̽��ֱ�� of Cambridge - DeepMind

̽��ֱ�� of Cambridge alumni awarded 2024 Nobel Prize in Chemistry

Anonymous — Wed, 09 Oct 2024 10:21:22 +0000

In 2020, Hassabis and Jumper of Google DeepMind presented an AI model called AlphaFold2. With its help, they have been able to predict the structure of virtually all the 200 million proteins that researchers have identified.

Since their breakthrough, AlphaFold2 has been used by more than two million people from 190 countries. Among a myriad of scientific applications, researchers can now better understand antibiotic resistance and create images of enzymes that can decompose plastic.

̽��ֱ��duo received the Nobel along with Professor David Baker of the ̽��ֱ�� of Washington, who succeeded in using amino acids to design a new protein in 2003.

Sir Demis Hassabis read Computer Science as an undergraduate at Queens' College, Cambridge, matriculating in 1994. He went on to complete a PhD in cognitive neuroscience at ̽��ֱ�� College London and create the videogame company Elixir Studios.

Hassabis co-founded DeepMind in 2010, a company that developed masterful AI models for popular boardgames. ̽��ֱ��company was sold to Google in 2014 and, two years later, DeepMind came to global attention when the company achieved what many then believed to be the holy grail of AI: beating the champion player of one of the world’s oldest boardgames, Go.

In 2014, Hassabis was elected as a Fellow Benefactor and, later, as an Honorary Fellow of Queens' College. In 2024, he was knighted by the King for services to artificial intelligence.

In 2018, the ̽��ֱ�� announced the establishment of a DeepMind Chair of Machine Learning, thanks to a benefaction from Hassabis’s company, and appointed Professor Neil Lawrence to the position the following year.

“It is wonderful to see Demis’s work recognised at the highest level — his contributions have been really transformative across many domains. I’m looking forward to seeing what he does next!” said Professor Alastair Beresford, Head of the Department of Computer Science and Technology and Robin Walker Fellow in Computer Science at Queens' College.

In a statement released by Google DeepMind following the announcement by the Nobel committee, Hassabis said: "I’ve dedicated my career to advancing AI because of its unparalleled potential to improve the lives of billions of people... I hope we'll look back on AlphaFold as the first proof point of AI's incredible potential to accelerate scientific discovery."

Dr John Jumper completed an MPhil in theoretical condensed matter physics at Cambridge's famous Cavendish Laboratory in 2008, during which time he was a member of St Edmund’s College, before going on to receive his PhD in Chemistry from the ̽��ֱ�� of Chicago.

"Computational biology has long held tremendous promise for creating practical insights that could be put to use in real-world experiments," said Jumper, Director of Google DeepMind, in a statement released by the company. "AlphaFold delivered on this promise. Ahead of us are a universe of new insights and scientific discoveries made possible by the use of AI as a scientific tool."

“ ̽��ֱ��whole of the St Edmund’s community joins me in congratulating our former Masters student Dr John Jumper on this illustrious achievement – the most inspiring example imaginable to our new generation of students as they go through their matriculation this week,” said St Edmund’s College Master, Professor Chris Young.

Professor Deborah Prentice, Vice-Chancellor of the ̽��ֱ�� of Cambridge: “I’d like to congratulate Demis Hassabis and John Jumper, who, alongside Geoffrey Hinton yesterday, are all alumni of our ̽��ֱ��. Together, their pioneering work in the development and application of machine learning is transforming our understanding of the world around us. They join an illustrious line-up of Cambridge people to have received Nobel Prizes – now totalling 125 individuals – for which we can be very proud.”

Article updated on 10 October 2024 to reflect that the number of Cambridge people to have received Nobel Prizes now totals 125.

Two ̽��ֱ�� alumni, Sir Demis Hassabis and Dr John Jumper, have been jointly awarded this year’s Nobel Prize in Chemistry for developing an AI model to solve a 50-year-old problem: predicting the complex structures of proteins.

I have many happy memories from my time as an undergraduate at Cambridge

Sir Demis Hassabis

Left: Demis Hassabis; Right: John Jumper

̽��ֱ��text in this work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Images, including our videos, are Copyright © ̽��ֱ�� of Cambridge and licensors/contributors as identified. All rights reserved. We make our image and video content available in a number of ways – on our main website under its Terms and conditions, and on a range of channels including social media that permit your use and sharing of our content under their respective Terms.

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AI system self-organises to develop features of brains of complex organisms

cjb250 — Mon, 20 Nov 2023 16:00:47 +0000

As neural systems such as the brain organise themselves and make connections, they have to balance competing demands. For example, energy and resources are needed to grow and sustain the network in physical space, while at the same time optimising the network for information processing. This trade-off shapes all brains within and across species, which may help explain why many brains converge on similar organisational solutions.

Jascha Achterberg, a Gates Scholar from the Medical Research Council Cognition and Brain Sciences Unit (MRC CBU) at the ̽��ֱ�� of Cambridge said: “Not only is the brain great at solving complex problems, it does so while using very little energy. In our new work we show that considering the brain’s problem solving abilities alongside its goal of spending as few resources as possible can help us understand why brains look like they do.”

Co-lead author Dr Danyal Akarca, also from the MRC CBU, added: “This stems from a broad principle, which is that biological systems commonly evolve to make the most of what energetic resources they have available to them. ̽��ֱ��solutions they come to are often very elegant and reflect the trade-offs between various forces imposed on them.”

In a study published today in Nature Machine Intelligence, Achterberg, Akarca and colleagues created an artificial system intended to model a very simplified version of the brain and applied physical constraints. They found that their system went on to develop certain key characteristics and tactics similar to those found in human brains.

Instead of real neurons, the system used computational nodes. Neurons and nodes are similar in function, in that each takes an input, transforms it, and produces an output, and a single node or neuron might connect to multiple others, all inputting information to be computed.

In their system, however, the researchers applied a ‘physical’ constraint on the system. Each node was given a specific location in a virtual space, and the further away two nodes were, the more difficult it was for them to communicate. This is similar to how neurons in the human brain are organised.

̽��ֱ��researchers gave the system a simple task to complete – in this case a simplified version of a maze navigation task typically given to animals such as rats and macaques when studying the brain, where it has to combine multiple pieces of information to decide on the shortest route to get to the end point.

One of the reasons the team chose this particular task is because to complete it, the system needs to maintain a number of elements – start location, end location and intermediate steps – and once it has learned to do the task reliably, it is possible to observe, at different moments in a trial, which nodes are important. For example, one particular cluster of nodes may encode the finish locations, while others encode the available routes, and it is possible to track which nodes are active at different stages of the task.

Initially, the system does not know how to complete the task and makes mistakes. But when it is given feedback it gradually learns to get better at the task. It learns by changing the strength of the connections between its nodes, similar to how the strength of connections between brain cells changes as we learn. ̽��ֱ��system then repeats the task over and over again, until eventually it learns to perform it correctly.

With their system, however, the physical constraint meant that the further away two nodes were, the more difficult it was to build a connection between the two nodes in response to the feedback. In the human brain, connections that span a large physical distance are expensive to form and maintain.

When the system was asked to perform the task under these constraints, it used some of the same tricks used by real human brains to solve the task. For example, to get around the constraints, the artificial systems started to develop hubs – highly connected nodes that act as conduits for passing information across the network.

More surprising, however, was that the response profiles of individual nodes themselves began to change: in other words, rather than having a system where each node codes for one particular property of the maze task, like the goal location or the next choice, nodes developed a flexible coding scheme. This means that at different moments in time nodes might be firing for a mix of the properties of the maze. For instance, the same node might be able to encode multiple locations of a maze, rather than needing specialised nodes for encoding specific locations. This is another feature seen in the brains of complex organisms.

Co-author Professor Duncan Astle, from Cambridge’s Department of Psychiatry, said: “This simple constraint – it’s harder to wire nodes that are far apart – forces artificial systems to produce some quite complicated characteristics. Interestingly, they are characteristics shared by biological systems like the human brain. I think that tells us something fundamental about why our brains are organised the way they are.”

Understanding the human brain

̽��ֱ��team are hopeful that their AI system could begin to shed light on how these constraints, shape differences between people’s brains, and contribute to differences seen in those that experience cognitive or mental health difficulties.

Co-author Professor John Duncan from the MRC CBU said: “These artificial brains give us a way to understand the rich and bewildering data we see when the activity of real neurons is recorded in real brains.”

Achterberg added: “Artificial ‘brains’ allow us to ask questions that it would be impossible to look at in an actual biological system. We can train the system to perform tasks and then play around experimentally with the constraints we impose, to see if it begins to look more like the brains of particular individuals.”

Implications for designing future AI systems

̽��ֱ��findings are likely to be of interest to the AI community, too, where they could allow for the development of more efficient systems, particularly in situations where there are likely to be physical constraints.

Dr Akarca said: “AI researchers are constantly trying to work out how to make complex, neural systems that can encode and perform in a flexible way that is efficient. To achieve this, we think that neurobiology will give us a lot of inspiration. For example, the overall wiring cost of the system we've created is much lower than you would find in a typical AI system.”

Many modern AI solutions involve using architectures that only superficially resemble a brain. ̽��ֱ��researchers say their works shows that the type of problem the AI is solving will influence which architecture is the most powerful to use.

Achterberg said: “If you want to build an artificially-intelligent system that solves similar problems to humans, then ultimately the system will end up looking much closer to an actual brain than systems running on large compute cluster that specialise in very different tasks to those carried out by humans. ̽��ֱ��architecture and structure we see in our artificial ‘brain’ is there because it is beneficial for handling the specific brain-like challenges it faces.”

This means that robots that have to process a large amount of constantly changing information with finite energetic resources could benefit from having brain structures not dissimilar to ours.

Achterberg added: “Brains of robots that are deployed in the real physical world are probably going to look more like our brains because they might face the same challenges as us. They need to constantly process new information coming in through their sensors while controlling their bodies to move through space towards a goal. Many systems will need to run all their computations with a limited supply of electric energy and so, to balance these energetic constraints with the amount of information it needs to process, it will probably need a brain structure similar to ours.”

̽��ֱ��research was funded by the Medical Research Council, Gates Cambridge, the James S McDonnell Foundation, Templeton World Charity Foundation and Google DeepMind.

Reference
Achterberg, J & Akarca, D et al. Spatially embedded recurrent neural networks reveal widespread links between structural and functional neuroscience findings. Nature Machine Intelligence; 20 Nov 2023; DOI: 10.1038/s42256-023-00748-9

Cambridge scientists have shown that placing physical constraints on an artificially-intelligent system – in much the same way that the human brain has to develop and operate within physical and biological constraints – allows it to develop features of the brains of complex organisms in order to solve tasks.

Not only is the brain great at solving complex problems, it does so while using very little energy

Jascha Achterberg

DeltaWorks

Graphic representing an artificially intelligent brain

̽��ֱ��text in this work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Images, including our videos, are Copyright © ̽��ֱ�� of Cambridge and licensors/contributors as identified. All rights reserved. We make our image and video content available in a number of ways – as here, on our main website under its Terms and conditions, and on a range of channels including social media that permit your use and sharing of our content under their respective Terms.

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Public Domain

How sure is sure? Incorporating human error into machine learning

sc604 — Wed, 09 Aug 2023 23:40:58 +0000

Human error and uncertainty are concepts that many artificial intelligence systems fail to grasp, particularly in systems where a human provides feedback to a machine learning model. Many of these systems are programmed to assume that humans are always certain and correct, but real-world decision-making includes occasional mistakes and uncertainty.

Researchers from the ̽��ֱ�� of Cambridge, along with ̽��ֱ��Alan Turing Institute, Princeton, and Google DeepMind, have been attempting to bridge the gap between human behaviour and machine learning, so that uncertainty can be more fully accounted for in AI applications where humans and machines are working together. This could help reduce risk and improve trust and reliability of these applications, especially where safety is critical, such as medical diagnosis.

̽��ֱ��team adapted a well-known image classification dataset so that humans could provide feedback and indicate their level of uncertainty when labelling a particular image. ̽��ֱ��researchers found that training with uncertain labels can improve these systems’ performance in handling uncertain feedback, although humans also cause the overall performance of these hybrid systems to drop. Their results will be reported at the AAAI/ACM Conference on Artificial Intelligence, Ethics and Society (AIES 2023) in Montréal.

‘Human-in-the-loop’ machine learning systems – a type of AI system that enables human feedback – are often framed as a promising way to reduce risks in settings where automated models cannot be relied upon to make decisions alone. But what if the humans are unsure?

“Uncertainty is central in how humans reason about the world but many AI models fail to take this into account,” said first author Katherine Collins from Cambridge’s Department of Engineering. “A lot of developers are working to address model uncertainty, but less work has been done on addressing uncertainty from the person’s point of view.”

We are constantly making decisions based on the balance of probabilities, often without really thinking about it. Most of the time – for example, if we wave at someone who looks just like a friend but turns out to be a total stranger – there’s no harm if we get things wrong. However, in certain applications, uncertainty comes with real safety risks.

“Many human-AI systems assume that humans are always certain of their decisions, which isn’t how humans work – we all make mistakes,” said Collins. “We wanted to look at what happens when people express uncertainty, which is especially important in safety-critical settings, like a clinician working with a medical AI system.”

“We need better tools to recalibrate these models, so that the people working with them are empowered to say when they’re uncertain,” said co-author Matthew Barker, who recently completed his MEng degree at Gonville & Caius College, Cambridge. “Although machines can be trained with complete confidence, humans often can’t provide this, and machine learning models struggle with that uncertainty.”

For their study, the researchers used some of the benchmark machine learning datasets: one was for digit classification, another for classifying chest X-rays, and one for classifying images of birds. For the first two datasets, the researchers simulated uncertainty, but for the bird dataset, they had human participants indicate how certain they were of the images they were looking at: whether a bird was red or orange, for example. These annotated ‘soft labels’ provided by the human participants allowed the researchers to determine how the final output was changed. However, they found that performance degraded rapidly when machines were replaced with humans.

“We know from decades of behavioural research that humans are almost never 100% certain, but it’s a challenge to incorporate this into machine learning,” said Barker. “We’re trying to bridge the two fields so that machine learning can start to deal with human uncertainty where humans are part of the system.”

̽��ֱ��researchers say their results have identified several open challenges when incorporating humans into machine learning models. They are releasing their datasets so that further research can be carried out and uncertainty might be built into machine learning systems.

“As some of our colleagues so brilliantly put it, uncertainty is a form of transparency, and that’s hugely important,” said Collins. “We need to figure out when we can trust a model and when to trust a human and why. In certain applications, we’re looking at probability over possibilities. Especially with the rise of chatbots, for example, we need models that better incorporate the language of possibility, which may lead to a more natural, safe experience.”

“In some ways, this work raised more questions than it answered,” said Barker. “But even though humans may be miscalibrated in their uncertainty, we can improve the trustworthiness and reliability of these human-in-the-loop systems by accounting for human behaviour.”

̽��ֱ��research was supported in part by the Cambridge Trust, the Marshall Commission, the Leverhulme Trust, the Gates Cambridge Trust and the Engineering and Physical Sciences Research Council (EPSRC), part of UK Research and Innovation (UKRI).

Reference:
Katherine M Collins et al. ‘Human Uncertainty in Concept-Based AI Systems.’ Paper presented at the Sixth AAAI/ACM Conference on Artificial Intelligence, Ethics and Society (AIES 2023), August 8-10, 2023. Montréal, QC, Canada.

Researchers are developing a way to incorporate one of the most human of characteristics – uncertainty – into machine learning systems.

Uncertainty is central in how humans reason about the world but many AI models fail to take this into account

Katherine Collins

PeopleImages via Getty Images

Futuristic image of a doctor looking at brain scans

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New programme to accelerate AI research capability at Cambridge

sc604 — Tue, 23 Jun 2020 23:55:43 +0000

Supported by a donation from Schmidt Futures, a philanthropic initiative founded by Eric and Wendy Schmidt, the Accelerate Programme for Scientific Discovery will level the playing field for young researchers, providing them with specialised training in these powerful techniques, which have the potential to speed up the pace of discovery across a range of disciplines.

̽��ֱ��programme will initially be aimed at researchers in STEMM (science, technology, engineering, mathematics and medicine), but will grow to include arts, humanities and social science researchers who want to use machine learning skills to accelerate their research.

̽��ֱ��Accelerate Programme will be led by Professor Neil Lawrence, DeepMind Professor of Machine Learning.

“Machine learning and AI are increasingly part of our day-to-day lives, but they aren’t being used as effectively as they could be, due in part to major gaps of understanding between different research disciplines,” said Lawrence. “This programme will help us to close these gaps by training physicists, biologists, chemists and other scientists in the latest machine learning techniques, giving them the skills they need while accelerating the excellent research already taking place at the ̽��ֱ��.”

“As the intellectual home of Alan Turing, the father of artificial intelligence and modern computer science, Cambridge has long fostered technological innovation and invention,” said Vice-Chancellor Professor Stephen Toope. “This programme will help ensure that Cambridge continues to be a beacon for the very best young global researchers, and that we’re giving them the tools they need to thrive.”

̽��ֱ��five-year programme will be designed and delivered by four new early-career specialists, who will work with researchers from the Department of Computer Science and Technology as well as collaborators from industry. In the first year, the specialists will provide structured training in machine learning techniques to 32 PhD students and postdoctoral researchers, with training provided to a total of 160 PhD students and postdocs over the first five years of the programme. ̽��ֱ��specialists will also have the opportunity to pursue their own research interests as part of their fellowships.

̽��ֱ��programme will also benefit from in-kind support from DeepMind. ̽��ֱ��world-leading British AI company, founded by Queens’ College alumnus Demis Hassabis, has assisted in the development of the programme, and will offer programme participants guest lectures from DeepMind's research team and the opportunity to apply for internship positions.

“Machine learning and AI have the potential to revolutionise any number of fields, but there simply aren’t enough scientists with machine learning skills in those fields at the moment,” said Professor Ann Copestake, Head of the Department of Computer Science and Technology. “This programme will combine Cambridge’s research depth and breadth with the unparalleled expertise in machine learning research we have here in the Department, to build a new type of research culture equipped to face the challenges and opportunities of the 21^st century.”

“We are delighted to support this far-reaching program at Cambridge,” said Stuart Feldman, Chief Scientist at Schmidt Futures. “We expect it to accelerate the use of new techniques across the broad range of research as well as enhance the AI knowledge of a large number of early-stage researchers at this superb university.”

One of the goals of the Accelerate Programme is to build a network of machine learning experts across the ̽��ֱ��. ̽��ֱ��PhD students and postdoctoral researchers who are trained through the Programme will share their knowledge with colleagues, building up capacity throughout Cambridge at scale.

Cambridge’s AI expertise has recently been expanded with the appointment of Dr Ferenc Huszár, who joins the ̽��ֱ�� from Twitter, Dr Carl Henrik Ek, who is joining from the ̽��ֱ�� of Bristol, and Dr Nicholas Lane who is joining from the ̽��ֱ�� of Oxford.

A new initiative at Cambridge will equip young researchers outside computer science with the skills they need to use machine learning and artificial intelligence techniques to power their research.

This programme will help ensure that Cambridge continues to be a beacon for the very best young global researchers, and that we’re giving them the tools they need to thrive

Vice-Chancellor Professor Stephen Toope

Photo by Maxime VALCARCE on Unsplash

Timelapse

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. Images, including our videos, are Copyright © ̽��ֱ�� of Cambridge and licensors/contributors as identified. All rights reserved. We make our image and video content available in a number of ways – as here, on our main website under its Terms and conditions, and on a range of channels including social media that permit your use and sharing of our content under their respective Terms.

Yes

Cambridge appoints first DeepMind Professor of Machine Learning

sc604 — Tue, 17 Sep 2019 23:09:13 +0000

Professor Lawrence joins the ̽��ֱ��’s Department of Computer Science and Technology from Amazon Cambridge, where he has been Director of Machine Learning for the past three years. He is also Professor of Machine Learning at the ̽��ֱ�� of Sheffield, where he will retain a visiting position.

Professor Lawrence’s research interests are in probabilistic models with applications in computational biology, personalised health and developing economies. At Sheffield, he led the ML@SITraN group, and helped to develop an Open Data Science Initiative an approach to data science designed to address societal needs.

“There’s so much expertise at Cambridge, in all aspects of systems and data: that’s why I’m so excited about joining,” Lawrence said. “AI and machine learning have the potential to reshape almost every aspect of our lives, but we desperately need more machine learning specialists, or else the promise of AI will not be realised.”

Professor Lawrence completed his PhD at Cambridge’s Department of Computer Science and Technology in 2000. He has previously held positions at Microsoft Research Cambridge and the ̽��ֱ�� of Manchester. In addition to his academic research, he hosts the Talking Machines podcast and is a contributor to the Guardian.

For the past five years, Professor Lawrence has been working with Data Science Africa, an organisation looking to connect machine learning researchers in Africa in order to solve problems on the ground. Professor Lawrence has an advisory role with the group, and says that many of the machine learning approaches used in Africa can have benefits in the developed world as well.

“With data and machine learning, you can have a more advanced data infrastructure in Africa than in some developed countries,” he said. “It’s rare in the UK or Europe that you’re asked to look at a machine learning problem from end to end, but you can do that in Africa, and it leads to better solutions. That’s the kind of approach I want to take to machine learning in my work at Cambridge.”

Demis Hassabis, co-founder and CEO, DeepMind, said: “I’m delighted to see Cambridge announce its first DeepMind Professor of Machine Learning. Professor Lawrence’s work in computational biology and his thoughtful advocacy for advancing technology in the developing world have been commendable. It’s an honour for DeepMind to be able to support the Department of Computer Science and Technology - from which I gained so much - in this way, and I look forward to seeing machine learning and AI flourish at Cambridge.”

“Neil will have a transformative effect on machine learning and artificial intelligence research at Cambridge,” said Professor Ann Copestake, Head of the Department of Computer Science and Technology. “He will build on our existing strengths in this area, and work with colleagues from across the ̽��ֱ�� to develop new solutions in ethical and sustainable ways.”

“It is vital we have a deep pool of talented scientists in universities and industry so the UK can continue to be a world leader in artificial intelligence,” said Minister for Digital Mark Warman. “This Government is investing millions into skills and talent training, including a number of Turing AI Fellowships in partnership with ̽��ֱ��Alan Turing Institute, and I welcome the appointment of Professor Neil Lawrence as the inaugural DeepMind Professor of Machine Learning at Cambridge. This is one of a range of moves demonstrating the enormous strength of the UK’s research base.”

In addition to the gift to support the DeepMind Professorship, the company are also supporting four Master’s students from underrepresented groups wishing to study machine learning and computer science at Cambridge. ̽��ֱ��first students supported through this programme will be starting their studies this coming term.

Following an international search, Professor Neil Lawrence has been appointed as the inaugural DeepMind Professor of Machine Learning at Cambridge, supported by a benefaction from the world-leading British AI company.

There’s so much expertise at Cambridge, in all aspects of systems and data: that’s why I’m so excited about joining

Neil Lawrence

Yes

Cambridge to appoint DeepMind Chair of Machine Learning

sc604 — Tue, 24 Jul 2018 09:24:16 +0000

̽��ֱ��new chair, which will be based at Cambridge’s Department of Computer Science and Technology, will build on the ̽��ֱ��’s strengths in computer science and engineering, and will be a focal point for the wide range of AI-related research taking place across the ̽��ֱ��. Cambridge researchers 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.

̽��ֱ��first DeepMind Chair is expected to take up their position in October 2019, following an international search by the department. ̽��ֱ��chair will have full academic freedom to pursue research in the field of machine learning.

Cambridge has a long tradition of excellence in computer science, and is home to the largest technology cluster in Europe. ̽��ֱ��DeepMind Chair will build on this tradition by enhancing Cambridge’s capacity in AI-related research, and will contribute to the UK’s standing as a global hub in this rapidly-growing area.

̽��ֱ��gift is part of a wider DeepMind programme to encourage uptake of machine learning, to support the wider academic ecosystem. As part of these efforts, DeepMind will give a donation to support four Master’s students from underrepresented groups wishing to study machine learning and computer science at Cambridge. More information will be made available this coming autumn, for scholarships beginning in the 2019 academic year.

Demis Hassabis, DeepMind’s co-founder and CEO, completed his undergraduate degree in computer science at Queens’ College, Cambridge and received his PhD from UCL, while numerous other employees continue to give back to Cambridge through teaching and mentorship.

“I have many happy memories from my time as an undergraduate at Cambridge, so it’s now a real honour for DeepMind to be able to contribute back to the Department of Computer Science and Technology and support others through their studies,” said Hassabis. “My hope is that the DeepMind Chair in Machine Learning will help extend Cambridge’s already world-leading teaching and research capacities, and support further scientific breakthroughs towards the development of safe and ethical AI.”

“This gift will not only enhance Cambridge’s strengths in the field of AI research, but will benefit the UK more broadly, as AI has such transformative potential in so many aspects of our lives,” said Professor Stephen Toope, Vice-Chancellor of the ̽��ֱ�� of Cambridge. “Our researchers are not only developing these new technologies, but are working to ensure that they benefit humanity. This new Professorship is an important piece of that puzzle.”

“This new Professorship will build on our existing strengths and become an important focus for research and teaching in applied AI,” said Professor Ann Copestake, Head of the Department of Computer Science and Technology. “ ̽��ֱ��interdisciplinary environment in the ̽��ֱ�� will help the development of ethical and sustainable AI-based solutions to complex social, economic and environmental challenges.”

Minister for Digital and the Creative Industries Margot James MP said: “ ̽��ֱ��UK already has a global standing in AI and this new post at Cambridge is another string to our bow. Through our Industrial Strategy and £1bn AI sector deal, we are creating the right environment for the technology to be developed in the UK. I welcome any initiative which will help us achieve our aim of making sure it improves our economy and society.”

̽��ֱ�� ̽��ֱ�� of Cambridge will establish a DeepMind Chair of Machine Learning, thanks to a benefaction from the world-leading British AI company.

This gift will not only enhance Cambridge’s strengths in the field of AI research, but will benefit the UK more broadly, as AI has such transformative potential in so many aspects of our lives.

Stephen Toope, Vice-Chancellor

Yes