̽��ֱ�� of Cambridge - Adrian Weller

Queen's Birthday Honours 2022

cjb250 — Wed, 01 Jun 2022 21:00:04 +0000

Leaders in fields from chemistry to cancer research and computing are among the Cambridge academics recognised today.

Fake news, black holes and AI: Cambridge academics to speak at Hay Festival

mjg209 — Tue, 26 Mar 2019 11:00:00 +0000

̽��ֱ��Series is now an established feature of the Hay Festival and is now in its eleventh year. This year’s speakers include experts on the localised effects of climate change, combatting fake news, black holes, food security and the impact of dinosaurs on the British landscape.

̽��ֱ��Series is part of the ̽��ֱ�� of Cambridge’s commitment to public engagement. ̽��ֱ��Festival runs from 25th May to 2nd June and is now open for bookings.

Several speakers will address how experts navigate a world of fake news and artificial intelligence. Bill Sutherland, Miriam Rothschild Chair in Conservation Biology, will describe attempts to make global evidence available to all, improve the effectiveness of experts and change attitudes toward the use of evidence, especially in relation to conservation. Sander van der Linden from the Cambridge Social Decision-Making Lab and Department of Psychology will speak about how we can counter fake news and whether we can inoculate the public against misinformation. His forthcoming book will investigate the psychology of trust and how to communicate about facts and evidence in a post-truth society. Rapid changes in the use of artificial intelligence and the social and ethical implications of these will be discussed by Adrian Weller, a senior research fellow in machine learning.

Other speakers will address how reading is being transformed in a digital age. Writer, editor and researcher Tyler Shores will explore reading in an age of digital distraction while literacy education expert Fiona Maine will speak about the potential of complex, ambiguous wordless picturebooks and short films as springboards for children’s critical and creative discussions about the world and how we live in it.

From the world of science speakers include Professor Nicole Soranzo on the evolution of human genetics and how new genetic evidence is being used to better understand the interplay between our DNA (‘nature’) and the environment (‘nurture’). Professor Christopher Reynolds will describe how black holes stretch our understanding of space-time to the limits and power some of the most energetic phenomena in the Universe. Neuroscientist Professor Paul Fletcher will explain how different processes in the brain can lead to seemingly irrational decisions when it comes to what we eat. Dr Catherine Aitken will explore how life in the womb affects not only children’s lifelong health and well-being, but maybe even that of grandchildren.

Responses to climate change feature in several Cambridge Series sessions: climate change scientist Emily Shuckburgh will speak about her research on modelling localised effects of climate change and will also be in conversation with former Irish president Mary Robinson about climate justice. Another Cambridge Series session on female voices on climate change will see a panel of researchers talk about what kind of adaptations may be required as global warming increases and how we bring a broad range of the public on board, particularly with regard to the more complex issues around climate change. Speakers include Chandrika Nath, executive director of the Scientific Committee on Antarctic Research, Professor Melody Clark from the British Antarctic Survey and two Gates Cambridge Scholars - Morgan Seag, co-chair of the international council of the Association of Polar Early Career Scientists, and anthropologist Ragnhild Freng Dale from the Scott Polar Research Institute and the Western Norway Research Institute.

Other sessions explore issues of identity. Professor Michael Kenny will take part in a panel discussion on Brexit and the politics of national identity in the UK with Welsh government minister Eluned Morgan and Adam Price, leader of Plaid Cymru, while economist Victoria Bateman will address the role of women in the economic rise of the West. Her new book ̽��ֱ��Sex Factor - how women made the West rich argues that, far from the Industrial Revolution being all about male inventors and industrialists, the everyday woman underpinned Britain’s – and the West’s - rise.

For those interested in the more distant past Anthony Shillito and Neil Davies will explore their research on how ancient creatures, from dinosaurs to giant millipedes, shaped the land around them and what secrets are held within their prehistoric footprints. Martin Jones, Emeritus Professor of Archaeological Science at the ̽��ֱ�� of Cambridge, will discuss the vital question of food security, showing how our prehistoric ancestors built resilience into their food supply and what we can learn from them.

Peter Florence, director of Hay Festival, said: "Cambridge ̽��ֱ�� is home to some of the world's greatest thinkers, at the forefront of debate and exploration in the arts, sciences and global affairs. We're proud to open those ideas into conversations that resonate around the world from our field in Wales. Join us."

Ariel Retik, who oversees the Cambridge Series, said: “We are proud to continue our valued relationship with Hay. ̽��ֱ��Festival is a wonderful way of sharing with the public the research and learning that happens in Cambridge. We have found that Hay audiences are diverse, engaged and intellectually curious. They are an incredible cross-section of the public: from potential students and tourists, to journalists and policy-makers – everyone is represented. They are always interested in the research and, importantly, ask fantastic and challenging questions! We are excited for another year of talks and debates around the research and emerging ideas from Cambridge, which have global relevance and potential for world-changing impact."

Other ̽��ֱ�� of Cambridge speakers at the Festival include Professor Martin Rees, neuroscientist Giles Yeo, author and lecturer Robert Macfarlane and neuroscientist Hannah Critchlow. Charlie Gilderdale, NRICH Project Secondary Coordinator, will once again be running maths masterclasses with Alison Eves from the Royal Institution.

Book tickets

Full line-up of the Cambridge Series

Nineteen academics from a wide range of disciplines will take part in this year’s Cambridge Series of talks at the Hay Festival, one of the most prestigious literary festivals in the world.

We are excited for another year of talks and debates around the research and emerging ideas from Cambridge, which have global relevance and potential for world-changing impact

Ariel Retik

Sam Hardwick

Hay Festival

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Yes

In tech we trust?

lw355 — Fri, 23 Feb 2018 09:30:00 +0000

Dr Jat Singh is familiar with breaking new ground and working across disciplines. Even so, he and colleagues were pleasantly surprised by how much enthusiasm has greeted their new Strategic Research Initiative on Trustworthy Technologies, which brings together science, technology and humanities researchers from across the ̽��ֱ��.

In fact, Singh, a researcher in Cambridge’s Department of Computer Science and Technology, has been collaborating with lawyers for several years: “A legal perspective is paramount when you’re researching the technical dimensions to compliance, accountability and trust in emerging ICT; although the Computer Lab is not the usual home for lawyers, we have two joining soon.”

Governance and public trust present some of the greatest challenges in technology today. ̽��ֱ��European General Data Protection Regulation (GDPR), which comes into force this year, has brought forward debates such as whether individuals have a ‘right to an explanation’ regarding decisions made by machines, and introduces stiff penalties for breaching data protection rules. “With penalties including fines of up to 4% of global turnover or €20 million, people are realising that they need to take data protection much more seriously,” he says.

Singh is particularly interested in how data-driven systems and algorithms – including machine learning – will soon underpin and automate everything from transport networks to council services.

As we work, shop and travel, computers and mobile phones already collect, transmit and process much data about us; as the ‘Internet of Things’ continues to instrument the physical world, machines will increasingly mediate and influence our lives.

It’s a future that raises profound issues of privacy, security, safety and ultimately trust, says Singh, whose research is funded by an Engineering and Physical Sciences Research Council Fellowship: “We work on mechanisms for better transparency, control and agency in systems, so that, for instance, if I give data to someone or something, there are means for ensuring they’re doing the right things with it. We are also active in policy discussions to help better align the worlds of technology and law.”

What it means to trust machine learning systems also concerns Dr Adrian Weller. Before becoming a senior research fellow in the Department of Engineering and a Turing Fellow at ̽��ֱ��Alan Turing Institute, he spent many years working in trading for leading investment banks and hedge funds, and has seen first-hand how machine learning is changing the way we live and work.

“Not long ago, many markets were traded on exchanges by people in pits screaming and yelling,” Weller recalls. “Today, most market making and order matching is handled by computers. Automated algorithms can typically provide tighter, more responsive markets – and liquid markets are good for society.”

But cutting humans out of the loop can have unintended consequences, as the flash crash of 2010 shows. During 36 minutes on 6 May, nearly one trillion dollars were wiped off US stock markets as an unusually large sell order produced an emergent coordinated response from automated algorithms. “ ̽��ֱ��flash crash was an important example illustrating that over time, as we have more AI agents operating in the real world, they may interact in ways that are hard to predict,” he says.

Algorithms are also beginning to be involved in critical decisions about our lives and liberty. In medicine, machine learning is helping diagnose diseases such as cancer and diabetic retinopathy; in US courts, algorithms are used to inform decisions about bail, sentencing and parole; and on social media and the web, our personal data and browsing history shape the news stories and advertisements we see.

How much we trust the ‘black box’ of machine learning systems, both as individuals and society, is clearly important. “There are settings, such as criminal justice, where we need to be able to ask why a system arrived at its conclusion – to check that appropriate process was followed, and to enable meaningful challenge,” says Weller. “Equally, to have effective real-world deployment of algorithmic systems, people will have to trust them.”

But even if we can lift the lid on these black boxes, how do we interpret what’s going on inside? “There are many kinds of transparency,” he explains. “A user contesting a decision needs a different kind of transparency to a developer who wants to debug a system. And a third form of transparency might be needed to ensure a system is accountable if something goes wrong, for example an accident involving a driverless car.”

If we can make them trustworthy and transparent, how can we ensure that algorithms do not discriminate unfairly against particular groups? While it might be useful for Google to advertise products it ‘thinks’ we are most likely to buy, it is more disquieting to discover the assumptions it makes based on our name or postcode.

When Latanya Sweeney, Professor of Government and Technology in Residence at Harvard ̽��ֱ��, tried to track down one of her academic papers by Googling her name, she was shocked to be presented with ads suggesting that she had been arrested. After much research, she discovered that “black-sounding” names were 25% more likely to result in the delivery of this kind of advertising.

Like Sweeney, Weller is both disturbed and intrigued by examples of machine-learned discrimination. “It’s a worry,” he acknowledges. “And people sometimes stop there – they assume it’s a case of garbage in, garbage out, end of story. In fact, it’s just the beginning, because we’re developing techniques that can automatically detect and remove some forms of bias.”

Transparency, reliability and trustworthiness are at the core of Weller’s work at the Leverhulme Centre for the Future of Intelligence and ̽��ֱ��Alan Turing Institute. His project grapples with how to make machine-learning decisions interpretable, develop new ways to ensure that AI systems perform well in real-world settings, and examine whether empathy is possible – or desirable – in AI.

Machine learning systems are here to stay. Whether they are a force for good rather than a source of division and discrimination depends partly on researchers such as Singh and Weller. ̽��ֱ��stakes are high, but so are the opportunities. Universities have a vital role to play, both as critic and conscience of society. Academics can help society imagine what lies ahead and decide what we want from machine learning – and what it would be wise to guard against.

Weller believes the future of work is a huge issue: “Many jobs will be substantially altered if not replaced by machines in coming decades. We need to think about how to deal with these big changes.”And academics must keep talking as well as thinking. “We’re grappling with pressing and important issues,” he concludes. “As technical experts we need to engage with society and talk about what we’re doing so that policy makers can try to work towards policy that’s technically and legally sensible.”

Inset image: read more about our AI research in the ̽��ֱ��'s research magazine; download a pdf; view on Issuu.

Fairness, trust and transparency are qualities we usually associate with organisations or individuals. Today, these attributes might also apply to algorithms. As machine learning systems become more complex and pervasive, Cambridge researchers believe it’s time for new thinking about new technology.

With penalties including fines of up to €20 million, people are realising that they need to take data protection much more seriously

Jat Singh

Daniel Werbrouck

Want to hear more?

Join us at the Cambridge Science Festival to hear Adrian Weller discuss how we can ensure AI systems are transparent, reliable and trustworthy.

Thursday 15 March 2018, 7:30pm - 8:30pm

Mill Lane Lecture Rooms, 8 Mill Lane, Cambridge, UK, CB2 1RW

BOOK HERE

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. For image use please see separate credits above.

Yes

Preparing for the future: artificial intelligence and us

lw355 — Fri, 02 Feb 2018 09:00:13 +0000

AI systems are now used in everything from the trading of stocks to the setting of house prices; from detecting fraud to translating between languages; from creating our weekly shopping lists to predicting which movies we might enjoy.

This is just the beginning. Soon, AI will be used to advance our understanding of human health through analysis of large datasets, help us discover new drugs and personalise treatments. Self-driving vehicles will transform transportation and allow new paradigms in urban planning. Machines will run our homes more efficiently, make businesses more productive and help predict risks to society.

While some AI systems will outperform human intelligence to augment human decision making, others will carry out repetitive, manual and dangerous tasks to augment human labour. Many of the greatest challenges we face, from understanding and mitigating climate change to quickly identifying and containing disease outbreaks, will be aided by the tools of AI.

What we’ve seen of AI so far is only the leading edge of the revolution to come./system/files/issue_35_research_horizons_new.pdf

Yet the idea of creating machines that think and learn like humans has been around since the 1950s. Why is AI such a hot topic now? And what does Cambridge have to offer?

Three major advances are enabling huge progress in AI research: the availability of masses of data generated by all of us all the time; the power and processing speeds of today’s supercomputers; and the advances that have been made in mathematics and computer science to create sophisticated algorithms that help machines learn.

Unlike in the past when computers were programmed for specific tasks and domains, modern machine learning systems know nothing about the topic in question, they only know about learning: they use huge amounts of data about the world in order to learn from it and to make predictions about future behaviour. They can make sense of complex datasets that are difficult to use and have missing data.

That these advances will provide tremendous benefits is becoming clear. One strand of the UK government’s Industrial Strategy is to put the UK at the forefront of the AI and data revolution. In 2017, a report by PricewaterhouseCoopers described AI as “the biggest commercial opportunity in today’s fast-changing economy”, predicting a 10% increase in the UK’s GDP by 2030 as a result of the applications of AI.

Cambridge ̽��ֱ�� is helping to drive this revolution – and to prepare for it.

Our computer scientists are designing systems that are cybersecure, model human reasoning, interact in affective ways with us, uniquely identify us by our face and give insights into our biological makeup.

Our engineers are building machines that are making decisions under uncertain conditions based on probabilistic estimation of perception and for the best course of action. And they’re building robots that can carry out a series of actions in the physical world – whether it’s for self-driving cars or for picking lettuces.

Our researchers in a multitude of different disciplines are creating innovative applications of AI in areas as diverse as discovering new drugs, overcoming phobias, helping to make police custody decisions and forecasting extreme weather events.

Our philosophers and humanists are asking fundamental questions about the ethics, trust and humanity of AI system design, and the effect that the language of discussion has on the public perception of AI. Together with the work of our engineers and computer scientists, these efforts aim to create AI systems that are trustworthy and transparent in their workings – that do what we want them to do.

All of this is happening in a university research environment and wider ecosystem of start-ups and large companies that facilitates innovative breakthroughs in AI. ̽��ֱ��aim of this truly interdisciplinary approach to research at Cambridge is to invent transformative AI technology that will benefit society at large.

However, transformative advances may carry negative consequences if we do not plan for them carefully on a societal level.

̽��ֱ��fundamental advances that underpin self-driving cars may allow dangerous new weapons on the battlefield. Technologies that automate work may result in livelihoods being eliminated. Algorithms trained on historical data may perpetuate, or even exacerbate, biases and inequalities such as sex- or race-based discrimination. Without careful planning, systems for which large amounts of personal data is essential, such as in healthcare, may undermine privacy.

Engaging with these challenges requires drawing on expertise not just from the sciences, but also from the arts, humanities and social sciences, and requires delving deeply into questions of policy and governance for AI. Cambridge has taken a leading position here too, with the recent establishment of the Leverhulme Centre for the Future of Intelligence and the Centre for the Study of Existential Risk, as well as being one of the founding partners of ̽��ֱ��Alan Turing Institute based in London.

In the longer term, it is not outside the bounds of possibility that we might develop systems able to match or surpass human intelligence in the broader sense. There are some who think that this would change humanity’s place in the world irrevocably, while others look forward to the world a superintelligence might be able to co-create with us.

As the ̽��ֱ�� where the great mathematician Alan Turing was an undergraduate and fellow, it seems entirely fitting that Cambridge’s scholars are exploring questions of such significance to prepare us for the revolution to come. Turing once said: “we can only see a short distance ahead, but we can see plenty there that needs to be done.”

Inset image: read more about our AI research in the ̽��ֱ��'s research magazine; download a pdf; view on Issuu.

Dr Mateja Jamnik (Department of Computer Science and Technology), Dr Seán Ó hÉigeartaigh (Centre for the Study of Existential Risk and the Leverhulme Centre for the Future of Intelligence, CFI), Dr Beth Singler (Faraday Institute for Science and Religion and CFI) and Dr Adrian Weller (Department of Engineering, CFI and ̽��ֱ��Alan Turing Institute).

Today we begin a month-long focus on research related to artificial intelligence. Here, four researchers reflect on the power of a technology to impact nearly every aspect of modern life – and why we need to be ready.

What we’ve seen of AI so far is only the leading edge of the revolution to come.

Mateja Jamnik, Seán Ó hÉigeartaigh, Beth Singler and Adrian Weller

Jonathan Settle / ̽��ֱ�� of Cambridge

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. For image use please see separate credits above.

Yes

Artificial intelligence: computer says YES (but is it right?)

lw355 — Thu, 20 Oct 2016 14:17:17 +0000

There would always be a first death in a driverless car and it happened in May 2016. Joshua Brown had engaged the autopilot system in his Tesla when a tractor-trailor drove across the road in front of him. It seems that neither he nor the sensors in the autopilot noticed the white-sided truck against a brightly lit sky, with tragic results.

Of course many people die in car crashes every day – in the USA there is one fatality every 94 million miles, and according to Tesla this was the first known fatality in over 130 million miles of driving with activated autopilot. In fact, given that most road fatalities are the result of human error, it has been said that autonomous cars should make travelling safer.

Even so, the tragedy raised a pertinent question: how much do we understand – and trust – the computers in an autonomous vehicle? Or, in fact, in any machine that has been taught to carry out an activity that a human would do?

We are now in the era of machine learning. Machines can be trained to recognise certain patterns in their environment and to respond appropriately. It happens every time your digital camera detects a face and throws a box around it to focus, or the personal assistant on your smartphone answers a question, or the adverts match your interests when you search online.

Machine learning is a way to program computers to learn from experience and improve their performance in a way that resembles how humans and animals learn tasks. As machine learning techniques become more common in everything from finance to healthcare, the issue of trust is becoming increasingly important, says Zoubin Ghahramani, Professor of Information Engineering in Cambridge's Department of Engineering.

Faced with a life or death decision, would a driverless car decide to hit pedestrians, or avoid them and risk the lives of its occupants? Providing a medical diagnosis, could a machine be wildly inaccurate because it has based its opinion on a too-small sample size? In making financial transactions, should a computer explain how robust is its assessment of the volatility of the stock markets?

“Machines can now achieve near-human abilities at many cognitive tasks even if confronted with a situation they have never seen before, or an incomplete set of data,” says Ghahramani. “But what is going on inside the ‘black box’? If the processes by which decisions were being made were more transparent, then trust would be less of an issue.”

His team builds the algorithms that lie at the heart of these technologies (the “invisible bit” as he refers to it). Trust and transparency are important themes in their work: “We really view the whole mathematics of machine learning as sitting inside a framework of understanding uncertainty. Before you see data – whether you are a baby learning a language or a scientist analysing some data – you start with a lot of uncertainty and then as you have more and more data you have more and more certainty.

“When machines make decisions, we want them to be clear on what stage they have reached in this process. And when they are unsure, we want them to tell us.”

One method is to build in an internal self-evaluation or calibration stage so that the machine can test its own certainty, and report back.

Two years ago, Ghahramani’s group launched the Automatic Statistician with funding from Google. ̽��ֱ��tool helps scientists analyse datasets for statistically significant patterns and, crucially, it also provides a report to explain how sure it is about its predictions.

“ ̽��ֱ��difficulty with machine learning systems is you don’t really know what’s going on inside – and the answers they provide are not contextualised, like a human would do. ̽��ֱ��Automatic Statistician explains what it’s doing, in a human-understandable form.”

Where transparency becomes especially relevant is in applications like medical diagnoses, where understanding the provenance of how a decision is made is necessary to trust it.

Dr Adrian Weller, who works with Ghahramani, highlights the difficulty: “A particular issue with new artificial intelligence (AI) systems that learn or evolve is that their processes do not clearly map to rational decision-making pathways that are easy for humans to understand.” His research aims both at making these pathways more transparent, sometimes through visualisation, and at looking at what happens when systems are used in real-world scenarios that extend beyond their training environments – an increasingly common occurrence.

“We would like AI systems to monitor their situation dynamically, detect whether there has been a change in their environment and – if they can no longer work reliably – then provide an alert and perhaps shift to a safety mode.” A driverless car, for instance, might decide that a foggy night in heavy traffic requires a human driver to take control.

Weller’s theme of trust and transparency forms just one of the projects at the newly launched £10 million Leverhulme Centre for the Future of Intelligence (CFI). Ghahramani, who is Deputy Director of the Centre, explains: “It’s important to understand how developing technologies can help rather than replace humans. Over the coming years, philosophers, social scientists, cognitive scientists and computer scientists will help guide the future of the technology and study its implications – both the concerns and the benefits to society.”

CFI brings together four of the world’s leading universities (Cambridge, Oxford, Berkeley and Imperial College, London) to explore the implications of AI for human civilisation. Together, an interdisciplinary community of researchers will work closely with policy-makers and industry investigating topics such as the regulation of autonomous weaponry, and the implications of AI for democracy.

Ghahramani describes the excitement felt across the machine learning field: “It’s exploding in importance. It used to be an area of research that was very academic – but in the past five years people have realised these methods are incredibly useful across a wide range of societally important areas.

“We are awash with data, we have increasing computing power and we will see more and more applications that make predictions in real time. And as we see an escalation in what machines can do, they will challenge our notions of intelligence and make it all the more important that we have the means to trust what they tell us.”

Artificial intelligence has the power to eradicate poverty and disease or hasten the end of human civilisation as we know it – according to a speech delivered by Professor Stephen Hawking 19 October 2016 at the launch of the Centre for the Future of Intelligence.

Computers that learn for themselves are with us now. As they become more common in ‘high-stakes’ applications like robotic surgery, terrorism detection and driverless cars, researchers ask what can be done to make sure we can trust them.

As we see an escalation in what machines can do, they will challenge our notions of intelligence and make it all the more important that we have the means to trust what they tell us

Zoubin Ghahramani

Thierry Ehrmann

2019 by ExperiensS

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. For image use please see separate credits above.

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