics Research Team, Human Augmentation Research Center. She received B.Sc and Ph.D degrees in Engineering from the University of Tokyo, and was a visiting researcher at Aalto University. Her current research interests include assistive and robot systems, particularly their safety aspects, and has developed a risk assessment tool for assistive robots and test dummies for exoskeleton typed physical assistant robots. She is also an IEEE RAS Distinguished Lecturer.

Michelle J. Johnson Associate Professor & Director of Rehabilitation Robotics Lab – University of Pennsylvania Michelle J. Johnson is an associate professor of the departments of physical medicine and rehabilitation, and of bioengineering at University of Pennsylvania, and directs the Rehabilitation Robotic Research and Design Laboratory in the Pennsylvania Institute of Rehabilitation Medicine which is affiliated with the General Robotics, Automation, Sensing & Perception (GRASP) Lab. Johnson’s PhD is in Mechanical Engineering from Stanford University. Her research specialization is in the design, development, and therapeutic use of novel, affordable, intelligent robotic assistants to quantify upper limb motor function in adults and children at risk of or with brain injury. She is also a US Fulbright Scholar.

Hadas Kress-Gazit Associate Professor – Sibley School of Mechanical and Aerospace Engineering, and ICRA2022 Program Chair Hadas Kress-Gazit is an Associate Professor at the Sibley School of Mechanical and Aerospace Engineering at Cornell University. Her research focuses on formal methods for robotics and automation and more specifically on synthesis for robotics – automatically creating verifiable robot controllers for complex high-level tasks. Her group explores different types of robotic systems including modular robots, soft robots and swarms, and synthesizes ideas from robotics, formal methods, control, hybrid systems and human-robot interaction. She has received multiple recognitions and awards for her research, her teaching and her advocacy for groups traditionally underrepresented in robotics.

Bala Krishnamurthy CEO – Mobile Software Inc Bala Krishnamurthy is a pioneer in robotics. From her early days at Unimation to her current role as CEO of Mobile Software. Krishnamurthy has over 40 years of experience in researching, designing and developing embedded real-time systems and software. She adapted the VAL language designed for PUMA robots to the hydraulic Unimate robots, then led the team at Engelberger’s HelpMate Robotics developing the software that allowed autonomous courier robots to navigate throughout hospitals. Krishnamurthy was also a member of NASA’s Office of Exploration Systems research proposal review panel for Human and Robotic Technology and authored dozens of technical articles.

Sam MacDonald President – Deep Trekker Sam MacDonald is President and cofounder of Deep Trekker, a Canadian made ROV (Remotely Operated Vehicle) for economical underwater robotic exploration. After working in automation and at Blackberry in marketing, Macdonald founded her own marketing leadership company. She then started Deep Trekker in 2009 taking it from garage to global company. Today, Deep Trekker has expanded into a host of verticals, including aquaculture, commercial diving, salvage, military, first responders, oil and gas, energy, marine surveying, wastewater, infrastructure and recreation, and sells products in 80 countries.

Karon Maclean Professor – University of British Columbia, Director – SPIN The Sensory Perception and Interaction Research Group Karon MacLean is a Professor in Computer Science at UBC, with degrees in Biology and Mechanical Engineering and time spent as a professional robotics engineer and haptics / interaction researcher at Interval Research, Palo Alto. MacLean’s research specializes in haptic interaction: cognitive, sensory and affective design for people interacting with the computation we touch, emote and move with and learn from, from robots to touchscreens and the situated environment. MacLean leads UBC’s Designing for People interdisciplinary research cluster and is Special Advisor, Innovation, and Knowledge Mobilization to UBC’s Faculty of Science.

Chris Macnab Associate Professor – University of Calgary Chris Macnab is an associate professor at the University of Calgary. She earned a BEng in Engineering Physics from the Royal Military College of Canada and a PhD from the University of Toronto Institute for Aerospace Studies, where she investigated control of flexible-joint space robots using neural networks. Macnab has worked as a control systems designer for Dynacon Enterprises, as part of a team that developed and tested a control system for an antenna on the space station. She has also worked for CRS Robotics, programming robot movements. Macnab’s specialization is in control systems, motion control, and robotics.

Shauna McIntyre CEO – Sense Photonics Shauna McIntyre is CEO at Sense Photonics, developer of high-performance, scalable 3D vision systems with offices in San Francisco, North Carolina and the UK. She is an automotive industry veteran who has led Google’s automotive services and Google Maps’ automotive programs. Mclntyre was also Chief of Staff for Google’s consumer electronics division and serves on the Board of Directors of Lithia Motors. Mclntyre brings a track record of driving innovation in traditional industries and a long-standing mission to deliver new, breakthrough experiences to industrial and automotive companies through intelligent hardware.

Elena Messina Group Leader, Manipulation & Mobility – National Institute of Standards and Technology (NIST) Elena Messina leads the Intelligent Systems Division’s Manipulation & Mobility Systems Group at the US National Institute of Standards and Technology. She also manages the Engineering Laboratory’s Robotic Systems for Smart Manufacturing Program. Messina founded key efforts to develop test methodologies for measuring performance of robots, which range from long-term use of robotic competitions to drive innovation to consensus standards for evaluating robotic components and systems. Messina has received three Department of Commerce Bronze Medals for Superior Performance and Technical Leadership, and the Edward Bennet Rosa Award for research and development leading to standardized test methods for emergency response robots.

Linda Nagata SciFi Writer, Owner – Mythic Island Press Linda Nagata is a science fiction and fantasy writer from Hawaii, best known for her high-tech science fiction, including the far-future adventure series, Inverted Frontier, and the near-future thriller, The Last Good Man. Nagata is most recognized for her Nanotech Succession series, which is considered exemplary of the Nanopunk genre. Nagata’s work has been nominated for the Hugo, Nebula, Locus, John W. Campbell Memorial, and Theodore Sturgeon Memorial awards. She has also won the Nebula award, and is a two-time Locus award winner.

Samia Nefti-Meziani Founder and Head, Professor – Salford University,University of Salford’s Autonomous Systems and Advances Robotics Research Centre Samia Nefti-Meziani is a Professor at Salford University UK, where she is founder of the ASAR Centre. She is a veteran and one of the pioneers in Soft Robotics and low cost cross-sectorial robotics systems and their application in many sectors including Manufacturing, Nuclear, Space, Autonomous cars and Healthcare. Nefti-Meziani runs several large UK and European research programs, and regularly appears on TV and radio. She is also a Cofounder and Executive Board Member of the UK’s National Robotics Network, former Vice Chairman of IEEE RAS UK and a member of the UK-Government Robotics Growth Partnership.

Nicci Rossouw CEO – Exaptec Nicci Rossouw is CEO and founder of Exaptec, a provider of social, service and telepresence robots. Rossouw specializes in robotics automation solution design and delivery using a Robotics-as-a-Service as a business model. Rossouw has provided robotic telepresence solutions to educational facilities and businesses to augment communication and movement for disabled and incapacitated people. Exaptec was one of Westpac’s top Businesses of Tomorrow winners.

Yulia Sandamirskaya Applications Research Lead, Neuromorphic Computing Lab – Intel Labs Yulia Sandamirskaya is a senior researcher at Intel Labs leading a group developing applications of Intel’s neuromorphic research chips. Sandamirskaya was also a group leader in the Institute of Neuroinformatics at the University of Zurich and ETH Zurich. Sandamirskaya specializes in perception, movement control, memory formation, and learning in embodied neuronal systems, and implemented neuronal architectures in neuromorphic devices, interfaced with robotic sensors and motors. Sandamirskaya has served as the chair of EUCOG, the European Society for Artificial Cognitive Systems, and as coordinator of the NEUROTECH project.

Katherine Scott Developer Advocate – Open Robotics Katherine Scott is a Developer Advocate at Open Robotics, the maintainers of ROS and Ignition Gazebo, and sits on the board of the Open Source Hardware Association. Scott was Cofounder and Software Lead at Tempo Automation, a manufacturer of low-volume electronics, served as Director of Research and Development at Sight Machine, a leader in manufacturing analytics, and worked as a computer vision engineer. Along the way, Scott developed a large part of the SimpleCV Python library, and several innovative software prototypes in the fields of computer vision, graphics, augmented reality, and robotics.

Hazlina Selamat Associate Professor – Universiti Teknologi Malaysia, Director – Apt Touch Sdn Bhd, Research Fellow – Centre for Artificial Intelligence and Robotics (CAIRO) Hazlina Selamat is an Associate Professor at Universiti Teknologi Malaysia, an active member of the university’s Centre for Artificial Intelligence & Robotics (CAIRO), Founding Member for the Malaysian Society for Automatic Control Engineers (MACE). She is the co-founder and Director of Apt Touch Sdn Bhd, a company that designs, develops, and manufactures fuel injection systems for small engines. Her projects include developing the optimization engine for an energy management system, and the pre-commercialization of a retrofit fuel injection kit for carburetor engines. Her interests are adaptive control, online system identification and application of control to the high-order and nonlinear systems.

Elizabeth Sklar Research Director and Professor – Agrirobotics and University of Lincoln and Kings College of London Elizabeth Sklar is a Professor at the University of Lincoln and King’s College London, UK, where she heads the Centre for Robotics Research. Sklar is a veteran of MIT/Lincoln Lab, a Founder Trustee of the RoboCup Federation, a Fulbright Scholar 2013-2014, former member of the Board of Directors for the IFAAMAS, and sits on the Editorial Board for the Journal of Autonomous Agents & MultiAgent Systems. Sklar specializes in interaction for multi-robot and human-robot systems, and shared decision making by applying computational argumentation-based dialogue to human-agent and human-robot systems.

Allison Thackston Staff Software Engineer, Manager Shared Autonomy – Waymo Allison Thackston has a passion for solving practical problems with robotics. She currently leads a team solving some of the toughest problems in self-driving technology at Waymo. Thackston was previously the Engineering Lead and Manager of the Shared Autonomy Robotics team at Toyota Research Institute and was the Lead Engineer for Robotic Perception on Robonaut 2, the first humanoid robot on the International Space Station. Thackston is experienced in robust task and motion planning, manipulation, and applied computer vision.

Liane Thompson CEO – Aquaai Norway AS Liane Thompson is the CEO of Aquaai, a marine robotics company that uses data to improve our understanding of ocean health by collecting and delivering real-time visual and environmental data to a web-based dashboard. Thompson’s company has grown from a daughter’s idea into a full-fledged production, with a global VC backed brand. Norwegian salmon producer Kvarøy Fiskeoppdrett is working with Aquaai’s bio-inspired finned robots to monitor fish behavior and conditions at their fish farms. Aquaai has been awarded Top Aquaculture Innovation Award 2019 Fish2.0 Global Forum, an Innovation Norway grant, as well as being selected to Australia’s TekFish Challenge.

Claire Tomlin Charles A. Desoer Professor of Electrical Engineering and Computer Sciences – UC Berkeley Claire Tomlin is a professor at the University of California Berkeley, where she holds the Charles A. Desoer Chair in Engineering. She specializes in control of safety-critical systems applied to air traffic control automation and unmanned air systems, and is known for her pioneering work developing methods for verifying the safe range of operation for hybrid systems. Tomlin is a MacArthur Foundation Fellow, a Fellow of the IEEE and AIMBE, a member of the US National Academy of Engineering and the American Academy of Arts and Sciences. She received the IEEE Transportation Technologies Award, and an honorary doctorate from KTH.

Janet Wiles Professor – University of Queensland Janet Wiles is Professor of Complex and Intelligent Systems at the University of Queensland, where she leads a multidisciplinary team studying social robotics and has pioneered the use of robots in Indigenous communities. She also leads the Future Technologies thread at the ARC Centre of Excellence for the Dynamics of Language developing Human Centred AI for language technologies and making AI tools available to a broad range of users. She is in the Human Centred Computing discipline in UQ’s School of Information Technology and Electrical Engineering. Wiles specializes in human-robot interaction, language technologies, bio-inspired computation, visualization and artificial intelligence, complex systems modelling in biology and neuroscience, human memory, language, and cognition.

Rong Xiong Professor at Zhejiang University – Institute of Cyber Systems and Control, Zhejiang University Rong Xiong is a professor and head of the Robotics Laboratory at Zhejiang University, co-director of the ZJU-UTS Joint Center on Robotics Research, international trustee of RoboCup, and expert member for a special project on intelligent robots. Ministry for Science and Technology, China. Her group is known for their work developing humanoid robots that can play table tennis with each other and also compete with human players. Xiong received first prizes at the Scientific and Technological Award, and Teaching Achievement of Zhejiang Province. Xiong’s research specialization includes visual recognition, simultaneous localization and mapping, motion planning, and control for robots.

Want to keep reading? There are 180 more stories on our 2013 to 2019 lists. Why not nominate someone for inclusion next year!

And we encourage #womeninrobotics and women who’d like to work in robotics to join our professional network at http://womeninrobotics.org

Many executives naively group AI into its own category with SmartCity inventions as a niche use case. However as Akio Toyoda, Chief Executive of Toyota, presented at CES it is the reverse. The “Woven City,” initiative by the car manufacturer illustrates that autonomous cars, IoT devices and intelligent robots are subservient to society and, hence, require their own “living laboratory”. Toyoda boldly described a novel construction project for a city of the future 60 miles from Tokyo, “With people, buildings and vehicles all connected and communicating with each other through data and sensors, we will be able to test AI technology, in both the virtual and the physical world, maximizing its potential. We want to turn artificial intelligence into intelligence amplified.” Woven City will include 2,000 residents (mostly existing and former employees) on a 175 mile acre site (formerly a Toyota factory) at the foothills of Mount Fuji, providing academics, scientists and inventors a real-life test environment.

Toyota has hired the Dutch architectural firm Bjarke Ingels Group (BIG) to design its urban biosphere. According to Bjarke Ingels, “Homes in the Woven City will serve as test sites for new technology, such as in-home robotics to assist with daily life. These smart homes will take advantage of full connectivity using sensor-based AI to do things automatically, like restocking your fridge, or taking out your trash — or even taking care of how healthy you are.” While construction is set to begin in 2021, the architect is already boasting: “In an age when technology, social media and online retail is replacing and eliminating our natural meeting places, the Woven City will explore ways to stimulate human interaction in the urban space. After all, human connectivity is the kind of connectivity that triggers wellbeing and happiness, productivity and innovation.”

Walking back into the LVCC from the Westgate, I heard Toyoda’s keynote in my head – “mobility for all” – forming a prism in which to view the rest of the show. Looking past Hyundai/Uber’s massive Air Taxi and Omron’s ping-pong playing robot; thousands of suited executives led me under LG’s television waterfall to the Central Hall. Hidden behind an out of place Delta Airlines lounge, I discovered a robotics startup already fulfilling aspects of the Woven City. Vancouver-based A&K Robotics displayed a proprietary autonomous mobility solution serving the ballooning geriatric population.  The U.S. Census Bureau projects that citizens over the ages of 65 will double from “52 million in 2018 to 95 million by 2060” (or close to a quarter of the country’s population). This statistic parallels other global demographic trends for most first world countries. In Japan, the current population of elderly already exceeds 28% of its citizenry, with more than 70,000 over the age of 100. When A&K first launched its company it marketed conversion kits for turning manual industrial machines into autonomous vehicles. Today, the Canadian team is applying its passion for unmanned systems to improve the lives of the most vulnerable – people with disabilities. As Jessica Yip, A&K COO, explains, “When we founded the company we set out to develop and prove our technology first in industrial environments moving large cleaning machines that have to be accurate because of their sheer size. Now we’re applying this proven system to working with people who face mobility challenges.” The company plans to initially sell its elegant self-driving wheelchair (shown below) to airports, a $2 billion opportunity serving 63 million passengers worldwide.

In the United States the federal government mandates that airlines provide ‘free, prompt wheelchair assistance between curbside and cabin seat’ as part of the 1986 Air Carrier Access Act. Since passing the bill, airport wheelchair assistance has mushroomed to an almost unserviceable rate as carriers struggle to fulfill the mandated free service. In reviewing the airlines performance Eric Lipp, of the disability advocacy group Open Doors, complains, “Ninety percent of the wheelchair problems exist because there’s no money in it. I’m not 100% convinced that airline executives are really willing to pay for this service.” In balancing profits with accessibility, airlines have employed unskilled, underpaid workers to push disabled fliers to their seats. A&K’s solution has the potential of both liberating passengers and improving the airlines’ bottomline performance. Yip contends, “We’re embarking on moving people, starting in airports to help make traveling long distances more enjoyable and empowering.”

A&K joins a growing fleet of technology companies in tackling the airport mobility issue. Starting in 2017, Panasonic partnered with All Nippon Airways (ANA) to pilot self-driving wheelchairs in Tokyo’s Narita International Airport. As Juichi Hirasawa, Senior Vice President of ANA, states: “ANA’s partnership with Panasonic will make Narita Airport more welcoming and accessible, both of which are crucial to maintaining the airport’s status as a hub for international travel in the years to come. The robotic wheelchairs are just the latest element in ANA’s multi-faceted approach to improving hospitality in the air and on the ground.” Last December, the Abu Dhabi International Airport publicly demonstrated for a week autonomous wheelchairs manufactured by US-based WHILL. Ahmed Al Shamisi, Acting Chief Operations Officer of Abu Dhabi Airports, asserted: “Convenience is one of the most important factors in the traveller experience today. We want to make it even easier for passengers to enjoy our airports with ease. Through these trials, we have shown that restricted mobility passengers and their families can enjoy greater freedom of movement while still ensuring that the technology can be used safely and securely in our facilities.” Takeshi Ueda of WHILL enthusiastically added, “Seeing individuals experience the benefits of the seamless travel experience from security to boarding is so rewarding, and we are eager to translate this experience to airports across the globe.”

At the end of Toyoda’s remarks, he joked, “So by now, you may be thinking has this guy lost his mind? Is he a Japanese version of Willie Wonka?” As laughter permeated the theater, he excitedly confessed, “Perhaps, but I truly believe that THIS is the project that can benefit everyone, not just Toyota.” As I flew home, I left Vegas more encouraged about the future, entrepreneurs today are focused on something bigger than robots. In the words of Yip, “As a company we’re looking to serve all people, and are strategically focused on airports as a step towards smart cities where everyone has the opportunity to participate fully in society in whatever way they are interested. Regardless of age, physical challenges, or other, we want people to be able to get out of their homes and into their communities. To be able to see each other, interact, go to work or travel whenever they want to.”

Sign up today for next RobotLab event forum on Automating Farming: From The Holy Land To The Golden State, February 6th in New York City. 

The elephant in the room loomed large two weeks ago at the inaugural Internet of Things Consortium (IoTC) Summit in New York City. Almost every presentation began apologetically with the refrain, “In a 5G world…” practically challenging the industry’s rollout goals. At one point Brigitte Daniel-Corbin, IoT Strategist with Wilco Electronic Systems, sensed the need to reassure the audience by exclaiming, ‘its not a matter of if, but when 5G will happen!’ Frontier Tech pundits too often prematurely predict hyperbolic adoption cycles, falling into the trap of most soothsaying visions. The IoTC Summit’s ability to pull back the curtain left its audience empowered with a sober roadmap forward that will ultimately drive greater innovation and profit.

The industry frustration is understandable as China announced earlier this month that 5G is now commercially available in 50 cities, including: Beijing, Shanghai and Shenzhen. In fact, the communist state beat its own 2020 objectives by rolling out the technology months ahead of plan. Already more than 10 million cellular customers have signed up for the service. China has made upgrading its cellular communications a national priority with more than 86,000 5G base stations installed to date and another 130,000 5G base stations to go live by the end of the year. In the words of Wang Xiaochu, president of China Unicom, “The commercialization of 5G technology is a great measure of [President] Xi Jinping’s strategic aim of turning China into a cyber power, as well as an important milestone in China’s information communication industry development.” By contrast the United States is still testing the technology in a number of urban zones. If a recent PC Magazine review of Verizon’s Chicago pilot is any indication of the state of the technology, the United States is very far from catching up. As one reporter complains, “I walked around for three hours and found that coverage is very spotty.”

Last year, President Trump donning a hardhat declared “My administration is focused on freeing up as much wireless spectrum as needed [to make 5G possible].” The importance of Trump’s promotional event in April could not be more understated, as so much of the future of autonomous driving, additive manufacturing, collaborative robotics, shipping & logistics, smart city infrastructure, Internet of Things (IoT), and virtual & augmented reality relies on greater bandwidth. Most experts predict that 5G will offer a 10 to 100 times improvement over fourth generation wireless. Els Baert of NetComm explains, “The main advantage that 5G offers over 4G LTE is faster speeds — primarily because there will be more spectrum available for 5G, and it uses more advanced radio technology. It will also deliver much lower latency than 4G, which will enable new applications in the [Internet of Things] space.” Unfortunately, since Trump’s photo op, the relationship with China has worsened so much that US carriers are now blocked from doing business with the largest supplier of 5G equipment, Huawei. This leaves the United States with only a handful of suppliers, including market leaders Nokia and Ericsson. The limited supply chain is exasperated by how little America is spending on upgrading its telecommunications, according to Deloitte “we conclude that the United States underspent China in wireless infrastructure by $8 billion to $10 billion per year since 2015.”

The current state of the technology (roadblocks and all) demands fostering an innovation ecosystem today that parallels the explosion of new services for the 5G economy. As McKinsey reports there are more than 25 billion connected IoT devices currently, which is estimated to grow to more than 75 billion by 2025 with the advent of fifth generation wireless. The study further cites, “General Electric projects that IoT will add $10 to $15 trillion to a worldwide Global Domestic Product (GDP) growth by 2030. To put that into perspective, that number is equivalent to China’s entire current economy.” Regrettably, most of the available 5G accelerators in the USA are built to showcase virtual and augmented reality instead of fostering applications for the larger opportunity of business-to-business services. According to Business Insider “IoT solutions will reach $6 trillion by 2021,” across a wide spectrum of industries, including: healthcare, manufacturing, logistics, energy, smart homes, transportation and urban development. In fact, hardware will only account for about one-third of the new revenues (and VR/AR headsets comprise considerably less).

It is challenging for publicly traded companies (like T-Mobile, Verizon & AT&T), whose stock performance is so linked to the future of next generation wireless. Clearly, market makers are overly excited by the unicorns of Oculus (acquired by Facebook for $2 billion in 2014) and Magic Leap (valued at $4.5 billion in 2016) more than IoT sensors for robotic recycling, agricultural drones, and fuel efficient rectors. However, based upon the available data, the killer app for 5G will be found in industry not digital theatrics. This focus on theatrics is illustrated in one of the few statements online by Verizon’s Christian Guirnalda, Director of its 5G Labs, boasting “We’re literally making holograms here using a dozen of different cameras in a volumetric capture studio to create near real-time images of what people and products look like in 3D.” A few miles north of Verizon 5G Labs, New York City’s hospitals are overcrowded with patients and data leading to physical and virtual latency issues. Verizon could enable New York’s hospitals with faster network speeds to treat more patients in economically challenged neighborhoods remotely. Already, 5G threatens to exasperate the digital divide in the United States by targeting affluent communities for its initial rollout. By investing in more high-speed telemedicine applications, the telecommunications giant could potentially enable less privileged patients access to better care, which validates the need for increased government spending. Guirnalda’s Lab would be better served by applying the promise of 5G to solve these real-life urban challenges from mass transit to food scarcity to access to healthcare.

The drawback with most corporate 5G incubators is their windows are opaque – forcing inventors to experiment inside, while the real laboratory is bustling outside. The United Nations estimates by 2050 seventy percent of the world’s population will be urban. While most of this growth will take place in developing countries (i.e., Africa and Asia) already 80% of global GDP is generated in cities. The greatest challenge for the 21st century will be managing the sustainable development of these populations. At last month’s UN “World Cities Day,” the diplomatic body stated that 5G “big data technologies and cloud-computing offer the potential to enhance urban operations, functions, services, designs, strategies and policies.” The UN’s statement did not fall on deaf ears, even President Trump strained to comfort his constituents last month with the confession, “I asked Tim Cook to see if he could get Apple involved in building 5G in the U.S. They have it all – Money, Technology, Vision & Cook!”

Going to CES? Join me for my panel on Retail Robotics January 8th at 10am, Las Vegas Convention Center. 

Europe is focussed on making robots that work for the benefit of society. This requires empowering future roboticists and users of all ages and backgrounds. In its 9th edition, the European Robotics Week (#ERW2019) is expected to host more than 1000 events across Europe. Over the years, and over 5,000 events, the organisers have learned a thing or two about reaching the public, and ultimately making the robots people want.

Demystify robotics

For many, robots are only seen in the media or science fiction. The robotics community promises ubiquitous robots, yet most people don’t encounter robots in their work or daily lives. This matters. The Eurobarometer 2017 survey of attitudes towards the impact of digitisation found that the more familiar people are with robots, the more positive they are about the technology. A recent workshop for ERW organisers highlighted the “importance of being able to touch, feel, see and enjoy the presence of robots in order to remove the ‘fear factor’ and improve the image of robots.” People need to interact with real robots to understand their potential, and limitations.

Bring robots to public places

Most robotics events happen where roboticists and their robots already are, in universities and industry. This works well for those who show interest in the field, and have the means to attend. To reach a broader audience, robots need to be brought to public places, such as city centres, or shopping malls. ERW organisers said “don’t expect ordinary people to come to universities.” In Ghent Belgium for example, space was found in the city library to give visitors an opportunity to interact with robots. More recently, the Smart Cities Robotics (SciRoc) challenge held an international robot competition in a shopping mall in the UK.

Tackle global challenges

Robots have a role to play in tackling today’s most pressing challenges, whether it’s the environment, healthcare, assistive living, or education. Robots can also improve efficiencies in industry and avoid 4D (dangerous, dirty, difficult, drudgerous) jobs. This is not often explicitly highlighted, with robots presented for the sake of it as fun gadgets, instead of useful tools. By positioning robots as the helpers of tomorrow, we empower users to imagine their applications, and roboticists receive meaningful feedback on their use. Such applications may also be more exciting for a broader diversity of people.

The ‘Blue-Eyed Dragon’ Robot by Biljana Vicković (with the University of Belgrade, Mihajlo Institute, Robotics Laboratory Belgrade, Serbia) for example introduced an innovative and socially useful robotic artwork into a public space with a tin recycling function. It integrates robotics into an artwork with a demonstrable ecological, social and cultural impact. “The essence of this innovative work of art is that it enables the public to interact with it. As such people are direct participants and not merely an audience. In this way contemplation is replaced by action.” says its creator.

Tell stories about people who work with robots

Useful robots will ultimately be embedded in society, our work, our lives. Their role is often presented from the developers’s or industry’s perspective. This leaves the public with the sense that robots are being “done to them”, rather than with them. By bringing the users in the discussion, we hear stories of how they use the technology, what their hopes and concerns are, and ultimately design better robots and inspire future users to make use of robots themselves.

Bring a diversity of people together 

Making robots requires a large range of backgrounds, from social sciences, law, and business, to hardware and software engineering. Domain expertise, will also be key. Assistive robots will require input from nurse carers for example. Engaging with a diverse population of makers and users will help ensure the technology is developed for everyone. The ERW2019 central event in Poznan features a panel dedicated to women in digital and robotics.
Carmela Sánchez from Hisparob in Spain says “this year, our motto for ERW is Robotic Thinking and Inclusion. We focus on how robotics and computational thinking can help inclusion: inclusion of different abilities, social, and economic backgrounds, and genders.”

Avoid hype and exaggerations 

Inflated expectations about robotics may lead to disappointment when robots are deployed, or may lead to unfounded fears about their use. A recent ERW organiser commented “Robots are not prevalent or visible in society at large and so prevailing perceptions about robots are largely shaped by media presentation, which too often resort to negative stereotypes.” It’s worth noting robots are typically made for a single task, and many do not look like a humanoid robot. With this lens, robots no longer seem too difficult to engineer, and are far from science fiction depictions. This could be empowering for those who would like to become roboticist, and could help users imagine robots that would be helpful to them. The Smart CIties Robotics challenge for example showed the crowds how robots could help them take a lift, or deliver emergency medicine in a mall.

Teach teachers

By teaching teachers to teach robotics, we can reach many more students than what is possible through all the European Robotics Week combined. Lia Garcia, founder of Logix5 and a national coordinator of ERW in Spain underscored the need to engage the education sector: “We have to work with teachers. We need to get robotics onto the school curriculum, onto the teaching college curriculum and to get to teachers who teach teachers.” Workshops that teach educators, and help spread the word among local teachers are essential. As an added encouragement, they could receive CPD (continuing professional development) credits for taking part in robotics workshops. The ERW2019 central event in Poznan features a workshop dedicated to robotics education in Europe on 15 November.

Run competitions

Competitions are an important way of bringing students into robotics. It’s fun, and exciting, and shows they can build something that works in the real world. Europe now hosts several large robotics competitions including the European Robotics League (Emergency, Consumer, Professional, and Smart Cities). While these competitions are tailored to university students, others are run for kids. The ERW event page already has over 100 robot competitions and challenges listed for this year. Fiorella Operto from Scuola di Robotica has coordinated more than 100 teams from all over Italy committed to using a humanoid robot to promote the Italian Cultural Heritage. The 2020 edition of the NAO Challenge is devoted to “Arts&Cultures”, asking robotics to improve the knowledge of and to promote beautiful Italian art.

Keep it fun

More than ever, we have a broad range of tools to engage with the public. It could be as simple as drawing pictures of robots, to developing robot-themes escape rooms, or engaging on social media including youtube, twitter, instagram and tiktok. Robots are fun, which is why they are such good tools in education. Be creative with demos and activities. Make robots dance, allow people to decorate them, play games. University of Bristol for example will be running a swarm-themed escape room called Swarm Escape!.

Engage with stakeholders

Events with the public are a good opportunity to engage with stakeholders, including government, industry, and users. This is important as stakeholders will ultimately be the ones making robots a reality. Having them participate in such events helps them understand the potential, invest in technology and skills, and shape policy. It could also provide funding for some of the more ambitious events. “For the first time since 2012, Robotics Place, the cluster of Occitanie, organizes a one day meeting with its members on November 20th in Toulouse. Robotics Place members will meet with press, politics, students, partners and professional customers.” says Philippe Roussel, a local coordinator for France.

Act regionally, connect across Europe

Events are present across Europe, organised regionally for the local community. Connecting these events at a European scale increases impact, raise awareness, builds momentum, and allows for lessons to be shared across the content. euRobotics and Digital Innovation Hubs provide valuable resources for these purposes.

Yet there is a divide in access, with cities being better catered to than rural communities, or areas that are poorer. The challenge is to provide everyone with access and exposure to robotics and its opportunities. Extra effort should be made to reach out to underserved communities, for example using a “robot roadshow”. Organisers of ERW said “a further benefit of this cross-border approach would be to enhance the European dimension.” As an example, from May 2020, a 105m long floating Science Center called the MS Experimenta will be touring southern Germany, bringing science from port to port.

Get involved

Feeling inspired, ready to make a difference? Organise your own European Robotics Week event, big or small, and register it here along with the over 900 events already announced.

The Nazis were masters in using technology to mechanize killing and erasing all evidence of their crime. Nowhere is this more apparent than in Treblinka, Poland. The death camp exterminated close to 900,000 Jews over a 15-month period before a revolt led to its dismantlement in 1943. Only a Holocaust memorial stands today on the site of the former gas chamber as a testimony to the memory of the victims. Recently, scientists have begun to unearth new forensic evidence of the Third Reich’s war crimes using LIDAR to expose the full extent of their death factory.

In her work, “Holocaust Archeologies: Approaches and Future Directions,” Dr. Caroline Sturdy Colls undertook an eight-year project to piece together archeological facts from survivor accounts using remote sensors that are more commonly associated with autonomous vehicles and robots than Holocaust studies. As she explains, “I saw working at Treblinka as a cold case where excavation is not permitted, desirable or wanted, [non-invasive] tools offer the possibility to record and examine topographies of atrocity in such a way that the disturbance of the ground is avoided.” Stitching together point cloud outputs from aerial LIDAR sensors, Professor Sturdy Colls stripped away the post-Holocaust vegetation to expose the camp’s original foundations, “revealing the bare earth of the former camp area.” As she writes, “One of the key advantages that LIDAR offers over other remote sensing technologies is its ability to propagate the signal emitted through vegetation such as trees. This means that it is possible to record features that are otherwise invisible or inaccessible using ground-based survey methods.”

Through her research, Sturdy Colls was able to locate several previously unmarked mass graves, transport infrastructure and camp-era buildings, including structures associated with the 1943 prisoner revolt. She credits the technology for her findings, “This is mainly due to developments in remote sensing technologies, geophysics, geographical information systems (GIS) and digital archeology, alongside a greater appreciation of systematic search strategies and landscape profiling,” The researcher stressed the importance of finding closure after seventy-five years, “I work with families in forensics work, and I can’t imagine what it’s like not to know what happened to your family members.” Sturdy Colls’ techniques are now being deployed across Europe at other concentration camp sites and places of mass murder.

Flying north from Poland, I landed in the Netherlands city of Amsterdam to take part in their year-long celebration of Rembrandt (350 years since his passing). At the Rijksmuseum’s Hall of Honors a robot is featured in front of the old master’s monumental work, “Night Watch.” The autonomous macro X-ray fluorescence scanner (Macro-XRF scanner) is busy analyzing the chemical makeup of the paint layers to map and database the age of the pigments. This project, aptly named “Operation Night Watch,” can be experienced live or online showcasing a suite of technologies to determine the best methodologies to return the 1642 painting back to its original glory. Night Watch has a long history of abuse including: two world wars, multiple knifings, one acid attack, botched conservation attempts, and even the trimming of the canvas in 1715 to fit a smaller space. In fact, its modern name is really a moniker of the dirt build up over the years, not the Master’s composition initially entitled: “Militia Company of District II under the Command of Captain Frans Banninck Cocq.”

In explaining the multi-million dollar undertaking the museum’s director, Taco Dibbits, boasted in a recent interview that Operation Night Watch will be the Rijksmuseum’s “biggest conservation and research project ever.” Currently, the Macro-XRF robot takes 24 hours to perform one scan of the entire picture, with a demanding schedule ahead of 56 more scans and 12,500 high-resolution images. The entire project is slated to be completed within a couple of years. Dibbits explains that the restoration will provide insights previously unknown about the painter and his magnum opus: “You will be able to see much more detail, and there will be areas of the painting that will be much easier to read. There are many mysteries of the painting that we might solve. We actually don’t know much about how Rembrandt painted it. With the last conservation, the techniques were limited to basically X-ray photos and now we have so many more tools. We will be able to look into the creative mind of one of the most brilliant artists in the world.”

Whether it is celebrating the narrative of great works of art or preserving the memory of the Holocaust, modern conservatism relies heavily on the accessibility of affordable mechatronic devices. Anna Lopuska, a conservator at the Auschwitz-Birkenau Museum in Poland, describes the Museum’s herculean task, “We are doing something against the initial idea of the Nazis who built this camp. They didn’t want it to last. We’re making it last.” New advances in optics and hardware, enables Lopuska’s team to catalog and maintain the massive camp site with “minimum intervention.” The magnitude of its preservation efforts is listed on its website, which includes: “155 buildings (including original camp blocks, barracks, and outbuildings), some 300 ruins and other vestiges of the camp—including the ruins of the four gas chambers and crematoria at the Auschwitz II-Birkenau site that are of particular historical significance—as well as more than 13 km of fencing, 3,600 concrete fence posts, and many other installations.” This is on top of a collection of artifacts of human tragedy, as each item represents a person, such as “110 thousand shoes, about 3,800 suitcases, 12 thousand pots and pans, 40 kg of eyeglasses, 470 prostheses, 570 items of camp clothing, as well as 4,500 works of art.” Every year more and more survivors pass away making Lopuska’s task, and the unmanned systems she employs, more critical. As the conservationist reminds us, “Within 20 years, there will be only these objects speaking for this place.”

Editor’s Announcements: 1) Vote for our panel, “Love In The Robotic Age,” at SXSW; 2) Signup to attend RobotLab’s next event “Is Today’s Industry 4.0 A Hackers Paradise?” with  Chuck Brooks of General Dynamics on September 25th at 6pm, RSVP Today! 

What do you think are the top three policy and governance issues that face AI/ML currently?

Here are my answers.

1. For me the biggest governance issue facing AI/ML ethics is the gap between principles and practice. The hard problem the industry faces is turning good intentions into demonstrably good behaviour. In the last 2.5 years there has been a gold rush of new ethical principles in AI. Since Jan 2017 at least 22 sets of ethical principles have been published, including principles from Google, IBM, Microsoft and Intel. Yet any evidence that these principles are making a difference within those companies is hard to find – leading to a justifiable accusation of ethics-washing – and if anything the reputations of some leading AI companies are looking increasingly tarnished.

2. Like others I am deeply concerned by the acute gender imbalance in AI (estimates of the proportion of women in AI vary between ~12% and ~22%). This is not just unfair, I believe it too be positively dangerous, since it is resulting in AI products and services that reflect the values and ambitions of (young, predominantly white) men. This makes it a governance issue. I cannot help wondering if the deeply troubling rise of surveillance capitalism is not, at least in part, a consequence of male values.

3. A major policy concern is the apparently very poor quality of many of the jobs created by the large AI/ML companies. Of course the AI/ML engineers are paid exceptionally well, but it seems that there is a very large number of very poorly paid workers who, in effect, compensate for the fact that AI is not (yet) capable of identifying offensive content, nor is it able to learn without training data generated from large quantities of manually tagged objects in images, nor can conversational AI manage all queries that might be presented to it. This hidden army of piece workers, employed in developing countries by third party sub contractors and paid very poorly, are undertaking work that is at best extremely tedious (you might say robotic) and at worst psychologically very harmful; this has been called AI’s dirty little secret and should not – in my view – go unaddressed.

The effort is being led by two well-established associations representing over 400 European organisations from Industry and Research: the Big Data Value Association and euRobotics. A first step in this process saw the launch of a consultation document in Brussels last week entitled “Strategic Research, Innovation and Deployment Agenda for an AI PPP”.

The opportunity for Europe

The strategy document comes on the backdrop of international competition, with every country vying to take the lead in AI.

Roberto Viola, Director-General of the European Commission, DG CONNECT, linked it to the recently disputed European Champions League match between Tottenham and Liverpool, which saw an unexpected roster of teams make the final. “Are you sure that China and the US will lead AI? Watch our planes, watch our industrial robots, watch our cars – why are we so shy about our successes? I’ve been around the world, and I’m always asked how Europe can work with other countries in AI. I don’t think we’ve lost the race, in fact I don’t think it’s a race. If it’s a race, it’s about delivering good services in AI to Europeans. We can be in the final of the Champions league.”

Viola says harmonious cross-fertilisation across three dimensions is needed to make AI a success in Europe. First, the EU needs to mainstream AI in society and the economy. Companies have to find their place, and there is a big role for the public to support AI and its introduction in real scenarios. This is the part missing in the EU, compared to US and China. There is a need for more big public procurement. Second, there is a need to push for research and development in AI through European funding programmes, and third, policy is needed to accompany the development of AI in society.

Photo: Roberto Viola, Director-General of the European Commission, DG CONNECT; Credits: Octavian Carare

Trustworthy AI

Viola says “Europe was one of the first to develop ethical codes in AI, and now everyone is doing it. It shows that we know what is important and the impact and relevance of AI for society.”  Responsible AI was the leitmotif of the day, with everyone highlighting it as Europe’s unique selling point. The message was clear, Europe can do excellent AI, but is leading in terms of deploying it with society in mind. Juha Heikkilä, Head of the Robotics and Artificial Intelligence Unit at the European Commission says “Europe is doing things in a different way – that takes the citizen into account.”

AI Market Opportunity

The combination of big data, advances in algorithms, computing power, and advanced robotics is opening up new market opportunities for AI enabled systems.

Photo: David Bisset, Director – euRobotics, Sonja Zillner, Siemens AG; Credits: Octavian Carare

Sonja Zillner from Siemens, Co-Chief Editor for the strategy, says “the vision is to boost EU industrial competitiveness and lead the world in development and deployment value-driven trustworthy AI”. When asked for their preferred applications for the development of AI, the public requested improved healthcare services, energy efficiency, and availability of trains, as well as increased productivity in digital factories. This led to the realisation, Zillner adds, that “AI is across all sectors. All the sectors are investing. This is an important take-home – working across sectors is really central. We want to leverage AI driven market opportunity, across all sectors.”

A European AI Framework

David Bisset, Executive Director of euRobotics and Co-Chief Editor of the strategy presented an AI Framework that builds on the legal and societal fabric that underpins the impact of AI in Europe. Central to the success of this framework will be an ecosystem that brings together skills, data, and environments to experiment and deploy the technology.  Bisset says “we need data stores, regulatory sandboxes that allow us to test robots in cities without breaking the rules, we need EU regulation that creates a level playing field”. New technologies that work across sectors are needed for sensing, measurement and perception, continuous and integrated knowledge, trustworthy, hybrid decision making, physical and human action and interaction, systems, methodologies, and hardware.

Boosting the adoption of AI in Europe faces several challenges however, including the lack of skills, technology problems, lack of private investment, complexity of deploying products, and the policy and regulation landscape. Bisset says “We need a collective action from all stakeholders to address these challenges – we need an AI Innovation ecosystem”. Stakeholders include researchers, innovators, technology creators, regulators, users, citizens, investors, data suppliers, and application providers.

The implementation of the AI PPP will address the following Working Areas.

WA1: Mobilising the EU AI ecosystem

WA2: Skills and acceptance

WA3: Innovation and market enablers

WA4: Guiding standards and regulation

WA5: Promoting Research Excellence

Words of Wisdom

The event closed with a panel discussion, here are some nuggets of wisdom.

Photo: Panel; Credits: Octavian Carare

“We will not have enough people to treat patients. Without AI, without robotics, it will be a disaster for patients. Whatever the cost, patients will demand their treatments. Treatments will be better, more precise with AI.”  Rolf Roussaint, Director of Anaesthesiology at University Hospital Aachen.

“We need to measure that AI is bringing benefits – measurable AI is important, maybe more important than explainable AI” Henk-Jan Vink, TNO Managing Director Unit ICT.

“We should not be too shy about what the EU is doing in AI – we should be proud.”

“We need to be inclusive, it’s not robotics vs AI. We see robotics and AI as two sides of the same coin. One is a physical instantiation of AI. We need to join forces.” Juha Heikkilä, Head of Unit, Robotics and Artificial Intelligence, DG CONNECT, European Commission.

“Don’t fall in love with the technology – researchers are in love with the technology – and industry with profit. Instead we need use cases proving the new benefits for services and impacts on quality of life that AI can bring.” Gabriella Cattaneo, Associate Vice President of IDC4EU European Government Consulting Unit.

“There will be no progress for AI if we can’t find a way for researchers and startups to have access to data.” Hubert Tardieu, ATOS, Advisor to the CEO.

“In China and the US data is not an issue. The EU doesn’t have that data however – or it’s not shared due to concern. Focussing on the citizen is really important, but we also need to push for access to data.” Federico Milani, Deputy Head of Unit, Data Policies and Innovation, DG CONNECT, European Commission.

Call for collaboration

To conclude the event, Thomas Hahn, President of BDVA and Bernd Liepert, President of euRobotics, moderators of this event and the panel, launched a call for participation and collaboration to all European players active in this domain and committed to boost AI adoption in Europe!

Photo: Bernd Liepert, President of euRobotics, Thomas Hahn, President of BDVA, Credits: Octavian Carare

]]> https://robohub.org/the-social-animals-that-are-inspiring-new-behaviours-for-robot-swarms/ Sun, 12 May 2019 22:43:11 +0000 https://robohub.org/the-social-animals-that-are-inspiring-new-behaviours-for-robot-swarms/ By Edmund Hunt, University of Bristol

From flocks of birds to fish schools in the sea, or towering termite mounds, many social groups in nature exist together to survive and thrive. This cooperative behaviour can be used by engineers as “bio-inspiration” to solve practical human problems, and by computer scientists studying swarm intelligence.

“Swarm robotics” took off in the early 2000s, an early example being the “s-bot” (short for swarm-bot). This is a fully autonomous robot that can perform basic tasks including navigation and the grasping of objects, and which can self-assemble into chains to cross gaps or pull heavy loads. More recently, “TERMES” robots have been developed as a concept in construction, and the “CoCoRo” project has developed an underwater robot swarm that functions like a school of fish that exchanges information to monitor the environment. So far, we’ve only just begun to explore the vast possibilities that animal collectives and their behaviour can offer as inspiration to robot swarm design.

Robots that can cooperate in large numbers could achieve things that would be difficult or even impossible for a single entity. Following an earthquake, for example, a swarm of search and rescue robots could quickly explore multiple collapsed buildings looking for signs of life. Threatened by a large wildfire, a swarm of drones could help emergency services track and predict the fire’s spread. Or a swarm of floating robots (“Row-bots”) could nibble away at oceanic garbage patches, powered by plastic-eating bacteria.

Bio-inspiration in swarm robotics usually starts with social insects – ants, bees and termites – because colony members are highly related, which favours impressive cooperation. Three further characteristics appeal to researchers: robustness, because individuals can be lost without affecting performance; flexibility, because social insect workers are able to respond to changing work needs; and scalability, because a colony’s decentralised organisation is sustainable with 100 workers or 100,000. These characteristics could be especially useful for doing jobs such as environmental monitoring, which requires coverage of huge, varied and sometimes hazardous areas.

Social learning

Beyond social insects, other species and behavioural phenomena in the animal kingdom offer inspiration to engineers. A growing area of biological research is in animal cultures, where animals engage in social learning to pick up behaviours that they are unlikely to innovate alone. For example, whales and dolphins can have distinctive foraging methods that are passed down through the generations. This includes forms of tool use – dolphins have been observed breaking off marine sponges to protect their beaks as they go rooting around for fish, like a person might put a glove over a hand.

Forms of social learning and artificial robotic cultures, perhaps using forms of artificial intelligence, could be very powerful in adapting robots to their environment over time. For example, assistive robots for home care could adapt to human behavioural differences in different communities and countries over time.

Robot (or animal) cultures, however, depend on learning abilities that are costly to develop, requiring a larger brain – or, in the case of robots, a more advanced computer. But the value of the “swarm” approach is to deploy robots that are simple, cheap and disposable. Swarm robotics exploits the reality of emergence (“more is different”) to create social complexity from individual simplicity. A more fundamental form of “learning” about the environment is seen in nature – in sensitive developmental processes – which do not require a big brain.

‘Phenotypic plasticity’

Some animals can change behavioural type, or even develop different forms, shapes or internal functions, within the same species, despite having the same initial “programming”. This is known as “phenotypic plasticity” – where the genes of an organism produce different observable results depending on environmental conditions. Such flexibility can be seen in the social insects, but sometimes even more dramatically in other animals.

Most spiders are decidedly solitary, but in about 20 of 45,000 spider species, individuals live in a shared nest and capture food on a shared web. These social spiders benefit from having a mixture of “personality” types in their group, for example bold and shy.

My research identified a flexibility in behaviour where shy spiders would step into a role vacated by absent bold nestmates. This is necessary because the spider colony needs a balance of bold individuals to encourage collective predation, and shyer ones to focus on nest maintenance and parental care. Robots could be programmed with adjustable risk-taking behaviour, sensitive to group composition, with bolder robots entering into hazardous environments while shyer ones know to hold back. This could be very helpful in mapping a disaster area such as Fukushima, including its most dangerous parts, while avoiding too many robots in the swarm being damaged at once.

The ability to adapt

Cane toads were introduced in Australia in the 1930s as a pest control, and have since become an invasive species themselves. In new areas cane toads are seen to be somewhat social. One reason for their growth in numbers is that they are able to adapt to a wide temperature range, a form of physiological plasticity. Swarms of robots with the capability to switch power consumption mode, depending on environmental conditions such as ambient temperature, could be considerably more durable if we want them to function autonomously for the long term. For example, if we want to send robots off to map Mars then they will need to cope with temperatures that can swing from -150°C at the poles to 20°C at the equator.

In addition to behavioural and physiological plasticity, some organisms show morphological (shape) plasticity. For example, some bacteria change their shape in response to stress, becoming elongated and so more resilient to being “eaten” by other organisms. If swarms of robots can combine together in a modular fashion and (re)assemble into more suitable structures this could be very helpful in unpredictable environments. For example, groups of robots could aggregate together for safety when the weather takes a challenging turn.

Whether it’s the “cultures” developed by animal groups that are reliant on learning abilities, or the more fundamental ability to change “personality”, internal function or shape, swarm robotics still has plenty of mileage left when it comes to drawing inspiration from nature. We might even wish to mix and match behaviours from different species, to create robot “hybrids” of our own. Humanity faces challenges ranging from climate change affecting ocean currents, to a growing need for food production, to space exploration – and swarm robotics can play a decisive part given the right bio-inspiration.

Edmund Hunt, EPSRC Doctoral Prize Fellow, University of Bristol

This article is republished from The Conversation under a Creative Commons license. Read the original article.

I previously listed principles published before December 2017 here; this blogpost appends those principles drafted since January 2018 (plus one in October 2017 I had missed). The principles are listed here (in full or abridged) with links, notes and references but without critique.

Scroll down to the next horizontal line for the updates.

If there any (prominent) ones I’ve missed please let me know.

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.

Note that these principles have been revised and extended, in March 2019 (see below).

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”.

Updated principles…

Intel’s recommendation for Public Policy Principles on AI (October 2017)

1. Foster Innovation and Open Development – To better understand the impact of AI and explore the broad diversity of AI implementations, public policy should encourage investment in AI R&D. Governments should support the controlled testing of AI systems to help industry, academia, and other stakeholders improve the technology.
2. Create New Human Employment Opportunities and Protect People’s Welfare – AI will change the way people work. Public policy in support of adding skills to the workforce and promoting employment across different sectors should enhance employment opportunities while also protecting people’s welfare.
3. Liberate Data Responsibly – AI is powered by access to data. Machine learning algorithms improve by analyzing more data over time; data access is imperative to achieve more enhanced AI model development and training. Removing barriers to the access of data will help machine learning and deep learning reach their full potential.
4. Rethink Privacy – Privacy approaches like The Fair Information Practice Principles and Privacy by Design have withstood the test of time and the evolution of new technology. But with innovation, we have had to “rethink” how we apply these models to new technology.
5. Require Accountability for Ethical Design and Implementation – The social implications of computing have grown and will continue to expand as more people have access to implementations of AI. Public policy should work to identify and mitigate discrimination caused by the use of AI and encourage designing in protections against these harms.

These principles were announced in a blog post by Naveen Rao (Intel VP AI) here.

Lords Select Committee 5 core principles to keep AI ethical (April 2018)

1. Artificial intelligence should be developed for the common good and
   benefit of humanity. 
2. Artificial intelligence should operate on principles of intelligibility and
   fairness. 
3. Artificial intelligence should not be used to diminish the data rights or
   privacy of individuals, families or communities. 
4. All citizens have the right to be educated to enable them to flourish
   mentally, emotionally and economically alongside artificial intelligence. 
5. The autonomous power to hurt, destroy or deceive human beings
   should never be vested in artificial intelligence.

These principles appear in the UK House of Lords Select Committee on Artificial Intelligence report AI in the UK: ready, willing and able? published in April 2019. The WEF published a summary and commentary here.
AI UX: 7 Principles of Designing Good AI Products (April 2018)

1. Differentiate AI content visually – let people know if an algorithm has generated a piece of content so they can decide for themselves whether to trust it or not.
2. Explain how machines think – helping people understand how machines work so they can use them better
3. Set the right expectations – especially in a world full of sensational, superficial news about new AI technologies.
4. Find and handle weird edge cases – spend more time testing and finding weird, funny, or even disturbing or unpleasant edge cases.
5. User testing for AI products (default methods won’t work here).
6. Provide an opportunity to give feedback.

These principles, focussed on the design of the User Interface (UI) and User Experience (UX), are from Budapest based company UX Studio. 

The Toronto Declaration on equality and non-discrimination in machine learning systems (May 2018)

The Toronto Declaration: Protecting the right to equality and non-discrimination in machine learning systems does not succinctly articulate ethical principles but instead presents arguments under the following headings to address concerns “…about the capability of [machine learning] systems to facilitate intentional or inadvertent discrimination against certain individuals or groups of people”.

1. Using the framework of international human rights law The right to equality and non-discrimination; Preventing discrimination, and Protecting the rights of all individuals and groups: promoting diversity and inclusion
2. Duties of states: human rights obligations State use of machine learning systems; Promoting equality, and Holding private sector actors to account
3. Responsibilities of private sector actors human rights due diligence
4. The right to an effective remedy

Google AI Principles (June 2018) 

1. Be socially beneficial. 
2. Avoid creating or reinforcing unfair bias.
3. Be built and tested for safety.
4. Be accountable to people.
5. Incorporate privacy design principles.
6. Uphold high standards of scientific excellence.
7. Be made available for uses that accord with these principles. 

These principles were launched with a blog post and commentary by Google CEO Sundar Pichai here.

IBM’s 5 ethical AI principles (September 2018)

1. Accountability: AI designers and developers are responsible for considering AI design, development, decision processes, and outcomes.
2. Value alignment: AI should be designed to align with the norms and values of your user group in mind.
3. Explainability: AI should be designed for humans to easily perceive, detect, and understand its decision process, and the predictions/recommendations. This is also, at times, referred to as interpretability of AI. Simply speaking, users have all rights to ask the details on the predictions made by AI models such as which features contributed to the predictions by what extent. Each of the predictions made by AI models should be able to be reviewed.
4. Fairness: AI must be designed to minimize bias and promote inclusive representation.
5. User data rights: AI must be designed to protect user data and preserve the user’s power over access and uses

For a full account read IBM’s Everyday Ethics for Artificial Intelligence here.

Microsoft Responsible bots: 10 guidelines for developers of
conversational AI (November 2018)

1. Articulate the purpose of your bot and take special care if your bot will support consequential use cases.
2. Be transparent about the fact that you use bots as part of your product or service.
3. Ensure a seamless hand-off to a human where the human-bot exchange leads to interactions that exceed the bot’s competence.
4. Design your bot so that it respects relevant cultural norms and guards against misuse.
5. Ensure your bot is reliable.
6. Ensure your bot treats people fairly.
7. Ensure your bot respects user privacy.
8. Ensure your bot handles data securely.
9. Ensure your bot is accessible.
10. Accept responsibility.

Microsoft’s guidelines for the ethical design of ‘bots’ (chatbots or conversational AIs) are fully described here.

CEPEJ European Ethical Charter on the use of artificial
intelligence (AI) in judicial systems and their environment, 5 principles (February 2019)

1. Principle of respect of fundamental rights: ensuring that the design and implementation of artificial intelligence tools and services are compatible with fundamental rights.
2. Principle of non-discrimination: specifically preventing the development or intensification of any discrimination between individuals or groups of individuals.
3. Principle of quality and security: with regard to the processing of judicial decisions and data, using certified sources and intangible data with models conceived in a multi-disciplinary manner, in a secure technological environment.
4. Principle of transparency, impartiality and fairness: making data processing methods accessible and understandable, authorising external audits.
5. Principle “under user control”: precluding a prescriptive approach and ensuring that users are informed actors and in control of their choices.

The Council of Europe ethical charter principles are outlined here, with a link to the ethical charter istelf.
Women Leading in AI (WLinAI) 10 recommendations (February 2019)

1. Introduce a regulatory approach governing the deployment of AI which mirrors that used for the pharmaceutical sector.
2. Establish an AI regulatory function working alongside the Information Commissioner’s Office and Centre for Data Ethics – to audit algorithms, investigate complaints by individuals,issue notices and fines for breaches of GDPR and equality and human rights law, give wider guidance, spread best practice and ensure algorithms must be fully explained to users and open to public scrutiny.
3. Introduce a new Certificate of Fairness for AI systems alongside a ‘kite mark’ type scheme to display it. Criteria to be defined at industry level, similarly to food labelling regulations.
4. Introduce mandatory AIAs (Algorithm Impact Assessments) for organisations employing AI systems that have a significant effect on individuals.
5. Introduce a mandatory requirement for public sector organisations using AI for particular purposes to inform citizens that decisions are made by machines, explain how the decision is reached and what would need to change for individuals to get a different outcome.
6. Introduce a ‘reduced liability’ incentive for companies that have obtained a Certificate of Fairness to foster innovation and competitiveness.
7. To compel companies and other organisations to bring their workforce with them – by publishing the impact of AI on their workforce and offering retraining programmes for employees whose jobs are being automated.
8. Where no redeployment is possible, to compel companies to make a contribution towards a digital skills fund for those employees
9. To carry out a skills audit to identify the wide range of skills required to embrace the AI revolution.
10. To establish an education and training programme to meet the needs identified by the skills audit, including content on data ethics and social responsibility. As part of that, we recommend the set up of a solid, courageous and rigorous programme to encourage young women and other underrepresented groups into technology.

Presented by the Women Leading in AI group at a meeting in parliament in February 2019, this report in Forbes by Noel Sharkey outlines both the group, their recommendations, and the meeting.

The NHS’s 10 Principles for AI + Data (February 2019)

1. Understand users, their needs and the context
2. Define the outcome and how the technology will contribute to it
3. Use data that is in line with appropriate guidelines for the purpose for which it is being used
4. Be fair, transparent and accountable about what data is being used
5. Make use of open standards
6. Be transparent about the limitations of the data used and algorithms deployed
7. Show what type of algorithm is being developed or deployed, the ethical examination of how the data is used, how its performance will be validated and how it will be integrated into health and care provision
8. Generate evidence of effectiveness for the intended use and value for money
9. Make security integral to the design
10. Define the commercial strategy

These principles are set out with full commentary and elaboration on Artificial Lawyer here.

IEEE General Principles of Ethical Autonomous and Intelligent Systems (A/IS) (March 2019)

1. Human Rights: A/IS shall be created and operated to respect, promote, and protect internationally recognized human rights.
2. Well-being: A/IS creators shall adopt increased human well-being as a primary success criterion for development.
3. Data Agency: A/IS creators shall empower individuals with the ability to access and securely share their data to maintain people’s capacity to have control over their identity.
4. Effectiveness: A/IS creators and operators shall provide evidence of the effectiveness and fitness for purpose of A/IS.
5. Transparency: the basis of a particular A/IS decision should always be discoverable.
6. Accountability: A/IS shall be created and operated to provide an unambiguous rationale for all decisions made.
7. Awareness of Misuse: A/IS creators shall guard against all potential misuses and risks of A/IS in operation.
8. Competence: A/IS creators shall specify and operators shall adhere to the knowledge and skill required for safe and effective operation.

These amended and extended general principles form part of Ethical Aligned Design 1st edition, published in March 2019. For an overview see pdf here.


Ethical issues arising from the police use of live facial
recognition technology (March 2019) 

9 ethical principles relate to: public interest, effectiveness, the avoidance of bias and algorithmic justice, impartiality and deployment, necessity, proportionality, impartiality, accountability, oversight, and the construction of watchlists, public trust, and cost effectiveness.

Reported here the UK government’s independent Biometrics and Forensics Ethics Group (BFEG) published an interim report outlining nine ethical principles forming a framework to guide policy on police facial recognition systems.

Floridi and Clement Jones’ five principles key to any
ethical framework for AI  (March 2019)

1. AI must be beneficial to humanity.
2. AI must also not infringe on privacy or undermine security. 
3. AI must protect and enhance our autonomy and ability to take decisions and choose between alternatives. 
4. AI must promote prosperity and solidarity, in a fight against inequality, discrimination, and unfairness
5. We cannot achieve all this unless we have AI systems that are understandable in terms of how they work (transparency) and explainable in terms of how and why they reach the conclusions they do (accountability).

Luciano Floridi and Lord Tim Clement Jones set out, here in the New Statesman, these 5 general ethical principles for AI, with additional commentary.

The European Commission’s High Level Expert Group on AI Ethics Guidelines for Trustworthy AI (April 2019)

1. Human agency and oversight AI systems should support human autonomy and decision-making, as prescribed by the principle of respect for human autonomy. 
2. Technical robustness and safety A crucial component of achieving Trustworthy AI is technical robustness, which is closely linked to the principle of prevention of harm.
3. Privacy and Data governance Closely linked to the principle of prevention of harm is privacy, a fundamental right particularly affected by AI systems.
4. Transparency This requirement is closely linked with the principle of explicability and encompasses transparency of elements relevant to an AI system: the data, the system and the business models.
5. Diversity, non-discrimination and fairness In order to achieve Trustworthy AI, we must enable inclusion and diversity throughout the entire AI system’s life cycle. 
6. Societal and environmental well-being In line with the principles of fairness and prevention of harm, the broader society, other sentient beings and the environment should be also considered as stakeholders throughout the AI system’s life cycle. 
7. Accountability The requirement of accountability complements the above requirements, and is closely linked to the principle of fairness

For more detail on each of these principles follow the links above.

Published on 8 April 2019, the EU HLEG AI ethics guidelines for trustworthy AI are detailed in full here.


Draft core principles of Australia’s Ethics Framework for AI (April 2019)

1. Generates net-benefits. The AI system must generate benefits for people that are greater than the costs.
2. Do no harm. Civilian AI systems must not be designed to harm or deceive people and should be implemented in ways that minimise any negative outcomes. 
3. Regulatory and legal compliance. The AI system must comply with all relevant international, Australian Local, State/Territory and Federal government obligations, regulations and laws.
4. Privacy protection. Any system, including AI systems, must ensure people’s private data is protected and kept confidential plus prevent data breaches which could cause reputational, psychological, financial, professional or other types of harm.
5. Fairness. The development or use of the AI system must not result in unfair discrimination against individuals, communities or groups. This requires particular attention to ensure the “training data” is free from bias or characteristics which may cause the algorithm to behave unfairly.
6. Transparency & Explainability. People must be informed when an algorithm is being used that impacts them and they should be provided with information about what information the algorithm uses to make decisions.
7. Contestability. When an algorithm impacts a person there must be an efficient process to allow that person to challenge the use or output of the algorithm.
8. Accountability. People and organisations responsible for the creation and implementation of AI algorithms should be identifiable and accountable for the impacts of that algorithm, even if the impacts are unintended.

These draft principles are detailed in Artificial Intelligence Australia’s Ethics Framework A Discussion Paper. This comprehensive paper includes detailed summaries of many of the frameworks and initiatives listed above, together with some very useful case studies.

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).

Two small figures guard the table holding the Buddha’s relics. Are they spearmen, or robots? British Museum, CC BY-NC-SA

As early as Homer, more than 2,500 years ago, Greek mythology explored the idea of automatons and self-moving devices. By the third century B.C., engineers in Hellenistic Alexandria, in Egypt, were building real mechanical robots and machines. And such science fictions and historical technologies were not unique to Greco-Roman culture.

In my recent book “Gods and Robots,” I explain that many ancient societies imagined and constructed automatons. Chinese chronicles tell of emperors fooled by realistic androids and describe artificial servants crafted in the second century by the female inventor Huang Yueying. Techno-marvels, such as flying war chariots and animated beings, also appear in Hindu epics. One of the most intriguing stories from India tells how robots once guarded Buddha’s relics. As fanciful as it might sound to modern ears, this tale has a strong basis in links between ancient Greece and ancient India.

The story is set in the time of kings Ajatasatru and Asoka. Ajatasatru, who reigned from 492 to 460 B.C., was recognized for commissioning new military inventions, such as powerful catapults and a mechanized war chariot with whirling blades. When Buddha died, Ajatasatru was entrusted with defending his precious remains. The king hid them in an underground chamber near his capital, Pataliputta (now Patna) in northeastern India.

Traditionally, statues of giant warriors stood on guard near treasures. But in the legend, Ajatasatru’s guards were extraordinary: They were robots. In India, automatons or mechanical beings that could move on their own were called “bhuta vahana yanta,” or “spirit movement machines” in Pali and Sanskrit. According to the story, it was foretold that Ajatasatru’s robots would remain on duty until a future king would distribute Buddha’s relics throughout the realm.

Ancient robots and automatons

Hindu and Buddhist texts describe the automaton warriors whirling like the wind, slashing intruders with swords, recalling Ajatasatru’s war chariots with spinning blades. In some versions the robots are driven by a water wheel or made by Visvakarman, the Hindu engineer god. But the most striking version came by a tangled route to the “Lokapannatti” of Burma – Pali translations of older, lost Sanskrit texts, only known from Chinese translations, each drawing on earlier oral traditions.

In this tale, many “yantakara,” robot makers, lived in the Western land of the “Yavanas,” Greek-speakers, in “Roma-visaya,” the Indian name for the Greco-Roman culture of the Mediterranean world. The Yavanas’ secret technology of robots was closely guarded. The robots of Roma-visaya carried out trade and farming and captured and executed criminals.

Robot makers were forbidden to leave or reveal their secrets – if they did, robotic assassins pursued and killed them. Rumors of the fabulous robots reached India, inspiring a young artisan of Pataliputta, Ajatasatru’s capital, who wished to learn how to make automatons.

In the legend, the young man of Pataliputta finds himself reincarnated in the heart of Roma-visaya. He marries the daughter of the master robot maker and learns his craft. One day he steals plans for making robots, and hatches a plot to get them back to India.

Certain of being slain by killer robots before he could make the trip himself, he slits open his thigh, inserts the drawings under his skin and sews himself back up. Then he tells his son to make sure his body makes it back to Pataliputta, and starts the journey. He’s caught and killed, but his son recovers his body and brings it to Pataliputta.

Once back in India, the son retrieves the plans from his father’s body, and follows their instructions to build the automated soldiers for King Ajatasatru to protect Buddha’s relics in the underground chamber. Well hidden and expertly guarded, the relics – and robots – fell into obscurity.

Two centuries after Ajatasatru, Asoka ruled the powerful Mauryan Empire in Pataliputta, 273-232 B.C. Asoka constructed many stupas to enshrine Buddha’s relics across his vast kingdom. According to the legend, Asoka had heard the legend of the hidden relics and searched until he discovered the underground chamber guarded by the fierce android warriors. Violent battles raged between Asoka and the robots.

In one version, the god Visvakarman helped Asoka to defeat them by shooting arrows into the bolts that held the spinning constructions together; in another tale, the old engineer’s son explained how to disable and control the robots. At any rate, Asoka ended up commanding the army of automatons himself.

Exchange between East and West

Is this legend simply fantasy? Or could the tale have coalesced around early cultural exchanges between East and West? The story clearly connects the mechanical beings defending Buddha’s relics to automatons of Roma-visaya, the Greek-influenced West. How ancient is the tale? Most scholars assume it arose in medieval Islamic and European times.

But I think the story could be much older. The historical setting points to technological exchange between Mauryan and Hellenistic cultures. Contact between India and Greece began in the fifth century B.C., a time when Ajatasatru’s engineers created novel war machines. Greco-Buddhist cultural exchange intensified after Alexander the Great’s campaigns in northern India.

In 300 B.C., two Greek ambassadors, Megasthenes and Deimachus, resided in Pataliputta, which boasted Greek-influenced art and architecture and was the home of the legendary artisan who obtained plans for robots in Roma-visaya. Grand pillars erected by Asoka are inscribed in ancient Greek and name Hellenistic kings, demonstrating Asoka’s relationship with the West. Historians know that Asoka corresponded with Hellenistic rulers, including Ptolemy II Philadelphus in Alexandria, whose spectacular procession in 279 B.C. famously displayed complex animated statues and automated devices.

Historians report that Asoka sent envoys to Alexandria, and Ptolemy II sent ambassadors to Asoka in Pataliputta. It was customary for diplomats to present splendid gifts to show off cultural achievements. Did they bring plans or miniature models of automatons and other mechanical devices?

I cannot hope to pinpoint the original date of the legend, but it is plausible that the idea of robots guarding Buddha’s relics melds both real and imagined engineering feats from the time of Ajatasatru and Asoka. This striking legend is proof that the concepts of building automatons were widespread in antiquity and reveals the universal and timeless link between imagination and science.

Adrienne Mayor, Research Scholar, Classics and History and Philosophy of Science, Stanford University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Traditionally, autonomous machines at the ProMat Show have been relegated to a subsection of the exhibit floor under the Automate brand. A couple of years ago there were a handful of self-driving rovers with twice as many robotic arms, today almost one third of the entire McCormick Center was promoting unmanned solutions. As e-commerce sales continue to explode, pressuring fulfilment centers nationwide, the logistics industry now demands two separate conventions with Automate 2021 being held in the Motor City for the first time. This palpable buzz formed the backdrop to the packed startup theater that represented the burgeoning mechatronic ecosystem. In the words of Jeff Burnstein, president of A3 (Automate’s organizers), “Automation is among the most dynamic emerging markets, with venture funding increasing robustly each year. The finalists in the Automate Launch Pad Startup Competition represent the many types of innovation that will transform the manufacturing and services sectors over the next decade.”

Freeing The Supplychain From Bottlenecks

The first company that presented, IM Systems (IMS) traveled from the Netherlands to Chicago to unveil its invention that strikes at the core of the robo-universe – actuation. Today, most of the co-bots deployed are utilizing rotary actuators from Japanese-owned Harmonic Drive (HD). HD’s speed reducers are the industry’s standard for gearing technology, offering the greatest level of movement control, force and precision of any  commercially available mechanical system. This has translated to more than $500 million in annual revenue for HD and is projected to grow to more than $3 billion by 2024. HD’s grip on the industry is citied by many as the main reason why collaborative robot companies have failed to achieve unicorn-level growth. Currently unchallenged by competitors, HD has been free to artificially inflate prices by stiffly controlling the number of units it ships a year, maintaining a careful balance of low supply and high demand. As Thibaud Verschoor, founder of IM Systems, walked on stage I eagerly awaited to hear how his company was planning to disrupt HD’s virtual monopoly. Verschoor introduced a toothless gearbox called the Archimedes Drive that relies on friction instead of gear teeth to transmit torque, promising greater precision and lower cost thus hitting directly at Harmonic Drive. Originating from the Delft University of Technology, IMS is already boasting of its growing list of pre-orders with roboticists lining up for a long-awaited alternative. The startup founder quipped that today it is ‘now quicker to gestate a baby than get an actuator from Harmonic Drive, but not anymore’ as its business child will be shipping next year.

Extending Uptime Performance 

In addition to actuation, energy efficiency has been a hurdle for robots in completing unplugged missions. WiBotic, an innovation spun out of the University of Washington, promises continuous charging availability with its patented wireless inductive power transfer system. Dr. Ben Waters took to the stage to demonstrate how WiBotic is able to charge unmanned vehicles and drones within a ten centimeter proximity of his transfer coils. In addition, WiBotic’s platform is also able to monitor a facility’s entire fleet of autonomous systems to provide managers with better power consumption and machine utilization data, enabling greater productivity and cost efficiency. When I asked Dr. Waters what is next for his company, he exclaimed that they have conquered the air with drones, land with robots, now they are aiming at the sea.

Maximizing Human Labor 

Labor more than any other theme was the big discussion on the floor of ProMat and Automate. While many pundits decry automation for taking jobs, Daniel Theobald of Vecna Robotics shared with me, at our fireside chat, that no one has been fired because of a robot. In fact, today there are not enough humans to fulfill the growing demand of a global economy. One of the most grueling tasks still performed by humans is riveting, often performed in stiff contortions for hours at a time. Wilder Systems is a new collaborative robot system for the aerospace industry to relieve humans from the repetitive dangerous occupation of vertical drilling and fastening performed during fuselage manufacturing. Wilder offers a modular mobile system that is affordable for factories in providing their customers with greater speed, consistency, and accuracy. Until recently robotic systems like Wilder were only available to large corporations but  now with the startups “robot-as-a-service” business model, smaller aircraft plants are able to automate.

Providing A Gentle Touch 

When moderating a session the day before about the definition of success in deploying robots, a large grocery store operator asked the panel what is available for his needs. While hard goods is challenging, picking up the wide variety of fragile organic materials of food is almost impossible for traditional metal grippers. While there are a small number of soft robot solutions available on the market, most require air to be pumped into their elastomeric end effectors, adding cost and complexity to the installation. Ubiros claims to be the “first fully electrically operated soft gripper” without any expensive peripheral equipment, such as pressurized air. To illustrate the tenderness of their solution the startup showcased FlowerBot, a robot arm with their gripper that is capable of picking up roses to create attractive (pre-programmed) bouquets. Dr. Cagdas D. Onal, the company’s founder, proudly introduced the judges to their new customer that just purchased 250 units to begin automating his floral fulfillment center. To Dr Onal’s credit, the Automate pavilion literally smelled like roses as throngs of people sat in the theater with vases on their laps.

Visualizing Installations Before Production

An old proverb teaches that “seeing is believing,” unfortunately machines are often plagued more by Murphy’s Law during the on-boarding process. Firefly Dimension, an augmented reality startup for industrial applications, is focusing on speeding up manufacturing and product development with its unique perspective. Firefly’s headset is the only one in development promoting a 100% field of view equal to one’s eyes. This translates to lower down time, costs and potentially better outcomes. The company shared with the judges its early successes with manufacturers in China that are already testing their prototypes in their production facilities. As many industry analysts project the market for augmented reality solutions to exceed $60 billion by 2024, the Silicon Valley team of entrepreneurs is well positioned to take advantage of the next stage of automation technologies.

Making Robots Accessible For The Masses

According to Dr. Rahul Chipalkatty, CEO of Southie Autonomy, most of the tasks in the warehouse and factory are not automated because they change too quickly for professional integrators and on-staff engineers to respond. Dr. Chipalkatty asserts that this market is actually the lowest hanging fruit for automation, but has remained untapped as technologists have failed to target non-technical workers. To counter this trend, Southie literally developed a magic wand that utilizes artificial intelligence, gesture control and augmented reality projections to train robots on the fly to respond to spontaneous jobs. The Boston-based startup is marketing its technology for “ANY industrial robot to be re-purposed and re-deployed by ANY person, without robotics expertise or even computer skills.” Companies like Southie that focus on ease-of-use interfaces will only further the overall adoption of robotics in the coming years for millions of employees globally. 

Building A Collision-Free World 

On the day of the startup competition, Realtime Robotics announced the launch of its proprietary computer board and software that enables “collision-free” motion planning within milliseconds for collaborative robots and autonomous vehicles to work together. Marketed as RapidPlan and RapidSense the startup contends that its solution is the only one available to enable machines to safely operate within workcells with humans and other robots simultaneously. According to the press release (which was later explained on stage) RapidPlan enables users to load up to “20 million motions” into the system that analyzes “800,000 motions at 30 frames-per-second” which is automatically integrated with the machines onboard sensors via RapidSense. Realtime Robotics’ CEO, Peter Howard, bragged, “Our collision-free motion planning solutions allow robots to perform safely in dynamic, unstructured, and collaborative workspaces, while instantaneously reacting to changes as they occur.” Realtime, backed by Toyota AI, is definitely on my startup watch list for 2019/2020.

And The Winner Is…

Deliberating in the judges room, we were challenged to pick a winner from so many qualified diverse startups. Balancing the presentations against the immediate needs of automation industry, one company universally stood out for its important contribution. IM Systems’ Archimedes Drive has the potential to generate a billion-dollar valuation with its promise of bringing down the cost of adoption and quickening the speed of deployment. Following the show, I caught up with IMS founder Jack Schorsch and asked him how he plans to compete against a multinational conglomerate like Harmonic Drive. Schorsch responded, “I do think that HD & Nabtesco have to some degree deliberately throttled their supply, in order to keep unit profits high on sales to everyone but Fanuc/ABB/Yaskawa. On the flip side, I know in my bones that if you can come to market with a technically comparable or better drive you can sell every unit that you can make.”

Join RobotLab on May 16th when we host a discussion with Alexis Block, inventor of HuggieBot, and Andrew Flett, partner at Mobility Impact Partners, on “Society 2.0: Understanding The Human-Robot Connection In Improving The World,” RSVP Today! 

By Jessica Montgomery, Senior Policy Adviser

The Royal Society’s artificial intelligence (AI) programme explores the frontiers of AI technologies, and their implications for individuals, communities, and society.

As part of our programme of international science and policy dialogue about AI, last year we worked with the American Academy of Arts and Sciences to bring together leading researchers from across disciplines to consider the implications of AI for equality, transparency, and democracy.

This blog gives some of the key points from discussions, which are summarised in more detail in the workshop discussions note (PDF).

AI for society

Today’s AI technologies can help create highly accurate systems, which are able to automate sophisticated tasks. As these technologies progress, researchers and policymakers are grappling with questions about how well these technologies serve society’s needs.

Experience of previous waves of transformative technological change shows that, even when society has a good understanding of the issues new technologies present, it is challenging to create a common vision for the future, to set in place measures that align the present with that desired future, and to engage collective action in ways that help bring it into being.

Where might this common vision for the future come from? International conventions on human rights and sustainable development already point to areas of internationally-agreed need for action, and a growing list of organisations have produced guidelines or principles aiming to shape the development of AI for societal benefit. However, the mechanisms for putting these principles into practice remain unclear. At the same time, there are many other ways in which the things that societies value seem to be contested. In this context, what might AI mean for core values like fairness, transparency, or democracy?

Fairness and equality

Real-world data is messy: it contains missing entries, it can be skewed or subject to sampling errors, and it is often re-purposed for different types of analysis. These sampling errors or other issues in data collection can skew the outputs of a machine learning system, influencing how well it works for different communities of users.

The models created by a machine learning system can also generate unfair outputs, even if trained on accurate data. In recruitment, for example, systems that make predictions about the outcomes of job offers or training can be influenced by biases arising from social structures that are embedded in data at the point of collection. A lack of diversity in the tech community can compound these technical issues, if it reduces the extent to which developers are aware of potential biases when designing machine learning systems.

In seeking to resolve these issues, both technology-enabled and human-led solutions can play a role. For example:

• Initiatives to address issues of bias in datasets, for example Datasheets for Datasets (PDF), set out recommended usage for datasets that are made available for open use.
• A combination of technical and domain insights can improve the performance of machine learning systems on ‘real world’ problems. This requires teams of people from a range of backgrounds and areas of expertise.

Interpretability and transparency

The terms ‘interpretability’ or ‘transparency’ mean different things to different people (PDF), and words such as interpretable, explainable, intelligible, transparent or understandable are often used interchangeably, or inconsistently, in debates about AI. But is this variability in meaning problematic?

There are many reasons why users or developers might want to understand why an AI system reached a decision: interpretability can help developers improve system design; it can help users assess risk or understand how a system might fail; and it might be necessary for regulatory or legal standards.

And there are different approaches to creating interpretable systems. Some AI is interpretable by design, while in other cases researchers can create tools to interrogate complex AI systems.

The significance attached to interpretability, and the type of interpretability that is desirable, will likely depend on the context and user. So the absence of a clear definition of interpretability might ultimately be helpful, if it encourages researchers to reach out to a range of stakeholders to understand what different communities need from AI systems.

Democracy and civil society

Early in the development of digital technologies, a great hope had been that they would enable people to connect and build communities in new ways, strengthening society and promoting new forms of citizen engagement. To some extent, this goal has been achieved: people have an opportunity to communicate with much broader – or much narrower – groups in ways that were not previously possible.

Many countries today are grappling with the unintended consequences of these networks. The information echo chambers that have existed in the physical world have found new manifestations in algorithmically-enabled filter bubbles, and the anonymity afforded by digital interactions has raised new questions also arise about the trustworthiness of online information.

Public and policy debates about AI and democracy have tended to concentrate on how changing patterns of news consumption might shape people’s political opinions. In response, could AI be used to improve the circulation of information, providing people with trustworthy information necessary to inform political debate? Perhaps, but insights from behavioural and social sciences show that the process of making political choices is influenced by emotional and social forces, as well as information.

Democracy is more than the exchange of information in campaigns and elections. It draws from a collection of institutions and civic interactions. Democracy persists because institutions preserve it: in the press and the electoral process, but also in courts, in schools, in hospitals, and more. If democracy resides in institutions, then how can AI support them? There is a need for spaces where people can develop civic networks or new civic institutions that allow people from different backgrounds to engage as citizens on common endeavours, as well as interacting online.

To read more about the key questions raised by this discussion, check out the meeting note (PDF), and you can also read more about the Society’s AI programme.

California has released the disengagement reports the law requires companies to file and it’s a lot of data. Also worth noting is Waymo’s own blog post on their report where they report their miles per disengagement has improved from 5,600 to 11,000.

Fortunately some hard-working redditors and others have done some summation of the data, including this one from Last Driver’s Licence Holder. Most notable are an absolutely ridiculous number from Apple, and that only Waymo and Cruise have numbers suggesting real capability, with Zoox coming in from behind.

The problem, of course, is that “disengagements” is a messy statistic. Different teams report different things. Different disengagements have different importance. And it matters how complex the road you are driving is. (Cruise likes to make a big point of that.)

Safety drivers are trained to disengage if they feel at all uncomfortable. This means that they will often disengage when it is not actually needed. So it’s important to do what Waymo does, namely to play back the situation in simulator to see what would have happened if the driver had not taken over. That playback can reveal if it was:

• Paranoia (as expected) from the safety driver, but no actual issue.
• A tricky situation that is the fault of another driver.
• A situation where the vehicle would have done something undesired, but not dangerous.
• A situation like the above, but dangerous, though nothing would have actually happened. Example — temporarily weaving out of a lane when nobody else is there.
• A situation which would have resulted in a “contact” — factored with the severity of the contact, from nothing, to ding, to crash, to injury, to fatality.

A real measurement involves a complex mix of all these, and I’ll be writing up more about how we could possibly score these.

We know the numbers for these events for humans thanks to “naturalistic” driving studies and other factors. Turns out that humans are making mistakes all the time. We’re constantly not paying attention to something on the road we should be looking at, but we get away with it. We constantly find ourselves drifting out of a lane, or find we must brake harder than we would want to. But mostly, nothing happens. Robots aren’t handled that way — any mistake is a serious issue. Robocars will have fewer crashes because “somebody else was in the wrong place when I wasn’t looking.” Their crashes will often have causes that are foreign to humans.

In Waymo’s report you can actually see a few disengagements because the perception system didn’t see something. That’s definitely something to investigate and fix, but humans don’t see something very frequently, and we still do tolerably well.

A summary of the numbers for humans on US roads:

• Some sort of “ding” accident every 100,000 miles of driving (roughly).
• An accident reported to insurance every 250,000 miles.
• An accident reported to police every 500,000 miles.
• An injury accident every 1.5M miles.
• A fatality every 80M miles of all driving.
• A highway fatality every 180M of highway driving.
• A pedestrian killed every 600M miles of total driving.

Software disengagements

The other very common type of disengagement is a software disengagement. Here, the software decides to disengage because it detects something is going wrong. These are quite often not safety incidents. Modern software is loaded with diagnostic tests, always checking if things are going as expected. When one fails, most software just logs a warning, or “throws an exception” to code that handles the problem. Most of the time, that code does indeed handle the problem, and there is no safety incident. But during testing, you want to disengage to be on the safe side. Once again, the team examines the warning/exception to find out the cause and tries to fix it and figure out how serious it would have been.

That’s why Waymo’s 11,000 miles is a pretty good number. They have not published it in a long time, but their number of “necessary interventions” is much higher than that. In fact, we can bet that in the Phoenix area, where they have authorized limited operations with no safety driver, that it’s better than the numbers above.

The Bell Helicopter tiltrotor, ducted fan hybrid aircraft had a giant crowd when the hall was open.

My feet are aching, as usual, after 3 days on the CES show floor, and the question people always ask others there is “what have you seen that was interesting?”

I won’t say I didn’t see anything interesting, and I had a large number of rewarding conversations with all sorts of companies, making the trip very worthwhile, but I will say I saw less that was new and exciting than ever before. This may be a result of the show’s constant growth that meant in 3 days I still did not manage to get to 3 1/2 major rooms of the show, putting my focus on cars as I usually do.

A larger fraction of CES is not even remotely consumer electronics. In fact, the reason I go is largely the robocar related companies, none of whom are selling to consumers at this time, and most of which never plan to. And yes, the amazing new TVs from Samsung, LG and others are always a draw — LG always creates an astonishing video wall, and all companies were now displaying 8K TVs as a real product (even if a very expensive one for which no video sources are available, but which will be fantastic for still photos.)

My favourite part of the show, Eureka Park, even disappointed even though I gave it most of a day. The proportion of products which are poorly thought through is always high, but you search for gems that might someday change the world. I saw few Eureka moments.

Several fields, such as Iot/Smarthome were growing and maturing, with lots more companies, but at the same time, very little in smart home moves beyond the “Oh, that sounds nice” into truly appealing and useful. I’ve had computer controlled lights for decades. They are handy, and I would rather have them than not have them, but they aren’t essentials the way my phone and a dozen apps within it are, things I can’t do without any more.

There were, again, a ton of different LIDAR companies. What’s interesting about the LIDAR crowd is that they are almost all quite different, in many cases taking entirely different approaches to the technology — the output, the steering, the sensing, the band and even the core technology. I’ll have more coverage of that later. Of course these are not for consumers.

Other press were equally unexcited — I saw far less coverage of the “hot new thing” in the media this year as the show opened and progressed. This was the year of mostly incremental improvements.

Flying car style vehicles did generate excitement, but they were barely there. The most buzz was about Bell helicopter’s “Nexus” tilt-rotor hybrid vehicle on display. It’s a high-end vehicle, with 6 giant ducted fan tilt-rotors, and very much a prototype. Bell knows a lot about tilt-rotor from their work on the V-22 Osprey, a vehicle which has a rather checkered history, so it’s not sure how this will go. The few other companies doing flying vehicles (for people) had tiny booths showing nothing.

The Nexus is a hybrid-hybrid — both multirotor and fixed-wing flight, and both electric batteries and gasoline powered generator for power.

There was some interesting battery technology, including a small startup named GBatteries which claimed that by pulsing the charge voltage they could charge at vastly faster speeds — 5C, or 10 minutes from empty to 80%, and 5 minutes to 50%, without any more heat or battery damage than today’s fast charging (which takes about an hour.) Their demo was just on a drill, so it is yet to be proven in a car, but it would change some of the usability of battery devices and electric cars.

I saw other interesting battery tech but there is some irony – because of all the investment made by Tesla and others in standard lithium ion cells, these cells are now better than other, more innovative technologies, because of their mass production (driving down the cost) and the heavy R&D (improving charge times and reducing battery degradation.) The lithium ion cell was created to be lightweight for laptops where weight is absolutely crucial, but now it’s everywhere, from cars to scooters to even grid storage.

There was an uptick of mobility products (or at least I am paying the most attention to them) beyond cars, including scooters, delivery, bike riding and more.

In spite of what most are writing, it was a year of much progress.

A number of other summaries of 2018 in robocars have called it a bad year, the year it all went south, even the year the public realized that robocars will never come.

In fact, 2018 was the year the field reached a new level of maturity, as its warts began to show, and we saw the first missteps (minor and major) and the long anticipated popping of some of the hype.

As predicted by Gartner’s famous “hype cycle” any highly-hyped technology goes through a “trough of disillusionment” after the initial surge.I see several reasons why the trough is happening now:

• The public is starting to understand some realities which have not been very well conveyed to them, though they were known by the major teams:
  • This is a very hard task
  • It is geographic in nature, due to the need of mapping and local driving rules, and so it begins in limited areas and grows from there.
  • The amount of QA needed to get to unmanned operation is immense, and if you have money, there is no reason to remove safety drivers until you’re quite sure.
  • The so called “level 5” isn’t on any serious roadmap, and may never happen
• Cars showed up at the peak of Gartner’s very own chart a couple of years ago, so it’s just about time in their rulebook
• It’s very typical in software for dates to slip.People predict when they hope they can make a target, which is really the beginning of the time window in their estimate.
• Some people, such as Elon Musk, have made irresponsibly optimistic predictions, leading the public to imagine their drive-everywhere robocar is coming next year.
• If you follow Gartner’s literal curve, they had robocars at the top in 2015.So the trough doesn’t really even need a reason.But I think there will be multiple peaks and troughs.

In reality, the plan at Waymo and many other companies has always been to build a car that serves a limited set of streets — a small set of easy streets — at the start, and then starts growing the network of streets, and eventually towns with streets, as time goes by.

There is going to be a big surge once the technology reaches a level where the remaining problems are no longer technological as logistic.That is to say, when the barrier to expanding to new streets and cities is the detailed work of mapping those streets, learning the local rules and working with local governments.That’s when the “land rush” happens.The limiting factor there is time and talent more than it’s money.

But none of that happens until the cars are ready for deployment, and until they are, they will be tested as prototypes with safety drivers in them.Even the first prototype services, like Waymo’s and Zoox’s and others, will have safety drivers in them.

The Uber fatality — the top story of the year

No question the big story this year was the death of Elaine Herzberg as the result of a compound series of errors and bad practices at Uber.The story is notable for many reasons, including of course how it happened, but also in the public’s reaction. For a long time, I’ve been assured by many skeptics that the first death would mean the end of the robocar dream.The public actually thinks the first deaths were in Teslas (they weren’t) and Tesla stock went up after they took places. The Uber fatality was real, and did teach us that teams are capable of more negligence than I had thought. While it did scale up public distrust, and Uber did shut down their program for at least a year, the overall effect still seems modest.(The larger effect will be much greater intolerance for the next fatality, the one that would have been the first.)

Here’s some of my many posts on Uber this year:

• It certainly looks bad for Uber
• How does a robocar see a pedestrian and how might Uber have gone wrong?
• Comparing the Uber and Tesla fatalities with a table
• Uber reported to have made an error tuning perception system
• NTSB Report implies serious fault for Uber in fatality
• Did Uber really botch the switch to one driver and a timeline of the failure
• Almost every thing that went wrong in the Uber fatality is both terrible and expected
• Don’t watch TV while safety driving

Story #2 — Waymo’s non-launch

Waymo remains the clear leader in the field, so the next top story has to be about them, but sadly it’s the story of their first miss — promising to launch in 2018 and feeling forced to do a “launch” that was really just a formalization of existing activity. I believe that Uber is partly to blame here, in that it did use up a lot of the public’s tolerance for errors, especially in the Phoenix area.Waymo soft launches in Phoenix, but…

The better story for Waymo, however, was their first totally unmanned operations earlier in the year.This also disappointed people because these unmanned operations were on a much more limited scale than people originally imagined, but it’s still a major milestone.It means Waymo’s team convinced the lawyers and board that the systems were good enough to take this risk, even if only in a limited area.
Waymo goes totally unmanned, arbitration and other news

• Uber and Waymo settle lawsuit in a giant victory for Uber
  The Waymo-Uber battle, triggered by Anthony Levandowski, was concluded this year.
• Anthony Levandowski, Jiajun Zhu and Dave Furguson, all formerly of Google Car, make big announcements Anthony even came back, and other alumni of Waymo/Google Car had their milestones.Nuro’s launch of a truly unmanned road robot is also significant.
• Aurora’s manifesto is worth a readAurora, lead by former Waymo leader Chris Urmson, laid out their excellent philosophy well.
• Recent Waymo announcements are slightly underwhelming
• Waymo’s left turns frustrate other drivers

Flying Cars

This was also the year that “flying cars” also known as e-VTOL aircraft, “took off.”It’s now clear the engineering problems are close to solved, though many social and logistic problems remain.These vehicles are at the stage robocars were 10 years ago, and the excitement is building.Sebastian Thrun, the modern “father of self-driving cars” and the man who first got me excited about them, has switched his efforts to flying.I’ll be writing more on this in the coming year.

Other notable news

In chronological order, not order of importance

Lidars and other sensors
• Tons of LIDARs at CES 2018Sensors were a big story this year
• New “Shared Mobility Principles” have too much 2018 thinking.
• FMCW LIDAR is coming, what does it mean?
• Nice summary of LIDAR technologies — is it a “crutch?”
• Robocars enter phase two as different strategies aboundWhat approaches are the teams taking?
• The GM/Cruise robocar interior is refreshingly spartan What robocar interiors will look like.
• All about sensors: Advanced radar and more for the future of perception
• Local Motors hopes to win with 3D printed robocars A different, small volume approach
• Cruise getting a ticket is a very positive story Cruise got too close to a pedestrian, or did it?
• Tesla model X fatality in Silicon Valley had Autopilot turned on We learn more about what Tesla autopilot isn’t.
• Tesla makes a custom neural network chip, is that wise?A first taste of robocar processor battles.
• Google gets a bunch of news — and that is news.Also, contract manufacturing
• Tim Kentley-Klay suddenly ousted from Zoox See also my analysis of Zoox, written just before this.
• New NHTSA regulations, Waymo miles and big deals

My essays on the issues

The main focus of this site are my essays on the issues and future of robocars.Here are the ones from this year I think you will find most valuable.

• Designing a metric to measure robocar safety — what does insurance teach? I examine the hard problem of measuring the safety level of a robocar
• Making tunnels for robocars would be vastly cheaper than subways for trains like SF’s new Central SubwayOnce we have robocars, we should rethink even what we put in private right-of-way like subway tunnels
• The paradox on robocar accidentsA fundamental change of thinking for understanding trends in accidents
• NHTSA/SAE’s “levels” of robocars may be contributing to highway deaths
• Driving without a map is another example of being cheap rather than being safeA mistake I think a few of the major teams are making.This is not the time to be cheap, this is the time to be safe as quickly as you can.
• Safety Drivers for Robocars — the issues and rationaleIn light of Uber’s mistakes, how should we think of the role of the safety driver?
• No, ads won’t pay for your robotaxi ride — but your employer might, and that has big consequencesDebunking one of the sillier ideas out thre, but realizing the basic concept may mean employers pay for commutes, which affects a lot of the economics of travel.
• The road trip robocar and tourist robocarWhat a robocar meant for tourists and road trips might be like.
• Is BRT the best answer for bewildered city planners?City planners should future-proof their urban plans by using BRT instead of LRT or Subways.
• Is the Zoox plan for a custom car the path to robotaxi domination?I talk about Zoox’s radical plan and wonder if it’s smart.
• Sharing the ride:Less sharing is better for transit, more sharing better for carsBreaking up the biggest myth of transit planning, that bigger is better.
• The death of parking services has already begunRobocars change parking greatly.Uber has already started that change.
• Calculating all the externalities of drivingWhat we need to do to really plan transportation well.
• The Spot Market in ParkingA plan for how parking will get sold in the future.
• Cars will go to the chargers, you don’t need to bring the chargers to the carsSolving the charging infrastructure problem for electric cars.
• What does airline competition tell us about robotaxi competition?
• Sleeper carsThe first of two essays on cars designed for long-haul overnight trips.
• Sleeper cars and the unexpected efficiency of solo transport
• The dance between pedestrians and robocarsHow should cars and pedestrians interact?
• The future timeline of robocars — 2020s land rush, 2030s maturityThe answer to that ever-popular question of “When will the robocars arrive?”
• Will people with robocars hire them out as taxis when they are not using them?Breaking apart the economics of sharing your private robocar.
• What processors will be important in robocars?Is the battle for the chip market important?
• What happens to human driven cars in the robocar world?No, we don’t kick the humans off the road for a long time.
• Waymo is first, but is Cruise second, and how can you tell?Why it’s hard to rank the teams.
• Study claims robocars increase pollution; they could not be more wrong
• The utilitarian math overwhelming says we should be aggressive in robocar development.How do we do that?A look into the moral calculus of taking risks in order to save lives.

Earlier this month, I crawled into Dr. Wendy Ju‘s autonomous car simulator to explore the future of human-machine interfaces at CornellTech’s Tata Innovation Center. Dr. Ju recently moved to the Roosevelt Island campus from Stanford University. While in California, the roboticist was famous for making videos capturing people’s reactions to self-driving cars using students disguised as “ghost-drivers” in seat costumes. Professor Ju’s work raises serious questions of the metaphysical impact of docility.

Last January, Toyota Research published a report on the neurological effects of speeding. The team displayed images and videos of sports cars racing down highways that produced spikes in brain activity. The study states,”we hypothesized that sensory inputs during high-speed driving would activate the brain reward system. Humans commonly crave sensory inputs that give rise to pleasant sensations, and abundant evidence indicates that the craving for pleasant sensations is associated with activation within the brain reward system.” The brain reward system is directly correlated to the body’s release of dopamine via the Ventral Tegmental Area. The findings confirmed that higher levels of brain activity on the VTA “were stronger in the fast condition than in the slow condition.” Essentially, speeding (which most drivers engage in regardless of laws) is addicting, as the brain rewards such aggressive behaviors with increased levels of dopamine.

As we relegate more driving to machines, the roads are in danger of becoming highways of strung out dopamine junkies craving new ways to get their fix. Self-driving systems could lead to a marketing battle for in-cabin services pushed by manufacturers, software providers, and media/Internet companies. As an example, Apple filed a patent in August for “an augmented-reality powered windshield system,” This comes two years after Ford filed a similar patent for a display or “system for projecting visual content onto a vehicle’s windscreen.” Both of these filings, along with a handful of others, indicate that the race for capturing rider mindshare will be critical to driving the adoption of robocars. Strategy Analytics estimates this “passenger economy” could generate $7 trillion by 2050. Commuters who spend 250 million hours a year in the car are seen by these marketers as a captive audience for new ways to fill dopamine-deprived experiences.

I predict at next month’s Consumer Electronic Show (CES) in-cabin services will be the lead story coming out of Las Vegas. For example, last week Audi announced a new partnership with Disney to develop innovative ways to entertain passengers. Audi calls the in-cabin experience “The 25th Hour,” which will be further unveiled at CES. Providing a sneak peak into its meaning, CNET interviewed Nils Wollny, head of Audi’s digital business strategy. According to Wollny, the German automobile manufacturer approached Disney 18 months ago to forge a relationship. Wollny explains, “You might be familiar with their Imagineering division [Walt Disney Imagineering], they’re very heavy into building experiences for customers. And they were highly interested in what happens in cars in the future.” He continues, “There will be a commercialization or business approach behind it [for Audi]… I’d call it a new media type that isn’t existing yet that takes full advantage of being in a vehicle. We created something completely new together, and it’s very technologically driven.” When illustrating this vision to CNET’s Road Show, Wollny directed the magazine to Audi’s fully autonomous concept car design that “blurs the lines between the outside world and the vehicle’s cabin.” This is accomplished by turning windows into screens with digital overlays that simultaneously show media while the outside world rushes by at 60 miles per an hour.

Self-driving cars will be judged not by speed of their engines, but the comfort of their cabins. Wollny’s description is reminiscent of the marketing efforts of social media companies that were successful in turning an entire generation into screen addicts. Facebook founder Sean Parker, admitted recently that the social network was founded with the strategy of consuming “as much of your time and conscious attention as possible.” To accomplish this devious objective, Parker confesses that the company exploited the “vulnerability in human psychology.” When you like something or comment on a friend’s photo, Parker boasted “we… give you a little dopamine hit.” The mobile economy has birthed dopamine experts such as Ramsay Brown, cofounder of Dopamine Labs, which promises app designers with increased levels of “stickiness” by aligning game play to the player’s cerebral reward system. Using machine learning Brown’s technology monitors each player’s activity by providing the most optimal spike of dopamine. New York Times columnist David Brook’s said it best, “Tech companies understand what causes dopamine surges in the brain and they lace their products with ‘hijacking techniques’ that lure us in and create ‘compulsion loops’.”

The promise of automation is to free humans from dull, dirty, and dangerous chores. The flip side many espouse is that artificial intelligence could make us too reliant on technology, idling society. Already, semi-autonomous systems are being cited as a cause of workplace accidents. Andrew Moll of the United Kingdom’s Chamber of Shipping warned that greater levels of automation by outsourcing decision making to computers has lead to higher levels of maritime collisions. Moll pointed to a recent spat of seafaring incidents,”We have seen increasing integration of ship systems and increasing reliance on computers. He elaborated that “Humans do not make good monitors. We need to set alarms and alerts, otherwise mariners will not do checks.” Moll exclaimed that technology is increasingly making workers lazy as many feel a “lack of meaning and purpose,” and are suffering from mental fatigue which is leading to a rise in workplace injuries. “Seafarers would be tired and demotivated when they get to port,” cautioned Moll. These observations are not isolated to shipping, the recent fatality by Uber’s autonomous taxi program in Arizona faulted safety driver fatigue as one of the main causes for the tragedies. In the Pixar movie WALL-E, the future is so automated that humans have lost all motivation to leave their mobile lounge chairs. To avoid this dystopian vision, successful robotic deployments will have to strike the right balance of augmenting the physical, while providing cerebral stimulation.

To better understand the automation landscape, join us at the next RobotLab event on “Cybersecurity & Machines” with John Frankel of ffVC and Guy Franklin of SOSA on February 12th in New York City, RSVP Today!

I have been doing research on intelligence for 30 years. Like most of my colleagues, I did not get involved in the field with the aim of producing technological objects, but because I have an interest in the the abstract nature of the notion of intelligence. I wanted to understand intelligence. That’s what science is: Understanding.

However, when a group of researchers ends up understanding something new, that knowledge can be exploited for beneficial or harmful purposes.

That’s where we are — at a turning point where the science of artificial intelligence is emerging from university laboratories. For the past five or six years, large companies such as Facebook and Google have become so interested in the field that they are putting hundreds of millions of dollars on the table to buy AI firms and then develop this expertise internally.

The progression in AI has since been exponential. Businesses are very interested in using this knowledge to develop new markets and products and to improve their efficiency.

So, as AI spreads in society, there is an impact. It’s up to us to choose how things play out. The future is in our hands.

Killer robots, job losses

From the get-go, the issue that has concerned me is that of lethal autonomous weapons, also known as killer robots.

While there is a moral question because machines have no understanding of the human, psychological and moral context, there is also a security question because these weapons could destabilize the world order.

Another issue that quickly surfaced is that of job losses caused by automation. We asked the question: Why? Who are we trying to bring relief to and from what? The trucker isn’t happy on the road? He should be replaced by… nobody?

We scientists seemingly can’t do much. Market forces determine which jobs will be eliminated or those where the workload will be lessened, according to the economic efficiency of the automated replacements. But we are also citizens who can participate in a unique way in the social and political debate on these issues precisely because of our expertise.

Computer scientists are concerned with the issue of jobs. That is not because they will suffer personally. In fact, the opposite is true. But they feel they have a responsibility and they don’t want their work to potentially put millions of people on the street.

Revising the social safety net

So strong support exists, therefore, among computer scientists — especially those in AI — for a revision of the social safety net to allow for a sort of guaranteed wage, or what I would call a form of guaranteed human dignity.

The objective of technological innovation is to reduce human misery, not increase it.

It is also not meant to increase discrimination and injustice. And yet, AI can contribute to both.

Discrimination is not so much due, as we sometimes hear, to the fact AI was conceived by men because of the alarming lack of women in the technology sector. It is mostly due to AI leading on data that reflects people’s behaviour. And that behaviour is unfortunately biased.

In other words, a system that relies on data that comes from people’s behaviour will have the same biases and discrimination as the people in question. It will not be “politically correct.” It will not act according to the moral notions of society, but rather according to common denominators.

Society is discriminatory and these systems, if we’re not careful, could perpetuate or increase that discrimination.

There could also be what is called a feedback loop. For example, police forces use this kind of system to identify neighbourhoods or areas that are more at-risk. They will send in more officers… who will report more crimes. So the statistics will strengthen the biases of the system.

The good news is that research is currently being done to develop algorithms that will minimize discrimination. Governments, however, will have to bring in rules to force businesses to use these techniques.

Saving lives

There is also good news on the horizon. The medical field will be one of those most affected by AI — and it’s not just a matter of saving money.

Doctors are human and therefore make mistakes. So the more we develop systems with more data, fewer mistakes will occur. Such systems are more precise than the best doctors. They are already using these tools so they don’t miss important elements such as cancerous cells that are difficult to detect in a medical image.

There is also the development of new medications. AI can do a better job of analyzing the vast amount of data (more than what a human would have time to digest) that has been accumulated on drugs and other molecules. We’re not there yet, but the potential is there, as is more efficient analysis of a patient’s medical file.

We are headed toward tools that will allow doctors to make links that otherwise would have been very difficult to make and will enable physicians to suggest treatments that could save lives.

The chances of the medical system being completely transformed within 10 years are very high and, obviously, the importance of this progress for everyone is enormous.

I am not concerned about job losses in the medical sector. We will always need the competence and judgment of health professionals. However, we need to strengthen social norms (laws and regulations) to allow for the protection of privacy (patients’ data should not be used against them) as well as to aggregate that data to enable AI to be used to heal more people and in better ways.

The solutions are political

Because of all these issues and others to come, the Montréal Declaration for Responsible Development of Artificial Intelligence is important. It was signed Dec. 4 at the Society for Arts and Technology in the presence of about 500 people.

It was forged on the basis of vast consensus. We consulted people on the internet and in bookstores and gathered opinion in all kinds of disciplines. Philosophers, sociologists, jurists and AI researchers took part in the process of creation, so all forms of expertise were included.

There were several versions of this declaration. The first draft was at a forum on the socially responsible development of AI organized by the Université de Montréal on Nov. 2, 2017.

That was the birthplace of the declaration.

Its goal is to establish a certain number of principles that would form the basis of the adoption of new rules and laws to ensure AI is developed in a socially responsible manner. Current laws are not always well adapted to these new situations.

And that’s where we get to politics.

The abuse of technology

Matters related to ethics or abuse of technology ultimately become political and therefore belong in the sphere of collective decisions.

How is society to be organized? That is political.

What is to be done with knowledge? That is political.

I sense a strong willingness on the part of provincial governments as well as the federal government to commit to socially responsible development.

Because Canada is a scientific leader in AI, it was one of the first countries to see all its potential and to develop a national plan. It also has the will to play the role of social leader.

Montréal has been at the forefront of this sense of awareness for the past two years. I also sense the same will in Europe, including France and Germany.

Generally speaking, scientists tend to avoid getting too involved in politics. But when there are issues that concern them and that will have a major impact on society, they must assume their responsibility and become part of the debate.

And in this debate, I have come to realize that society has given me a voice — that governments and the media were interested in what I had to say on these topics because of my role as a pioneer in the scientific development of AI.

So, for me, it is now more than a responsibility. It is my duty. I have no choice.

Yoshua Bengio, Professeur titulaire, Département d’informatique et de recherche opérationnelle, Université de Montréal

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Waymo car drives in Tempe

Waymo announced today they will begin commercial operations in the Phoenix area under the name “Waymo One.” Waymo has promised that it would happen this year, and it is a huge milestone, but I can’t avoid a small bit of disappointment.

Regular readers will know I am a huge booster of Waymo, not simply because I worked on that team in its early years, but because it is clearly the best by every metric we know. However, this pilot rollout is also quite a step down from what was anticipated, though for sensible reasons.

1. At first, it is only available to the early rider program members. In fact, it’s not clear that this is any different from what they had before, other than it is more polished and there is a commercial charging structure (not yet published.)
2. Vehicles will continue to operate with safety drivers.

Other companies — including Waymo, Uber, Lyft and several others — have offered limited taxi services with safety drivers. This service is mainly different in its polish and level of development — or at least that’s all we have been told. They only say they “hope” to expand it to people outside the early rider program soon.

In other words, Waymo has missed the target it set of a real service in 2018. It was a big, hairy audacious target, so there is no shame or surprise in missing it, and it may not be missed by much.

There is a good reason for missing the target. The Uber fatality, right in that very operation area, has everybody skittish. The public. Developers. Governments. It used up the tolerance the public would normally have for mistakes. Waymo can’t take the risk of a mistake, especially in Phoenix, especially now, and especially if it is seen it came about because they tried to go too fast, or took new risks like dropping safety drivers.

I suspect at Waymo they had serious talks about not launching in Phoenix, in spite of the huge investment there. But in the end, changing towns may help, but not enough. Everybody is slowed down by this. Even an injury-free accident that could have had an injury will be problematic — and the truth is, as the volume of service increases, that’s coming.

It was terribly jarring for me to watch Waymo’s introduction video. I set it to play at one minute, where they do the big reveal and declare they are “Introducing the self driving service.”

The problem? The car is driving down N. Mill Avenue in Tempe, the road on which Uber killed Elaine Herzberg, about 1,100 feet from the site of her death. Waymo assures me that this was entirely unintentional — and those who live outside the area or who did not study the accident may not recognize it — but it soured the whole launch for me.

A day before snow hindered New York commuters, researchers at the University of Iowa and Princeton identified the growth of urbanization as the leading cause for catastrophic storm damage. Wednesday’s report stated that the $128 billion wake of Hurricane Harvey was 21 times greater due to the population density of Houston, one of America’s fastest growing cities. This startling statistic is even more alarming in light of a recent UN study which reported that 70% of the projected 9.7 billion people in the world will live in urban centers by 2050. Superior urban management is one of the major promises of autonomous systems and smart cities.

Today, one of the biggest headaches for civil planners is the growth of traffic congestion and demand for parking, especially considering cars are one of the most inefficient and expensive assets owned by Americans. According to the Governors Highway Safety Association the average private car is parked 95% of the time. Billions of dollars of real-estate in America is dedicated to parking. For example, in a city like Seattle 40% of the land is consumed by parking. Furthermore, INRIX analytics estimates that more than $70 billion is spent by Americans looking for parking. The average driver wastes $345 a year in time, fuel and emissions. “Parking pain costs much more…New Yorkers spend 107 hours a year looking for parking spots at a cost of $2,243 per driver,” states INRIX.This month I spoke with Dr. Anuja Sonalker about her plan to save Americans billions of dollars from parking. Dr. Sonalker is the founder of STEER Technologies, a full-service auto-valet platform that is providing autonomous solutions to America’s parking pain. The STEER value proposition uses a sensor array that easily connects to a number of popular automobile models, seamlessly controlled by one’s smart phone. As Dr. Sonalker explains, “Simply put STEER allows a vehicle user to pull over at a curb (at certain destinations), and with a press of a button let the vehicle go find a parking spot and park for you. When it’s time to go, simply summon the vehicle and it comes back to get you.” An added advantage of STEER is its ability to conserve space, as cars can be parked very close together since computers don’t use doors.

Currently, STEER is piloting its technology near its Maryland headquarters. In describing her early success, Dr. Sonalker boasts, “We have successfully completed testing various scenarios under different weather, and lighting conditions at malls, train stations, airports, parks, construction sites, downtown areas. We have also announced launch dates in late 2019 with the Howard Hughes Corporation to power the Merriweather district – a 4.9 Million square foot new smart development in Columbia, MD, and the BWI airport.” The early showing from STEER’s performance is the results of its proprietary product that is built for all seasons and topographies. “Last March, we demonstrated live in Detroit under a very fast snowstorm system. Within less than an hour the ground was covered in 2+ inches of snow,” describes Dr. Sonalker. “No lane markings were visible any more, and parking lines certainly were not visible. The STEER car successfully completed its mission to ‘go park’, driving around the parking lot, recognizing other oncoming vehicles, pacing itself accordingly and locating and manoeuvring itself into a parking spot among other parked vehicles in that weather.”

In breaking down the STEER solution, Dr. Sonalker expounds, “The technology is built with a lean sensor suite, its cost equation is very favorable to both after market and integrated solutions for consumer ownership.” She further clarifies, “From a technology stand point both solutions are identical in the feature they provide. The difference lies in how the technology is integrated into the vehicle. For after market, STEER’s technology runs on additional hardware that is retrofitted to the vehicle. In an integrated solution STEER’s technology would be housed on an appropriate ECU driven by vehicle economics and architecture, but with a tight coupling with STEER’s software. The coupling will be cross layered in order to maintain the security posture.” Unlike many self-driving applications that rely heavily on LIDAR (Light Detection And Ranging), STEER uses location mapping of predetermined parking structures along with computer vision. I pressed Dr. Sonalker about her unusual setup, “Yes, it’s true we don’t use LIDAR. You see, STEER started from the principle of security-led design which is where we start from a minimum design, minimum attack surface, maximum default security.”

I continued my interview of Dr. Sonalker to learn how she plans to roll out the platform, “In the long term, we expect to be a feature on new vehicles as they roll out of the assembly line. 2020-2021 seems to be happening based on our current OEM partner targets. Our big picture vision is that people no longer have to think about what to do with their cars when they get to their destination. The equivalent effect of ride sharing – your ride ends when you get off. There will be a network of service points that your vehicle will recognize and go park there until you summon it for use again.” STEER’s solution is part of a growing fleet of new smart city initiatives cropping up across the automation landscape. At last year’s Consumer Electronic Show, German auto supplier Robert Bosch GmbH unveiled its new crowd-sourcing parking program called, “community-based parking.” Using a network of cameras and sensors to identify available parking spots Bosch’s cloud network automatically directs cars to the closest spot. This is part of Bosch’s larger urban initiative, as the company’s president Mike Mansuetti says, “You could say that our sensors are the eyes and ears of the connected city. In this case, its brain is our software. Of Bosch’s nearly 400,000 associates worldwide, more than 20,000 are software engineers, nearly 20% of whom are working exclusively on the IoT. We supply an open software platform called the Bosch IoT Suite, which offers all the functions necessary to connect devices, users, and companies.”

As the world grabbles with population explosion exacerbated by cars strangling city centers, civil engineers are challenging technologists to reimagine urban communities. Today, most cars are dormant, and when used run at one-fifth capacity with typical trips less than a mile from one’s home (easily accessible on foot or bike). In the words Dr. Sonalker, “All autonomous technologies will lead to societal change. AV Parking will result in more efficient utilization of existing spaces, fitting more in the same spaces, better use of underutilized remote lots, and frankly, even shunting parking off to further remote locations and using prime space for more enjoyable activities.”

Join the next RobotLab forum discussing “Cybersecurity & Machines” to learn how hackers are attacking the the ecosystem of smart cities and autonomous vehicles with John Frankel of ffVC and Guy Franklin of SOSA on February 12th in New York City, RSVP Today!

Two weeks ago, I participated on a panel at the BCI Summit exploring the impact of quantum computing. As a neophyte to the subject, I marveled at the riddle posed by Grover’s Algorithm. Imagine you are assigned to find a contact in a phonebook with a billion names, but all you are given is a telephone number. A quantum computer is able to decipher the answer with remarkable speed at a rate of .003% of today’s binary systems require one operation per line of data (in this case one billion).

Last week, Volkswagen announced that it is developing a smart traffic management system using quantum computers with the aim of increasing global transportation efficiency. In the words of Florian Neukart of Volkswagen’s CODE Lab in San Francisco, “We want to gain an in-depth understanding of applications of this technology which could be beneficial to the company, including traffic optimization. Public transport organizations and taxi companies in large cities are highly interested in managing their fleets efficiently. Our quantum-optimized traffic management system could help make that a reality.” Neukart is joined in the effort with an all-star team that includes: Google, quantum computing hardware startup D-Wave, and Swiss-based data analytics firm Teralytics.

According to Alastair MacLeod of Teralytics, today’s supercomputers are not quick  enough to crunch the immense amount of data posed by urban traffic to forecast transportation needs. In order to accomplish this objective, the team of researchers is evaluating current traffic flows using conventional binary computing technologies, then uploading the data via the cloud to D-Wave’s quantum interface for predictive analysis. The next step in Volkswagen’s vision is to simultaneously predict traffic movements and even pedestrian and vehicular destinations. Ultimately, the goal of the German car manufacturer is to create smarter and safer cities that navigate its citizens efficiently around their streets. As MacLeod describes, “In the end, mobility at its highest level is humans moving around in patterns and needing to somehow match the mode of transportation that suits best the trip and the supply of those different modes of transportation for the demand.”

In order to understand the impact of quantum computing on autonomous systems, I reached out to my fellow panelist Dr. Jerry Chow, manager of Experimental Quantum Computing for IBM. Recently, the computer consulting powerhouse announced that it is working with Volkswagen’s competitor, Daimler. According to Chow’s press office, “IBM Q Network partner Daimler AG is working with us to advance the potential use cases of quantum computing for the automotive and transportation industry. Some areas of their research include finding and developing new materials for automotive application through quantum chemistry, complex optimization problems such as for manufacturing processes or vehicle routing for fleet logistics or autonomous/self-driving cars, and the intersection of quantum and machine learning to enhance the capabilities of artificial intelligence.”

When meeting Chow, he reminded me that it is still early in the development of such technologies. It is important to unpack the current offerings within this nascent marketplace, which also include: Google, Microsoft, Intel and a couple of University upstarts. One of the key distinguishing factors is the number of qubits in the system, which simply translate to the size of its memory. For example, a 64-qubit quantum computer can store up to 18 quintillion numbers – coupled with entanglements of parallel processing power and targeted algorithms, this enables such new computing paradigms to perform functions at lightning speeds. This past September, Rigetti (a startup out of Berkeley) opened up its 128-cubit system to the public via the cloud for such complex applications as modelling molecular structures and machine learning. Chad Rigetti expounds, “What we want to do is focus on the commercial utility and applicability of these machines, because ultimately that’s why this company exists,” which has raised already close to $120 million to fulfil this promise.

In the qubit race, Google’s Bristlecone is a distant second with 72 qubits, with IBM and Intel following suit with 49 and 50 qubits, respectively. While D-Wave brags of a 2,000-qubit system, many industry insiders do not consider the Vancouver startup a true quantum system as it leverages “quantum annealing,” which according to Science magazine “produced no quantum speedup,” over traditional binary systems. Adding to the complexity, a true quantum computer requires temperatures of a measure that is colder than interstellar space to necessitate a stationary state for its sub-atomic participles. In contrast, D-Wave’s core operates at -273 degrees celsius or 0.02 degrees from absolute zero.

Many experts believe that IBM offers the most commercially stable available platform via the cloud, as the larger qubit products report greater error rates. According to Dr. Chow’s team, “IBM Q Network has offered access to 20 qubit universal, superconducting quantum computers for about a year. They’re being used for research and industry use cases by Fortune 500 companies, academic institutions, startups, and government labs all over the world.” In addition to Daimler, Chow’s group is engaging with JP Morgan and Samsung. I pressed Dr. Chow about whether he foresees such advanced computing systems being used for reinforcement learning and unmanned systems, like humanoids. His office replied that “It’s possible that within five years, the industry will develop the first applications where a quantum computer, used alongside a classical computer, will offer a benefit to solving specific problems. And some of this research, including ours at IBM, does touch upon machine learning, but at a fundamental level. It’s too early to say what kinds of applications will be used in autonomous vehicles, robotics, or AI, in general.”

The future of these post-binary systems may depend largely on two fundamental factors: money and user adoption. Earlier this Fall, Congress proposed a $1.3 billion investment to accelerate federal programs for a National Quantum Initiative with the White House convening a Quantum Summit last September. This action is in response to China’s announced $10 billion spending plan to build a national laboratory for quantum science scheduled to open in 2020. The European Union in 2016 allotted a $1 billion for the continent’s own quantum-research initiative. In addition, developer kits are freely available online today, ironically accessed through binary computers. Dr. Chow enthusiastically encouraged the BCI Summit audience to start kicking-the-tires of IBM’s  SDK, Qiskit, in the hopes of expanding its library of applications (a list of other simulators and developers kits are available on the quantum wiki).

Join the next RobotLab forum discussing “Cybersecurity & Machines” to learn more about quantum computing and the future of encryption with John Frankel of ffVC and Guy Franklin of SOSA on February 12th in New York City.