You and AI Conversations about AI technologies and their implications for society

SUPPORTED BY CONVERSATIONS ABOUT AI TECHNOLOGIES AND THEIR IMPLICATIONS FOR SOCIETY DeepMind1 2 CONVERSATIONS ABOUT AI TECHNOLOGIES AND THEIR IMPLICATIONS FOR SOCIETY You and AI Conversations about AI technologies and their implications for society

Artificial Intelligence (AI) is the science of making computer systems smart, and an umbrella term for a range of technologies that carry out functions that typically require intelligence in humans. AI technologies already support many everyday products and services, and the power and reach of these technologies are advancing at pace.

The Royal Society is working to support an environment of careful stewardship of AI technologies, so that their benefits can be brought into being safely and rapidly, and shared across society. In support of this aim, the Society’s You and AI series brought together leading AI researchers to contribute to a public conversation about advances in AI and their implications for society.

CONVERSATIONS ABOUT AI TECHNOLOGIES AND THEIR IMPLICATIONS FOR SOCIETY 3 What AI can, and cannot, do

The last decade has seen exciting developments in AI – and AI researchers are tackling some fundamental challenges to develop it further

AI research seeks to understand what happens or inputs do not follow a standard intelligence is, and then recreate this through pattern, these systems cannot adapt their computer systems that can automatically rules or adjust their approach. perform tasks that require some level of reasoning or intelligence in humans. In the last decade, new methods that use learning algorithms have helped create In the past, AI research has concentrated computer systems that are more flexible on creating detailed rules for how to carry and adaptive, and Demis Hassabis FRS out a task and then developing computer (co-founder, DeepMind) has been at the systems that could carry out these rules; forefront of many of these developments. Dr Demis Hassabis researchers and programmers would FREng FRS develop a solution to a problem, codify this AI technologies today are a topic of Co-founder, DeepMind into a computer program, and in so doing widespread interest, and there is a lot In a lecture exploring create a system to implement the solution. of speculation (and hype) about what AI the frontiers of AI, Demis might or might not achieve in the coming Hassabis explained By pre-programming computer systems years. However, there remain significant how AI algorithms can with rules in this way, computer systems limitations to what AI technologies can do learn how to carry out a can perform as well as experts in certain, (see Current challenges in AI research). For task, by analysing data specific tasks. However, while these some, these limitations make AI particularly and experiencing their systems have been successful in highly exciting, as they generate new lines of environment. Instead of specific domains – such as playing research that address profound questions relying on designers to chess – they have a significant weakness: about the nature of intelligence. specify a solution to a inflexibility. If something unexpected problem, these algorithms use data to figure out how to solve a problem.

Image: Dr Demis Hassabis FREng FRS speaking with Professor Andrew Hopper FREng FRS, Treasurer and Vice-President of the Royal Society.

4 CONVERSATIONS ABOUT AI TECHNOLOGIES AND THEIR IMPLICATIONS FOR SOCIETY Current challenges in AI research

Unsupervised learning

Many recent advances in AI have been based on techniques called supervised learning or reinforcement learning. In supervised learning, a system is trained using many labelled examples of the correct solutions to a problem, so it can adjust its processes to get the right answer. In reinforcement learning, a system gets a score or reward for getting the right (or wrong) answer. Unsupervised learning deals with situations where systems do not have access to a labelled dataset and there is not an obvious reward system. Humans can learn in this type of environment, but it is challenging to create AI systems that can.

Memory and one-shot learning

Humans are capable of learning very quickly, sometimes from experiencing only one example of a task or problem. So-called ‘one shot’ learning is challenging for AI systems.

Imagination-based planning

When thinking about how to approach a task, humans can visualise different scenarios and the consequences of acting in different ways in those scenarios. This allows efficient planning for how a situation might play out in real life – thinking about the consequences of different actions before committing to a particular course.

Learning abstract concepts

As well as being able to plan detailed actions in an environment, humans also have a sense of high level concepts like language.

Transfer learning

Transfer learning is the process of taking knowledge from one area and applying it to a new problem that, on the surface, seems different, but which has similar underlying structures.

Language understanding

While humans can learn many different languages, understanding natural language remains a key challenge in AI – AI voice recognition systems can recognise words, but natural language processing to understand meaning at a higher level is more difficult.

CONVERSATIONS ABOUT AI TECHNOLOGIES AND THEIR IMPLICATIONS FOR SOCIETY 5 Technologies with AI at their heart have the power to change the world

AI technologies already support many everyday products and services, with further applications to come

Understanding the weather and climate Improving diagnostics and healthcare To understand daily weather patterns, One area of significant excitement around meteorologists use AI techniques and the application of AI is in healthcare, and simulations that model the Earth’s Antonio Criminisi (Principal Researcher, atmosphere, ocean, and land surface, Microsoft Research) is helping drive the drawing from a combination of the development of such applications. One of fundamental laws of physics, complex the most difficult tasks for doctors treating maths, and a large amount of data about cancer patients at present is in previous patterns in order to make measuring tumours. Producing accurate predictions about the future. Julia Slingo measurements is important in tracking Professor Dame Julia FRS (Chair, Cabot Institute, University whether a tumour is growing or shrinking, Slingo DBE FRS of Bristol) uses advanced statistics to which indicates how it is responding to Chair, Cabot Institute, understand the weather. As the predictive treatment, and in creating more effective University of Bristol power of such techniques improves, treatment pathways. AI technologies are In an event on AI such analyses can also help inform policy helping create new systems to measure applications Julia debates about the likelihood of extreme tumours, analysing scans and images and Slingo explained how weather events, such as flooding. assisting medical experts to delineate or AI techniques can segment tumours, and to assess how they help create the three- are responding to treatments. dimensional descriptions of the atmosphere and oceans that meteorologists use to predict local weather patterns.

Image: A panel discussion featuring Professor Marcus du Sautoy FRS, Professor Dame Julia Slingo FRS, Professor Suzanne Aigrain, Professor Steve Young FREng, and Dr Antonio Criminisi explored current and near-term applications of AI.

6 CONVERSATIONS ABOUT AI TECHNOLOGIES AND THEIR IMPLICATIONS FOR SOCIETY Interacting with computers and devices Supporting advances in science by voice recognition AI technologies are already helping Advances in AI technologies, such as deep scientists observe and understand the learning, have dramatically increased the universe, extracting insights from large accuracy of automatic speech recognition datasets to detect events or areas of systems that allow virtual personal assistants interest for further research. In analysing on smartphones and other devices to data from the Keppler satellite, for example, recognise and respond to commands. Steve AI algorithms can help pick out small Young (Professor of Information Engineering, signals that indicate the existence of University of Cambridge, and Senior Member planets from the large amounts of noisy of Technical Staff – Siri Team, Apple) is data picked up by the satellite. helping create these systems, and explained that the technologies that underpin these In future, such techniques may become conversational agents are improving quickly. even more important as a tool to extract In future there may be more sophisticated insights from ever-growing datasets. The systems that can communicate with users Square Kilometer Array, for example, will in natural language and that have the collect huge amounts of data from hundreds authority to execute transactions on behalf of stations, with an expected raw data flow of their owner. Unlocking this potential over five times the size of 2015’s global requires further technical advances, for internet traffic. Researchers – such as example in learning human conversational Suzanne Aigrain (Professor of Astrophysics, skills and improving systems’ common University of Oxford), who is applying AI to sense reasoning. There are also broader astronomy – will rely on algorithmic systems questions about the ethics of data use by to process this data, identifying which data these systems – learning to answer queries to keep for further analysis, by screening requires access to large pools of data, for known phenomena or important and which could raise privacy concerns – and unexpected events. questions about how society expects such system to answer ‘unethical’ questions. To make best use of these techniques, scientists will need to understand how to effectively implement AI systems in their work. This raises questions about how best to combine subject-specific expertise with technical AI know-how, and how to design systems that produce results, which can be interpreted by their users.

CONVERSATIONS ABOUT AI TECHNOLOGIES AND THEIR IMPLICATIONS FOR SOCIETY 7 There are big questions about how societies want to use AI

As the power and reach of AI technologies expands, there are new questions and issues for society

The growing variety and reach of these new New research is developing ways of applications mean that AI is no longer only managing bias in data, for example a theoretical research domain – it is also an by removing sensitive information – area of research that is applied at scale. sometimes referred to as ‘scrubbing to neutral’ – before that data is used to With this scale and influence come develop AI systems. However, many of questions about its societal implications. these current attempts to remove bias from Cynthia Dwork (Professor of Computer AI are very narrow, and remain difficult to Science, Harvard) and Kate Crawford apply in some of the areas where fairness (Principal Researcher, Microsoft Research, matters most, which are typically some of Professor Cynthia Dwork and Director, AI Now Institute) explored the most complex policy areas. Professor of such questions in two events on algorithmic Computer Science, fairness and the politics of AI. Questions about how to build fair Harvard University algorithms are the subject of increasing Cynthia Dwork explored Fairness and bias in AI interest in technical communities and an emerging theory of The datasets on which AI technologies are ideas about how to create technical ‘fixes’ algorithmic fairness, trained to carry out a task reflect society, to tackle issues of fairness are evolving, and the challenges and can contain the biases that were but fairness remains a challenging issue. of creating technical embedded in processes, relationships, or Notions of fairness can relate to groups, solutions to issues structures at the point of data collection. and whether different social groups are of bias and fairness in AI. When this data is used to develop AI, the treated equally or experience similar resulting systems reflect back the social outcomes, or individual expectations and cultural structures or practices of about personal outcomes. People think the past; this means the biases that have about fairness in different ways, drawing shaped society in the past (or shape it from ideas about equality of treatment or today) can form the basis of predictions opportunity, or perceptions of what is just or recommendations about future action. or right.

This has implications for how AI systems Technical fixes alone cannot answer these work – or fail to work – for different bigger questions about what fairness is or communities or users. Examples of such the type of social outcomes societies want failures in recent years have included: AI systems to help create. They require an Professor Kate Crawford women being less likely to be shown adverts understanding of the broader forces that Principal Researcher, for highly paid jobs, companies not making influence how and where AI systems are put Microsoft Research, and deliveries to poorer neighbourhoods, and to use, and the social forces that shape who Director, AI Now Institute racial disparities in approaches to policing designs AI systems and for whose benefit. or treatment by the justice system. Kate Crawford Thinking about the politics of AI in this discussed the politics of way becomes especially important when AI, and the implications examining the broader social and economic of its widening use for ramifications of these technologies, such as social inequalities. the impact of AI on work.

8 CONVERSATIONS ABOUT AI TECHNOLOGIES AND THEIR IMPLICATIONS FOR SOCIETY AI-enabled automation and work The extent to which AI contributes to a AI technologies are enabling the broader, shared prosperity depends on automation of a growing range of tasks that the policies, structures, and institutions in are typically carried out by humans. These place. Education and skills policy, corporate capabilities have promoted widespread governance, anti-trust and market policies, speculation about the impact of AI on work, labour relations, and employment practices whether by influencing wage growth or all play a role in shaping how AI is adopted unemployment rates. and its influence on work. Ultimately, the extent to which the benefits of AI-enabled AI will have a disruptive effect on work, with economic growth are felt across society Professor Joseph Stiglitz some jobs being lost, others being created, will depend on the rules of the game that ForMemRS and others changing. Drawing lessons such policies and institutions set in place, Professor of Economics, from previous waves of technology change and active consideration is needed now Columbia University and current economic conditions, Joseph about how society can ensure that the Stiglitz ForMemRS (Professor of Economics, increased use of AI is not accompanied Joseph Stiglitz discussed Columbia University) explained that by increased inequality. the impact of AI on technology-enabled changes to work tend work and employment, to affect lower-paid and lower-qualified exploring the potential workers more than others. implications for societal inequalities and the policy responses that could help address these.

CONVERSATIONS ABOUT AI TECHNOLOGIES AND THEIR IMPLICATIONS FOR SOCIETY 9 Sharing the benefits of AI across society

An environment of careful stewardship can help ensure that the benefits of AI are shared across society

AI technologies could support new projects, Then Peter Donnelly FRS (Professor of services, and economic growth that have the Statistical Science, University of Oxford), potential to bring widespread benefits. As Vivienne Ming (co-founder, Socos Labs), the power and reach of these technologies and Suchi Saria (Professor of Machine grow, there are important questions for Learning and Healthcare, Johns Hopkins societies to grapple with about the ways in University) debated who will likely be most Dame Wendy Hall which AI should – or should not – be used. affected by AI technologies, how their DBE FREng FRS impacts will be distributed across society, Regius Professor of To close the series, two panels of experts and what type of interventions might be Computer Science, answered questions from the public about necessary to address concerns about bias, University of Southampton who is – or is not – currently benefitting transparency and accountability, and the During a panel discussion from AI, and how its benefits can be future of work. chaired by Professor Jim shared across society. Al-Khalili FRS, Wendy With such significant potential benefits – Hall spoke about how the Wendy Hall FRS (Regius Professor and such significant potential disruption to UK can remain a leading of Computer Science, University of lives and livelihoods – it is important there player in the development Southampton), Neil Lawrence (Director is a public conversation about AI and its of AI technologies. of Machine Learning, Amazon Research societal implications. The Royal Society is Cambridge), and Ewa Luger (Fellow in working to facilitate an open and robust Digital Arts and Humanities, University conversation about the potential and pitfalls of Edinburgh) considered how AI could of AI, and the lectures, videos, and other benefit communities across the UK, and materials generated by the You and AI how the UK can maintain a leading role programme aim to support this conversation. in leading the development of these technologies internationally. You can find out more at royalsociety.org

Image: Panel from left to right: Dr Vivienne Ming, Professor Peter Donnelly FMedSci FRS, Professor Suchi Saria with Professor Brian Cox CBE FRS.

10 CONVERSATIONS ABOUT AI TECHNOLOGIES AND THEIR IMPLICATIONS FOR SOCIETY Thank you

The Royal Society would like to thank all those who contributed to these events:

Professor Jim Al-Khalili OBE FRS Dr Demis Hassabis CBE FREng FRS

Professor Suzanne Aigrain Professor Andrew Hopper CBE FREng FRS

Professor Chris Bishop FREng FRS Dr Neil Lawrence

Professor Brian Cox OBE FRS Dr Ewa Luger

Dr Antonio Criminisi Dr Vivienne Ming

Professor Kate Crawford Professor Suchi Saria

Professor Sir Peter Donnelly FMedSci FRS Professor Marcus du Sautoy OBE FRS

Professor Cynthia Dwork Professor Dame Julia Slingo DBE FRS

Dame Wendy Hall DBE FREng FRS Professor Joseph Stiglitz ForMemRS

Timandra Harkness Professor Steve Young FREng

CONVERSATIONS ABOUT AI TECHNOLOGIES AND THEIR IMPLICATIONS FOR SOCIETY 11 The Royal Society is a self-governing Fellowship of many of the world’s most distinguished scientists drawn from all areas of science, engineering, and medicine. The Society’s fundamental purpose, as it has been since its foundation in 1660, is to recognise, promote, and support excellence in science and to encourage the development and use of science for the benefit of humanity.

The Society’s strategic priorities emphasise its commitment to the highest quality science, to curiosity-driven research, and to the development and use of science for the benefit of society. These priorities are: • Promoting excellence in science • Supporting international collaboration • Demonstrating the importance of science to everyone

For further information The Royal Society 6 – 9 Carlton House Terrace London SW1Y 5AG T +44 20 7451 2500 W royalsociety.org

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