On Wednesday 29th November, UCL Events hosted Demis Hassabis to give the UCL Prize Lecture 2023 on his work at Google DeepMind, a company that he founded after completing his PhD at UCL.

Demis’ talk covered his journey through academia and interest in machine learning and artificial intelligence, which all started with a childhood love of games. Having started playing chess as young as 3, it is little wonder that this incredibly insightful and intelligent individual went on to work with algorithms and formats that were fun, functional and ground breaking. Demis’ interest in neuroscience and computational analysis was the perfect groundwork from which to create machine learning tools that now lead the world in their outcomes and developments. From AlphaGo to protein folding and beyond, Demis is a pioneer and revolutionary wrapped up in an extremely humble and engaging human being.

Our CDT is privileged to have a number of students funded by Google DeepMind, and these scholars were invited to attend a VIP meet and greet with Demis and his colleagues before the lecture began.

Our students were able to share their accomplishments and research with a number of academics, Google DeepMind executives and other invitees and were delighted to have been included in such a prestigious event.

There were over 900 people in personal attendance for the talk, with over 400 additional attendees online. With special thanks to the UCL OVPA team and UCL Events for making this happen and sharing the opportunity with our scholars.

It is undeniable at this point that AI is going to radically shape our future. After decades of effort, the field has finally developed techniques that can be used to create systems robust enough to survive the rough and tumble of the real world. As academics we are often driven by curiosity, yet rather quickly the curiosities we are studying and creating have the potential for tremendous real-world impact.

It is becoming ever more important to keep an eye on the consequences of our research, and to try to anticipate potential risks.

This has been the purpose of our AI discussion series that I have organised for the members of the AI Centre, especially for those on our Foundational AI CDT.

I kicked off the discussion series with a talk outlining the ongoing debate over whether there is an existential risk from AI “going rogue,” as Yoshua Bengio has put it. By this I mean a risk of humanity as a whole losing control over powerful AI systems. While this sounds like science fiction at first blush, it is fair to say that this debate is far from settled in the AI research community. There are very strong feelings on both sides, and if we are to cooperate as a community in mitigating risks from AI, it is urgent that we form a consensus on what these risks are. By presenting the arguments from both sides in a neutral way, I hope I have done a small amount to help those on both ends of the spectrum understand each other. You can watch my talk here: https://www.youtube.com/watch?v=PI9OXHPyN8M

Ivan Vegner, PhD student in NLP at the University of Edinburgh, was kind enough to travel down for our second talk, on properties of agents in general, both biological and artificial. He argued that sufficiently agentic AI systems, if created, would pose serious risks to humanity, because they may pursue sub-goals such as seeking power and influence or increasing their resistance to being switched off—after all, almost any goal is easier to pursue if you have power and cannot be switched off! Stuart Russell pithily puts this as “You can’t fetch the coffee if you’re dead.” Ivan is an incredibly lucid speaker. You can see his talk Human-like in Every way? here https://www.youtube.com/watch?v=LGeOMA25Xvc

For some, a crux in this existential risk question might be whether AI systems will think like us, or in some alien way. Perhaps we can more easily keep AI systems under control if we can create them in our own image? Or could this backfire—could we end up with systems that have the understanding to deceive or manipulate? Professors Chris Watkins and Nello Christianini dug into this question for us by debating the motion “We can expect machines to eventually think in a human-like way.” (Chris for, Nello against). There were many, many questions afterwards, and Chris and Nello very kindly stayed around to continue the conversation. Watch the debate here: https://www.youtube.com/watch?v=zWCUHmIdWhE

Separate from all of this is the question of misuse. Many technologies are dual-use, but their downsides can be successfully limited through regulation. With AI it is different: the scale can be enormous and rapidly increased (often the bottleneck is simply buying/renting more GPUs), there is a culture of immediately open-sourcing software so that anyone can use it, and AI models often require very little expertise to run or adapt to new use-cases. Professor Mirco Musolesi outlined a number of risks he perceives from using AI systems to autonomously make decisions in economics, geo-politics, and warfare. His talk was incredibly thought-provoking: You can see his talk here: https://youtu.be/QH9eYPglgt8

This series has helped foster an ongoing conversation in the AI Centre on the risks of AI and how we can potentially steer around them. Suffice to say it is a minefield.

We should certainly not forget the incredible potential of AI to have a positive impact on society, from automated and personalised medicine, to the acceleration of scientific and technological advancements aimed at mitigating climate change. But there is no shortage of perceived risks, and currently a disconcerting lack of technical and political strategies to deal with them. Many of us at the AI Centre are deeply worried about where we are going. Many of us are optimists. We need to keep talking and increase our common ground.

We’re racing into the future. Let’s hope we get what AI has been promising society for decades. Let’s try and steer ourselves along the way.

Reuben Adams is a final year PhD student in the UKRI CDT in Foundational AI.

Zak Morgan is a second year PhD candidate

During an EU Research project meeting, I was invited by one of the industry partners (UltraLeap) to give a talk at their company regarding my PhD project so far. Since it is my first talk aimed at industry specifically I was unsure on what to expect and so here I’ll lay out some of my reflections on the process with the aim of helping those who find themselves in my shoes in the future.

Like in my previous blog post, I’d emphasize that the content you prepare, whether it be a slide deck, poster or other materials, is only the spring board for discussion. Whilst my slide-deck for the formal presentation was 10 slides, I ended up showing many other results and work to aid answering questions in the Q&A afterwards. I’d highly recommend having a collection of supplementary material, like in an academic paper, to aid in answering any questions that might arise from the discussion.

Overall, I found the talk incredibly productive, and I’d like to think both me and the employees found the talk helpful. In particular I can share findings that I am able to carry out due to the nature of my work not being profit-focused, whilst they can provide engineering details and polish on thoughts, that are simply not valued in a research context.

I’d also advise you talk to your supervisors beforehand and the company to make sure you know what they want you to talk about, and what you can talk about. Sometimes this may require signing an NDA if you want to see particularly cool stuff that goes on in industry and not doing so will result in you missing out on a lot of the cool inner-workings at these industrial partners!

On a final note about preparation, if you find yourself in this position, although the talk may only have half an hour, or an hour scheduled, make sure you’re free for a much longer period. A talk over lunch before or with certain people afterwards can be just as productive as the meeting itself!

A massive thanks to UltraLeap for the opportunity, and I’d highly recommend other PhD students to make sure they’re utilising the great connections you can make during your research.

This work was supported by the Royal Academy of Engineering Chairs in Emerging Technology Scheme (CiET1718/14)

On 7^th and 8^th November 2022 three of the UKRI CDTs in Artificial Intelligence hosted an Inter-CDT conference for our students and industry partners at The Bristol Hotel. The UKRI CDT in Foundational AI worked alongside our sister CDTs at the University of Bath (ART-AI) and University of Bristol (Interactive AI), to produce a two day event that covered AI from deep tech entrepreneurship to AI Ethics and Defence.

Turnout from all three CDTs was excellent and it was a wonderful opportunity for students across the three institutions to meet and collaborate with one another, sharing their knowledge and research of AI both in theory and applied.

UCL were delighted to host two panel sessions; the first being on Deep Tech entrepreneurship with Dr. Riam Kanso from Conception X, Dr. Stacy-Ann Sinclair from CodeREG and Dr. Thomas Stone from Kintsugi (ad)Ventures. Hosted by our CDT Director, Prof David Barber, this interactive panel session saw our specialists discuss the pathways into start ups and entrepreneurships, the perils, pitfalls and positives that follow! It was wonderful to be able to hear from industry experts their personal journeys to successful business ventures and great to have such an engaged and enquiring audience, who were keen to ask numerous questions and gain further insight to future possibilities.

Our second panel closed the event and was a student-led initiative discussing large scale datasets and massive computational modelling in AI.

For a more detailed review of the event we highly recommend you read the review by ART-AI on their website.

We were delighted to celebrate our student Dennis Hadjivelichkov’s second place in the poster session that took place at the MShed in Bristol as well as enjoy the fine food and fabulous company of our CDT peers.

With thanks to ART-AI and Interactive AI CDTs for their co-hosting and co-organising skills. It was a delight to be able to share time and work with our sister CDTs and we hope to collaborate again in the not too distant future.

Tuesday 1^st November was a busy day at the CDT and UCL Centre for Artificial Intelligence with our joint UKRI CDT poster showcase and AI demo event. Together with the UKRI CDT in AI-Enabled Healthcare we put on an event featuring posters, demos, AI art and robots.

Prof David Barber presenting the latest news on the CDT

The afternoon began with presentations by the CDT centre directors Prof David Barber and Prof Paul Taylor, as well as our industry sponsor Ulrich Paquet from Deepmind. In attendance were students, academics and industry partners, keen to understand what we have been doing and where our research will take us in the future.

PhD Candidate Jakob Zeitler provides a demo on screen

We had approximately 40 posters on display, with a further 19 demonstrations of AI by a variety of groups from Vision to Natural Language Processing. Engagement with the poster presenters was high across the board and a wonderful opportunity for our students to engage with others about the work that they have undertaken the last few years.

PhD candidate Reuben Adams presents his poster to a crowd of attendees

We were honoured to have the Provost in attendance to witness just how vibrant and stimulating our centres are as part of a dynamic and successful Computer Science department.

Provost Dr Michael Spence unveils the Amedeo Modigliani painting

The UCL Centre of Artificial Intelligence have been donated a rare 3D generated AI generated painting of a Amedeo Modigliani, which started as a Masters and then PhD project for Dr. Anthony Bouchard and Dr. George Cann and will be displayed at the AI Centre for all to see.

The day ended with a robot display in the Function Space, showcasing the quadrapod robots that our students are working with both at the AI Centre and the soon to be opened UCL East.

Two quadrapod robots being demonstrated to the crowd

It was wonderful to witness all the different ways in which AI is being applied and developed to help solve some of societies greatest needs and to have the opportunity to share the work of our students with a wider audience.

With thanks to those who attended, our students, director David Barber, AI Centre manager Sarah Bentley and the TSG team for their time, patience and support in helping to make this a hugely successful event.

« COLT has been the prime annual meeting of the growing learning theory community for 35 years now, and that London edition has been beyond our expectations. We have been planning COLT 2022 since late 2019, and due to Covid it was unclear until a few weeks before the conference how many people would be able or willing to join. Our optimistic scenario was 150 on site attendees — we ended up at more than 270! COLT 2022 featured the higher number ever of papers (155) in a dual track format. I am especially proud that over 50% of attendees were MSc, PhD and postdocs: COLT has long been a welcoming and inclusive forum for early-career researchers. As local chair, COLT has eaten up a lot of my days and nights recently, but it certainly was worth it! » Benjamin Guedj, Inria and University College London, COLT 2022 Local Chair.

This July, I’ve had the great pleasure of participating in the Conference on Learning Theory COLT 2022 which has been held in person in London! I found the conference to be a real success, it was wonderful to finally be able to meet so many people sharing the same interests in learning theory! It was amazing to follow talks held in the historic Royal Institution of Great Britain which is the location of the famous televised Christmas Lectures!

The conference kicked off with a joint workshop between COLT and IMS (Institute of Mathematical Statistics) Annual Meeting with tutorials and talks by Emmanuel Candès, Nati Srebro and Vladimir Vovk on the topics of conformal prediction and mathematics of deep learning. This workshop allowed to bring together both audience (IMS and COLT) with aligned interests on statistics and learning theory. This was a great initiative which was really appreciated by all the participants I talked to, I hope the joint workshop between IMS and COLT will remain in future editions of the conferences!

During the four following days, all papers accepted to COLT 2022 have been presented by the authors. Each talk was ten minutes long, this format allowed to get a good overview of each of the 155 papers. Topics included Online Learning, Statistics, Privacy, Robustness, Computational Complexity, Deep Learning, Generalization, Bandits, Sampling, Optimization, Graphs, Information Theory, Reinforcement Learning and Control. It was also very interesting to listen to longer talks such as those of the two papers which received the best paper and best student paper awards of COLT 2022 (Efficient Convex Optimization Requires Superlinear Memory by Annie Marsden, Vatsal Sharan, Aaron Sidford, and Gregory Valiant, and New Projection-Free Algorithms for Online Convex Optimization with Adaptive Regret Guarantees by Ben Kretzu and Dan Garber), as well as those given by plenary speakers: Jelani Nelson from Berkeley, University of California, Maryam Fazel from University of Washington, and Alon Orlitsky from University of California San Diego.

I also really enjoyed the open problem sessions in which unsolved problems were presented in the hope that these can be solved in future editions of COLT, it was great to see which learning theory problems people currently find challenging! Other events were also organised such as the LeT-All career panel providing advice to early researchers, the Women in Machine Learning Theory luncheon discussing everyday challenges women are facing in academic and industrial Machine Learning research, the business meeting with COLT announcements about future editions of the conference, the workshop reception and the conference gala dinner which were the perfect opportunity to engage with other participants!

COLT 2022 was made possible thanks to the hard-working organizing committee: program chair Po-Ling Loh from University of Cambridge, program chair Maxim Raginsky from University of Illinois at Urbana-Champaign, local chair Benjamin Guedj from Inria and University College London, local chair Ciara Pike-Burke from Imperial College London, open problems chair Clément Canonne from University of Sydney, online experience chair Claire Vernade from DeepMind, and publication chair Suriya Gunasekar from Microsoft Research. Thank you all for making COLT 2022 possible and such a success!

I am now looking forward to COLT 2023!

I have been delighted to be in charge of organising the DeepMind/ELLIS CSML Seminar Series 2021/2022 for the second year in a row. The aim of this seminar series is to foster collaboration across different UCL departments of the UCL ELLIS Unit (previously Computational Statistics and Machine Learning, CSML) which include the Gatsby Computational Neuroscience Unit, the Centre for Artificial Intelligence, the Department of Computer Science, the Department of Statistical Science, and the Department of Electronic and Electrical Engineering. Talks topics cover some of the latest research in Machine Learning and Statistics. All information about the seminar series can be found online, recordings are available on YouTube, talks are advertised on Twitter, on a mailing list and on a calendar.

Due to the sanitary situation, we have held our seminars online for the first half of the academic year, this allowed to host international speakers from across the globe. For the second half of the academic year, we were able to resume in-person seminars and to host speakers at UCL. We are immensely grateful to DeepMind for sponsoring our seminar series, allowing us to also host speakers from outside of London. I’d also like to thank all the speakers for presenting their latest work at our seminar series during this 2021/2022 academic year, all talks are presented below. Finally, I’d like to thank Jean Kaddour, Oscar Key, Pierre Glaser and Azhir Mahmood for their help in hosting the seminars, and I am very excited to welcome Kai Teh from the UCL Department of Statistical Science and Mathieu Alain from the UCL Centre for Artificial Intelligence who will join me in co-organising the seminar series for the 2022/2023 academic year!

• Online Multitask Learning with Long-Term Memory
  • Mark Herbster
  • University College London
  • CSML
  • YouTube

• Contrastive Self-Supervised Learning and Potential Limitations
  • Ting Chen
  • Google Brain
  • CSML
  • YouTube

• Selective Inference with Kernels
  • Makoto Yamada
  • High-dimensional Statistical Modeling Team, RIKEN AIP
  • CSML
  • YouTube

• Optimal Subgroup Selection
  • Richard Samworth
  • Statistical Laboratory, University of Cambridge
  • CSML
  • YouTube

• Data Augmentation in High Dimensional Low Sample Size Setting using a Geometry-Based Variational Autoencoder
  • Stéphanie Allassonnière
  • School of medicine, University of Paris
  • CSML
  • YouTube

• Sharpness-Aware Minimization (SAM) Current Method and Future Directions
  • Hossein Mobahi
  • Google
  • CSML
  • YouTube

• The Bayesian Learning Rule for Adaptive AI
  • Emtiyaz Khan
  • Approximate Bayesian Inference Team, RIKEN AIP
  • CSML
  • YouTube

• Machine Learning without Human Supervision on Neuroscience Signals
  • Alexandre Gramfort
  • Inria MIND Team
  • CSML
  • YouTube

• Utilitarian Information Theory
  • Stefano Ermon
  • Stanford University
  • CSML
  • YouTube

• Lucas-Kanade Reloaded End-to-End Super-Resolution from Raw Image Bursts
  • Julien Mairal
  • Inria Grenoble, Thoth team
  • CSML
  • YouTube

• The Blessing and the Curse of the Multiplicative Updates – Discusses Connections between in Evolution and the Multiplicative Updates of Online Learning
  • Manfred K. Warmuth
  • Google Research NY
  • formerly University of California at Santa Cruz
  • CSML
  • YouTube

• Explaining Kernel Methods With RKHS-SHAP
  • Siu Lun (Alan) Chau
  • Oxford University
  • CSML
  • YouTube

• Non-Euclidean Matérn Gaussian Processes
  • Alexander Terenin
  • Cambridge University
  • CSML
  • YouTube

• Robust Bayesian Inference for Simulator-Based Models via the MMD Posterior Bootstrap
  • Harita Dellaporta
  • University of Warwick
  • CSML
  • YouTube

• Efficient MCMC Sampling with Dimension-Free Convergence Rate using ADMM-type Splitting
  • Daniel Paulin
  • University of Edinburgh
  • CSML

• Robust Estimation Via Maximum Mean Discrepancy
  • Badr-Eddine Chérief-Abdellatif
  • CNRS, LPSM
  • CSML
  • YouTube

• Equivariant and Coordinate Independent Convolutional Networks
  • Maurice Weiler
  • University of Amsterdam
  • CSML
  • YouTube

• Inference in High-Dimensional Logistic Regression Models with Separated Data
  • Rebecca Lewis
  • Imperial College London
  • CSML
  • YouTube

• Causal Foundations for Safe AI
  • Tom Everitt
  • DeepMind
  • CSML
  • YouTube

The start of this year has been incredibly fruitful for some of our students who have won awards and had papers accepted for conferences. We are incredibly proud of them all and happy to provide a humble brag of their achievements.

Jakob Zeitler wins UCL Grand Challenges Doctoral Students’ small grant

Jakob Zeitler (Cohort 1) was awarded a UCL Grand Challenges Doctoral Students’ small grant award for his project ‘Developing a New Approach to Teaching AI and Society’ alongside of Jaspreet Jagdev. Jakob has been very involved in looking at AI and ethics since the start of his research at UCL and has been integral in setting up speakers and reading groups around the subject of AI and Society.

Student Research Papers Accepted for conferences and publications

The following students have had papers accepted at the Tenth International Conference on Learning Representations (ICLR 22) (authors in bold from L to R)

Fickinger, A., Cohen, S., Russel, S., Amos, B. (2022). Cross-Domain Imitation Learning via Optimal Transport. Proc. of the International Conference on Learning Representations (ICLR)

McEwen, Jason D., Christopher GR Wallis, and Augustine N. Mavor-Parker. “Scattering Networks on the Sphere for Scalable and Rotationally Equivariant Spherical CNNs.”, ICLR 2022

Mguni, D.H., Jafferjee, T., Wang, J., Perez-Nieves, N., Slumbers, O., Tong, F., Li, Y., Zhu, J., Yang, Y. and Wang, J., 2021. LIGS: Learnable Intrinsic-Reward Generation Selection for Multi-Agent Learning. ICLR 2022.

Yu, Changmin, Dong Li, Jianye Hao, Jun Wang, and Neil Burgess. “Learning State Representations via Retracing in Reinforcement Learning.” ICLR 2022

Felix Biggs just had an AISTATS 2022

Felix Biggs and Benjamin Guedj, On Margins and Derandomisation in PAC-Bayes, AISTATS 2022. Preprint version is here https://arxiv.org/abs/2107.03955 and official announcement here http://aistats.org/aistats2022/accepted.html