The many potential social and economic benefits from advances in AI-based technologies depend entirely on the environment in which these technologies evolve, says the Royal Society. According to a new report from the UK’s science academy, urgent consideration needs to be given to the “careful stewardship” needed over the next ten years to ensure that the dividends from machine learning – the form of artificial intelligence that allows machines to learn from data – benefit all in UK society.

Machine Learning: the power and promise of computers that learn by example, published today (25 April 2017), comes at a critical time in the rapid development and use of this technology, and the growing debate about how it will reshape the UK economy and people’s lives. Crucially the report calls for research funding bodies to support a new wave of machine learning research that goes beyond technical challenges, and into areas aimed at addressing public confidence in machine learning – vital to the UK maintaining its internationally competitive edge at the forefront of this area.

The report also offers the first evidence about the UK public’s views on machine learning, including the application areas about which they are particularly positive, and the need for the real-world data feeding the growth of this technology to be dealt with fairly and securely.

Professor Peter Donnelly FRS, chair of the report’s working group and Director of the Wellcome Trust Centre for Human Genetics and Professor of Statistical Science at the University of Oxford, says:

“Machine learning is already used in many apps and services that we encounter every day. It is used to tag people in our photos, by our phones to interpret voice commands, by internet retailers to make recommendations, and by banks to spot unusual activity on a credit or debit card. However, these current applications only scratch the surface of understanding just how powerful a technology this could be.

“Machine learning will have an increasing impact on our lives and lifestyles over the next five to ten years. There is much work to be done so that we take advantage of machine learning’s potential and ensure that the benefits are shared, especially as this could be a key area of opportunity for the UK in the coming years.”

An action plan so no one is left behind and the benefits are shared

The report calls for action in a number of key areas over the next five to ten years to create an environment of “careful stewardship” that can help ensure that the benefits of this technology are felt broadly. Understanding who will be most affected, how the benefits are likely to be distributed, and where the opportunities for growth lie, will be key to designing the most effective interventions to enable people and businesses to adapt to, and take advantage of, the machine learning driven changes to their lives and livelihoods.

Supporting the development of skills at every level

• 23% of the UK population lack basic digital skills
• Build digital skills and understanding at every level from schools to universities, and into the workplace, and ensure that opportunities are not limited by gender, ethnicity or socio-economic background
• Government should consider introducing funded Masters courses in Machine Learning to develop a pool of informed users of machine learning across business, industry, and research sectors
• There is a critical need for increased training at PhD level and beyond to invest in the next generation of research leaders in machine learning.

Creating opportunities to use machine learning

• Integrate machine learning into the UK’s industrial strategy, to help businesses make the most of its potential
• Support a new wave of research in machine learning, including in areas that can address social or ethical concerns.

Creating a data environment that supports machine learning

• 90% of the world’s data has been created within the last five years
• Continue to build on the UK’s track record of open data and safe sharing of data
• Where there is value in accessing data that cannot be open – for example medical data or commercially sensitive industry data – ensuring there are frameworks and agreements in place which facilitate appropriate data sharing in these circumstances.

An enabling governance environment

• Support an informed public debate about what we want machine learning to do, and how the benefits are distributed
• Develop a new framework for data governance – one that can keep pace with the challenge of the governance of data and its uses in the 21st century. While there may be specific questions about the use of machine learning in particular contexts, these should be handled in a sector-specific way.

Results from the UK’s first in-depth assessment of public views on machine learning – carried out by the Royal Society and Ipsos MORI – demonstrate that while most people have not heard the term ‘machine learning’ (only 9% have), the vast majority have heard about or used at least one of its applications. The public do not have a single view on machine learning – attitudes, positive or negative, vary depending on the circumstances in which machine learning is being used. For example, computers that could help with medical diagnoses, or could help relieve pressure on professionals in core services such as health and social care, education, and policing were viewed more favourably than computers that could make investments in the stock market.

As with most new technologies, some of those interviewed expressed concerns. These included: depersonalisation, or machine learning systems replacing valued human experiences; the potential impact of machine learning on employment; the potential for machine learning systems to cause harm, for example accidents in autonomous vehicles; and machine learning systems restricting choice, such as when directing consumers to specific products and services.

Ongoing public confidence will be central to realising the benefits that machine learning promises, and continued engagement between machine learning researchers and practitioners and the public will be important as the field develops. The report also calls on researchers to consider the wider impact of their work and to receive training in recognising the ethical implications.

Machine Learning
Many services that we use every day rely on machine learning. Machine learning is used in internet search engines, email filters to sort out spam, websites to make personalised recommendations, banking software to detect unusual transactions, and lots of apps on our phones such as voice recognition.

Many services that we use every day rely on machine learning.

The technology has many more potential applications, some with higher stakes than others. Future developments could support the UK economy and will have a significant impact upon society. For example, machine learning could provide us with readily available ‘personal assistants’ to help manage our lives, it could dramatically improve the transport system through the use of autonomous vehicles; and the healthcare system, by improving disease diagnoses or personalising treatment. Machine learning could also be used for security applications, such as analysing email communications or internet usage. The implications of these and other applications of the technology need to be considered now and action taken to ensure uses will be beneficial to society.

What is the Royal Society project about?
There are both opportunities and challenges around this transformative technology and it raises social, legal, and ethical questions. This is why the Royal Society is starting a project on machine learning, aiming to stimulate a debate, to increase awareness and demonstrate the potential of machine learning and highlight the opportunities and challenges it presents. In the course of the project we will engage with policymakers, academia, industry and the wider public.

There are both opportunities and challenges around this transformative technology

The project will focus on current and near-term (5-10 years) applications of machine learning. It will have a strong public engagement element, and a variety of resources will be produced over the course of the project. The project scope was developed by a Core Group of experts who met over the summer 2015.

Who will inform this project?
This Royal Society project will be led by a Working Group involving a range of expertise.

You can also have a say.

Have your say
You can also have a say. This Royal Society project will involve a public dialogue exercise and ensure that public views inform policy development. You can start by responding to some of the questions in our Call for Evidence.

Fruit Fly – drosophila melanogaster. Source: Francis Prior/flickr

Taking cues from the natural world is growing ever more popular in robotics. This week, in a paper published by the Royal Society, a team from NCCR Robotics and LIS, EPFL present a revolutionary new artificial compound eye, created with the fruit fly in mind.

Evolution has cleverly equipped the arthropods’ compound eye with key features that enable these insects to react instantly to danger, even in cluttered environments.Their eyes are able to create low-resolution images with wide fields of view, high sensitivity to motion and a near infinite depth of field, all at low energy cost. The insect’s eye is made up of many thousands of ommatidia and each is an individual sight receptor. Every ommatidia is positioned slightly differently in relation to its neighbours, allowing the brain to collect and compare information from all and piece together a complete image of the surrounding area. These quick functioning, simple eyes are what allow insects to react so quickly when you try to swat them.

Each small eye can be used individually or combined with others. Source: Pericet-Camara et al., 2015.

With all these advantages in mind, the team, led by Prof. Dario Floreano, has created an artificial version of an ommatidium that functions through three hexagonal photodetectors arranged in a triangular shape, underneath a single lens. These photodetectors work together and combine perceived changes in structured light (optic flow), to present a 3D image that shows what is moving in the scene, and in which direction the movement is happening. This means that even when the eye is moving and the environment remains stationary, relative movement is picked up. With three sensors each receiving and combining optic flow in different directions, the eye is able to function as a stand alone sensor so that the multiple, perfectly aligned single eyes used in previous solutions are no longer required.

The eye has been tested in a wide range of lighting conditions and has been shown to function in environments ranging from a poorly lit room to bright sunshine. It even works three times faster than the insect eyes that it is modelled on, with 300 frames per second recorded during testing. This allows an image of a given environment to build up as the robot is moving, enabling it to spot obstacles and react quickly. The ability to rapidly extract data offline and provide an image suitable for navigation, without the need for high energy expenditure, is what allows this eye to be truly versatile.

Eyes can be arranged on a flexible array Source: Pericet-Camara et al., 2015).

This new sensor is 1925 x 475 x 860 μm in size and weighs only 2mg, making it ideal for a micro air vehicle (MAV), or other flying robots where both speed and payload are of high importance. A further advantage of this method is that multiple sensors may be used together, each pointing in different directions, to provide a miniature omnidirectional camera for motion detection. Since the sensors work independently, they can be mounted in any configuration, even on flexible substrates. The team has demonstrated the operation of their compositional eye in the shape of a bendable strip (which Prof. Floreano calls Vision Tape) because it can be attached to any planar or curved object.

Future work could see the sensor embedded onto the surface of soft and flexible robots or in smart clothing to allow blind people to “see” obstacles; it could be used in medical endoscopes, enabling more precise operations. The fact that it can also be arranged in layers, allowing for adaptation to arbitrary shapes, makes the possibilities for its use endless.

Reference

R. Pericet-Camara, M.K. Dobrzynski, R. Juston, S. Viollet, R. Leitel, H.A. Mallot, D. Floreano, “An artificial elementary eye with optic flow detection and compositional properties,” J. R. Soc. Interface, vol. 12: 20150414, 2015. http://dx.doi.org/10.1098/rsif.2015.0414

If you liked this article, you may also be interested in:

• Robots Podcast: Curved Artificial Compound Eye
• TED talk with Davide Scaramuzza on visual control of MAVs
• DALER: A bio-inspired robot that can both fly and walk
• Robots Podcast: A robot fly at Harvard and at the MoMA
• Developing Jibo: Animating the eye

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