1. Introduction:

The “Conference on Mathematics of Wave Phenomena 2025” was held at Karlsruhe Institute of Technology (KIT) in Karlsruhe, Baden-W”{u}rttemberg, Germany — about an hour away from Frankfurt by train — between the 24th and 28th of February.
Karlsruhe is an old and wonderful town dating back to the early 18th century, nestled between the Black Forest and the Rhine river, and was built by the old Margrave of Baden-Durlach, Karl-Wilhelm.
In fact, the city’s layout radially extends from the Margrave’s palace with all the streets converging at the palace, and it is where Karlsruhe gets its nickname — the fan city.
KIT is the largest research centre in Germany and was founded in 1815, and has a long list of renowned researchers who have worked there including Fritz Haber and Heinrich Hertz. The conference was chaired by Roland Schnaubel — an expert in hyperbolic and parabolic PDEs and spectral theory — and focused on developments in mathematical modelling, simulation and analysis of wave-type equations and phenomena; key fields in analysis and numerical analysis.

2. My Contribution

I was invited by an ex-UCL colleague to present my unpublished work on Integrating Traditional Numerical Analysis Solvers with Learnable Operators, in a minisymposium titled, “Intersection of Biomedical Acoustics and Machine Learning”, which was chaired by my secondary supervisor Prof. Ben Cox.
Unfortunately, I fell under the weather on the day of my Talk but I was able to present my work and it was well received by the full room I was presenting to, indicating that the focus of my work fit well within the minisymposium.
Despite being the only person at the conference researching the budding field of Neural Operators, there was a good amount of overlap of concepts and techniques with my fellow researchers in the minisymposium.
The talks by Andreas Hauptmann (Fast Fourier models in circular geometry for learned reconstructions in photoacoustic tomography) and Janek Gr”{o}hl (Tackling the photoacoustic inverse problems with semi-supervised deep learning) were of particular note as they focused on leveraging Machine Learning to accelerate or supplement traditional numerical solvers — a key area of research for me — and illuminated a new perspective on how to use ML to complement the strength of traditional techniques.

3. Conference Highlights

Thankfully, I was only under the weather for a day and so I managed to attend and listen to many fascinating talks — even if many of them required more than the 4 years of mathematics I received during my MSci.
Some of the most Interesting talks were focused on Photoacoustic Tomography (PAT) and Reconstruction, which is an area in biomedical imaging where a pulse of light causes tissue to heat up and emit wideband Ultrasound.
PAT (and PAT reconstruction) problems are difficult to model in the forward direction (from light pulse to Ultrasound detection) and even more so in the inverse/reconstruction direction (from received Ultrasound to material properties of the tissue) due to both: the need to model the photoacoustic effect (generating sound from light in a medium), and the Ultrasound transmission and scattering problems.
This is further compounded in the inverse/reconstruction direction due to compounding error from the noisy Ultrasound transducers, causing the reconstructions to be both expensive and error artifact prone.

The notable talks on this topic at Waves 2025 were:

“Self Supervised Sparce-Data Image Reconstruction for Photoacoustics” by Markus Haltmeier -This Talk focused on how to simplify PAT reconstruction by using an new sensor set up to translate a 3D problem into a 2D one, and by using ML models they developed called ‘Sparce2Inverse’ and ‘Noise2Inverse’ to either clean up the noisy data before using a classical reconstruction pipeline or to use traditional denoising techniques and then apply these models to clean up artifacts during reconstruction.

“Fast Fourier models in circular geometry for learned reconstructions in photoacoustic tomography” by Andreas Hauptmann et al. — This Talk focused on the computational difficulties which data-driven end-to-end models face and discussed how they have been building more efficient forward and adjoint models with the aim of bringing the computational cost of PAT reconstruction to be small enough for common medical use.

“Tackling the photoacoustic inverse problems with semi-supervised deep learning” by Janek Gr”{o}hl et al. — This talk focused on using Generative Adversarial Networks (GANs) in order to improve the forward model for PAT problems and to aim to make the results of digital twin experiments (computer modelling of the forward PAT problem in a real world example) look more like real experimental data.

“Physics-Informed deep learning for ultrasonic imaging” by Felix Lucka — This Talk focused on concepts close to my own projects, namely incorporating traditional Algorithms formulated on wave-physics (such as delay-and-sum or f-k migration) as differentiable layers in a ML Architecture frameworks to facilitate in creatin light-weight, data-driven approaches which can be trained end-to-end for a given imaging task while only requiring a small amount of training data.

“Correlation-informed ordered dictionary learning for imaging in scattering media” by Chrysoula Tsogka et al. — This Talk also focused on using traditional numerical analysis techniques with a data-driven component, in this case this was Sparce Dictionary Learning using an encoder-decoder network, to compute super-resolution images in a scattering problem.

There were many other fascinating talks, but these were the highlights when it came to the intersection of numerical analysis and ML techniques.

4. Final Thoughts

My time at Waves 2025 was fantastic and provided me with an excellent opportunity to interact with researchers focusing on related problems to my own and using similar ideas for architecture development, as well as a better understanding of the state of the Operator Learning and Scientific Machine Learning within specialist domains like Numerical Analysis.

My biggest take away from Waves 2025 would be that we as Machine Learning (ML) researchers should engage and interact more with domain experts to solve the complex and technical problems which traditional fields, such as modelling and biomedical imaging, face, as opposed to solving these problems on our own using increasingly larger and data-hungry models.

Or in other words, we need more Machine Learning and less Artificial Intelligence as researchers already have many existing tools which work frighteningly well, we should be focusing on improving these methods as opposed to replacing them wholesale with an end-to-end AI model — I found out that an area which many researchers were interested in was uncertainty quantification for outputs of ML and AI models.

Many of the problems which non-ML researchers and specialists are trying to solve have low amounts of training data (and in medical imaging this data is noisy as well), and thus, need solutions which are robust and can be integrated into existing frameworks without requiring an external server of GPUs to function.

Due to this, I encountered a sizable use of self-supervised or semi-supervised learning within the field in order to alleviate the lack of training data and typically a focus on either older techniques — such as dictionary learning and non-variational auto-encoders.

I think that a lot of productive and fruitful work is possible if we as ML researchers could provide domain expertise regarding techniques within ML or AI which supports the work of existing researchers and collaborate on bringing cutting edge advances in the ML and AI fields into existing fields such as numerical analysis and inverse problems.

Overall I had a great time at KIT and in Karlsruhe, it was a wonderful change of pace compared to London and a beautiful and historic city, and the conference provided me a lot of discussion time with fellow researchers and food for thought about possible research directions in the future.

1 Introduction
The Mathematisches Forschungsinstitut Oberwolfach — or MFO — is a Mathematical Institute located deep in the hills of the Black Forest, far away from the distractions of civilisation. It was built by the Nazis in 1944, in a deliberately secluded location, so as to be an unlikely target for Allied bombing. This is ideal, when you want to peacefully concentrate on maths.
I attended the workshop on Mathematical Imaging and Surface Processing, from 2nd to 7th February 2025. This was organised by Mirela Ben Chen, an inspiring figure in the Geometry Processing community, along with Antonin Chambolle and Benedikt Wirth. Throughout the week, we heard a variety of talks from fields including optimal transport, inverse problems, surface representation, rendering, video generation, fluid simulation and more. We heard from researchers whose work is strongly rooted in classical theory, as well as many researchers who are either using, or investigating the properties of, machine learning techniques such as diffusion models.

Presenting Spherical Neural Surfaces.

2 Contribution
My personal contribution was to present my recently-accepted paper, Neural Geometry Processing via Spherical Neural Surfaces. This paper fitted in very well with the Surface Processing side of the workshop, and I noticed links with several of the other talks, particularly:
• Xavier Dennec (Flag Spaces and Geometric Statistics) — this had relevance to the part of my project where I find eigenfunctions of the Laplace-Beltrami operator.
• Nicholas Sharp (SpaceMesh: A Continuous Representation for Learning Manifold Surface Meshes) — Nick’s discussion of various surface representations led very nicely into my presentation of an alternative neural representation.
Please see the project webpage for more information.

Group photo, in front of Schwarzwald trees

3 Interesting People and Talks
These are the talks that stuck in my mind or inspired me the most. Listening to these talks has helped me to figure out what I like the most in terms of research topics and presentation styles, and to take cues from this to steer my own research direction and presentation style.
• Nicholas Sharp (SpaceMesh: A Continuous Representation for Learning Manifold Surface Meshes): excellent presentation style. He is also very engaging and enthusiastic in conversation. I was excited that he talked about halfedege meshes, and he mentioned properties of orbits of subalgebras of halfedge meshes, which I had figured out myself last summer, not realising it was an established thing. I am motivated and inspired to see someone that is very skilled and knowledgeable at classical geometry processing techniques, combining these thoughtfully (not blindly) with neural networks, to create algorithms that are more robust than previous neural techniques, with improved performance over classical methods.
• Albert Chern (Fluid Dynamics with Sub-Riemannian Geometry): I am amazed at his ability to produce such incredible results with such an aesthetic method (no ugly performance tricks and no magic neural net, etc). I plan to learn some Riemannian Geometry so I can understand more. I played a geometric game, trigon Blockus, with Albert Chern and others.(1)  Albert Chern also likes the Nichomachus Identity (related to the year 2025) and we have both independently tried to find a 4-dimensional
visual proof of that, so far without success.
(1) Why is it triangle, and pentagon, not trigon and pentangle?
• Florine Hartwig (Optimal Motion from Shape Change): I was really excited to see this talk, because Niloy and I had seen the original talk eighteen months ago at the Obergurgl Geometry Processing Workshop in Innsbruck, Austria. The original paper provided an elegant framework to predict the global motion of a deformable body in space, given its shape change. The follow-up paper explored how to optimally deform a shape so that its global motion matches a target motion as closely as possible.
They were able to do the optimisation quite elegantly within the framework. I enjoyed talking to Florine. We had some things in common, such as a background in pure maths, and rowing. Her work also relates to Riemannian geometry and I want to understand more.
• Robert Beinert (A Geometric Optimal Transport Framework for 3D Shape Interpolation): I liked this talk very much because I have spent some time in my research thinking about surface correspondence and shape interpolation, from the perspective of neural surfaces, but I had never considered it from an optimal transport point of view. I really liked the method and I was impressed that it worked at all, but I am not entirely convinced about its framing/applications, because it has no semantic knowledge of
the shapes so it can easily go wrong when the shapes have different-enough proportions. I spent some time talking to Robert and Simon Schwarz during the afternoon excursion, and they explained to me the meaning of Habilitation in Germany.
• Mark Gillespie (harmonic functions rendering). Quite interesting talk. It was a ‘walk on spheres’ type of thing, for rendering implicitly defined surfaces, but the assumption is that the function is harmonic, not necessarily an SDF. This fits into the category of ‘questioning a common assumption in the field’. I’m not totally sure how often this is practically useful but it’s a nice problem set up.
• Nicole Feng (heat method geodesics) This is a cool paper. It constructs geodesic distance robustly, by ‘diffusing’ oriented normals for a short time and solving a Poisson equation. I also noticed she dealt with it quite well when some audience were distracting from the talk a bit by being pedantic about what it even means to have signed distance in some odd cases. She joked about it slightly and moved on, it didn’t put her off that they didn’t like one of the examples.
• Zorah Lahner (nuclear fusion) This was a really good example of a talk that everyone found interesting and engaging even though it didn’t have any actual results. I think that is a difficult kind of talk to give and it leaves the speaker vulnerable to a lot of tricky questions. Maybe people liked it partly because the lack of answers made it a good discussion point.

4 Benefit
This workshop has provided me with great academic benefit. I have been exposed to new topics and I now have a better idea where I need to do further reading to improve my background knowledge in several areas. These areas would include Riemannian geometry, optimal transport, and diffusion. Also importantly, I have worked on soft skills such as presenting and networking.
The MFO was a very calm place to think. I enjoyed looking at the maths books in the library and playing the instruments in the music room. I need to be calm in order to think  clearly and be creative. Therefore I appreciated the effort the MFO has made to make such a conducive environment.

Standing next to the Boy Surface — which is an immersion of the Real Projective Plane into R3. The particular immersion depicted by the sculpture also minimises the Willmore functional, which measures elastic energy.

Conjoined Stellated Icosahedra.

Last month, CDT students travelled to Vancouver to present their work at NeurIPS, one of the largest AI conferences. The schedule was packed, with 6 conference papers and 3 workshop papers presented. The CDT is proud to sponsor and support our PhD students as they present their research at the conference, showcasing their hard work and academic excellence on an international stage!
Day 1, Tuesday: No papers were presented, but the expo, tutorials, and careers fair kept everyone occupied.

Day 2, Wednesday:

William Bankes William and his supervisor presented their work on robust data down sampling. Naive approaches to training on a subset of data can cause problems when classes in the dataset are imbalanced, with rare classes becoming even rarer. A direct result of this is the model’s ability to predict on this subset of the data decreases. To address this they proposed an algorithm called REDUCR, which downsamples data in a manner that preserves the performance of minority classes within the dataset. They show REDUCR works across a range of text and image problems achieving state of the art results. The work is available here.

Jake Cunningham also presented his work on 
Reparameterized Multi-Resolution Convolutions for Long Sequence Modelling

Day 3, Thursday
Yuchen Zhu 

Yuchen’s joint paper with Jialin Yu and Ricardo Silva from UCL Statistical Sciences, Structured Learning of CompositionalSequential Models was presented as part of the main proceedings of NeurIPS 2024. They proposed an explicit model for expressing how the effect of sequential interventions can be isolated into modules, clarifying previously unclear data conditions that allow for the identification of their combined effect at different units and time steps. The paper is here.  Additionally, together with collaborators from MPI Tuebingen, Yuchen presented a paper Unsupervised Causal Abstraction at the Causal Representation Learning workshop. Due to the lack of interpretability of current large blackbox models, they propose a methodology for causally abstracting a large model to a smaller and more interpretable model. In particular, unlike existing methods, their method does not require supervision signals from the smaller model. The paper can be found  here.

David Chanin

David presented a paper along with co-author Daniel Tan, another UCL PhD Student. They find that Contrastive Activation Addition (CAA) steering has mixed results in terms of robustness and reliability. Steering vectors tend to generalise out of distribution when they work in distribution. However, steerability is highly variable across different inputs: depending on the concept, spurious biases can substantially contribute to how effective steering is for each input, presenting a challenge for the widespread use of steering vectors. While CAA is effective on some tasks, other behaviours turn out to be unsteerable. As a result, it is difficult to ensure they will be effective on a given task of interest, limiting their reliability as a general alignment intervention. The paper is available here.

Day 4, Friday
Reuben Adams

Reuben presented his paper (co-authored with his supervisors) on extending a classic theorem in the PAC-Bayes literature to account for arbitrary outcomes, rather than simply correct or incorrect classifications. Their work provides theoretical insight into the generalisation behaviour of neural networks and the different kinds of errors they can make. Their framework can cover not just Type I and Type II errors, but any kind of error that may occur in multi class classification. You can find the paper here.

Daniel Augusto
Daniel presented a co-authored paper in collaboration with
Getúlio Vargas Foundation for the main conference track. Their work proposes a new solution to streaming variational Bayesian inference using  GFlowNets as a foundation for their methodology. This was the first work that allows high quality variational inference for discrete parameters without requiring the storage of the whole dataset. They believe this work will be useful for applications in genetics, through the inference of phylogenetic trees, for preference learning, and other big-data contexts. Their paper can be read here.

Day 5, Saturday 

Oscar Key Oscar, along with co-authors from Graphcore presented a poster at the workshop on Efficient Natural Language and Speech Processing. Their work considers the top-k operation, which finds the largest k items in a list, and investigates how it can be computed as quickly as possible on the parallel hardware commonly used to run AI applications. Top-k is found in many AI algorithms, so it’s useful to make it fast, e.g. a large language model might use it to select the most important parts of a long prompt. Their full paper is available here.

Varsha Ramineni 

Varsha presented her research at the Workshop on Algorithmic Fairness through the Lens of Metrics and Evaluation (AFME 2024). This work addresses the challenge of evaluating classifier fairness when complete datasets, including protected attributes, are inaccessible. They propose a novel approach that leverages separate overlapping datasets, such as internal datasets lacking demographic information  and external sources like census data, to construct synthetic test data with all necessary variables. Experiments demonstrate that our approach produces synthetic data with high fidelity and offers reliable fairness evaluation where real data is limited.  Varsha says that she had an incredible experience attending her first NeurIPS and presenting her work and engaging in meaningful discussions throughout the conference was a deeply rewarding experience, providing invaluable feedback and ideas as the work extends. Do reach out to her if you’d like to learn more!

On June 12th to 21st of 2024, I had the pleasure to attend and present my work as a poster for the 2nd Bayes-Duality Workshop 2024 organized by the Bayes Duality, a Japan-French joint research project team. This workshop was hosted in the Centre for Advanced Intelligence Project (AIP) of RIKEN in Nihonbashi, Chūō City, Tokyo.

Nihonbashi is one of the oldest districts of Tokyo, a lively business district where finance and general office workers gather while neighbouring the imperial palace, where the Japanese monarch and his family lives. Feeling out of place in this somewhat non-academic environment, the two-week workshop contained invited talks, panels between speakers, showcase of works done by the Bayes Duality team, and a poster session.

As stated in the program, the workshop focused on the development of AI that learns adaptively, robustly, and continuously, like humans. A common theme in the presentations by collaborators of the Bayes Duality is to explore the mathematical connections between the training data examples of and the model parameters of these machine learning systems. This connection is incredibly desirable due to the following difference in complexity: the current state-of-art models have a vast number of uninterpretable parameters while the data examples can usually still be understood by human experts.

Due to the length of the workshop, the invited talks could cover an extensive range of topics. Such breadth of topics is hard to describe in such post and, most incredibly, none of them felt out of place in this workshop. Starting from the expected topics as the tutorial on the Bayesian learning rule, one of the papers that put together the connections between data-parameter duality, and convex duality, to more general topics in uncertainty quantification, such as Eugene Ndiaye’s tutorial and presentation on conformal prediction, continual learning, and identifiability of parameters in neural network models.

The poster session included works mentioned in the invited talks and others from students like me. I chose to present my progress on “Interpretable deep Gaussian processes for geospatial tasks”; in this project I analyse the issue of interpretability of three commonly used architectures of deep Gaussian processes and try to understand what practitioners really meant by “interpretable” and suggest a different metric than the commonly used. I felt this was the right work to present to the audience of this workshop due to their familiarity with Bayesian deep learning and interest in understanding the parameters of these models. As the only student from UCL, I was happy to display our work and connect with researchers from institutions all over the world, with attendees from the US, Asia, and Europe.

The AI & Art competition we ran as part of the CDT Showcase event brought together a fantastic array of talent and creativity, with participants impressing us with their outstanding submissions. We were thrilled to see some innovative approaches and unique perspectives reflected in each entry and are excited to highlight the winning entries that stood out among the rest.

1^st Place: Romy Williamson-The convergence of perception
This piece shows a series of stone busts arranged in a figure. The busts blend smoothly between a perfect sphere, Max Planck, and Igea – the Greek Goddess of Health.
In order to blend smoothly between the busts, I converted the meshes into Spherical Neural Surfaces (read my paper or listen to my talk to find out more) and I optimised a smooth neural map between the two domain spheres, minimising the conformal distortion energy using a variant of the First Fundamental Form.
Romy Comment: the convergence of perception (as named by ChatGPT)
I used our novel shape representation (Spherical Neural Surfaces) to represent the heads of Max Planck and the goddess Igea (converted from meshes), and performed a geometric optimization to find a nice correspondence (diffeomorphism), which then allowed me to interpolate to get the in-between heads.
 This is my paper about Spherical Neural Surfaces: https://arxiv.org/abs/2407.07755 . The geometric optimization part is similar to Neural Surface Maps (https://geometry.cs.ucl.ac.uk/projects/2021/neuralmaps/).

2^nd Place:Reuben Adams – Nook 
The colours in this photo have been subtly changed to encode an audio file of a crackling fireplace, which in turn has been imperceptibly altered to encode a text file of Hardy’s poem The Darkling Thrush. The work telescopes into one image a dreary and wet walk through the peak district, warming by the fire, and thoughts of an old friend.

3^rd Place: Pedro José Ferreira Moreira-UCL Summer School
Welcome to ‘UCL Summer School,’ an exciting comic book adventure that follows a young student on their thrilling journey at University College London Summer School!

Imagine being able to create a whole comic book without knowing how to draw – thanks to AI, that’s exactly what happened here! From packing bags and boarding a plane to sightseeing around London and attending cool AI seminars, this comic capturers every moment with vibrant, dynamic art.
What AI Can Do: AI makes it possible to turn your wildest ideas into reality, even if you can’t draw a stick figure. It helps craft detailed and expressive comic panels that perfectly match the story in your head. Plus, AI is like a super-fast sidekick, helping to create everything in no time!
The Not-So-Great Parts: Sometimes, AI might miss the mark on capturing those deep, personal emotions or might not get the scene just right without some help. It’s great, but it’s not a mind reader – yet!
The Future Is Bright: Imagine a world where AI tools are even more creative, intuitive, and just plain fun to use. We’re talking about easier ways to blend human creativity with AI’s power, making art that’s truly one-of-a-kind.

In ‘UCL Summer School’ you’ll see how AI can turn anyone into a comic book creator, expressing thoughts and stories in a vibrant way that’s never been easier. This comic is all about having fu, exploring new tech and realizing that with a little help from AI, the sky’s the limit for your creativity!
Pedro’s Comments. The motivation behind this comic book art is simple: to show that creativity shouldn’t be limited by technical skills. With the help of AI, anyone can turn their ideas into reality, no matter their experience. Even if you’re “not good at drawing,” you can bring your imagination to life. Sure, the technology isn’t perfect (extra fingers popping up in the art can be a funny surprise), but it’s more than enough to convey emotion and tell captivating stories

4^th Place: Kai Biegun-In With The New
This piece aims to convey a juxtaposition of retro analogue photography and state of the art AI image generation. Four film photos were taken on various film stocks with vintage analogue cameras, and descriptions of those images were used to generate four corresponding photos with the Adobe Firefly image generation suite. I have always felt that the grainy, textured look of film photographs gives them a certain quality that makes looking at them feel like you’re looking at a snapshot from a memory. This is in stark contrast to the saturated, ultra-smooth, somewhat cartoonish look of AI generated photos. I believe this speaks to the fact that, although we are moving towards a world where digital and AI generated media are the norm, there is still place for the analogue to provide a window into real moments, memories, and experiences.
Kai’s comments. The piece is a study of the differences between images captured with analogue cameras and images generated by AI, whereby the analogue photographs were recreated by generative AI by prompting it with a text description of each image. It aims to highlight not just the superficial differences in colour, texture, and subject, but also the difference in feeling one gets from knowing how each image was captured, and question whether that in itself contributes to the artistic merit of the images.

5^th Place: Roberta Chissich-Forest Escape.
Materials Used: Blender 4.1, ANT Landscape Addon, Node Wrangler Addon, Cycles Render Engine, Sapling Tree Gen Addon, Poly Haven Textures.

The Interactive Forest Environment is a meticulously crafted 3D scene designed to immerse viewers in a realist natural landscape. This piece leverages advanced procedural techniques and tools within Blender, reflecting the growing intersection of AI and art in the digital age.

Blender’s geometry nodes and procedural generation tools were extensively used to create the ground and vegetation layouts. These nodes enable the creation of complex, natural-looking terrains and distributions with minimal manual intervention. This results in highly detailed and varied environments without the need for manual modelling of each element. The use of procedural shaders and texture blending techniques in Blender mimics AL-assisted methods to combine ground textures from Poly Haven seamlessly, ensuring enhanced detail and natural transitions.

To optimize rendering, the Cycles Render Engine utilizes NVIDIA’s AI-accelerated denoising technology. OptiX reduces noise in rendered images, significantly speeding up the rendering process while maintaining high-quality visuals. This integration of AI technology helps in producing clean, detailed renders with fewer samples, making the workflow more efficient.

This artwork is inspired by the calming and restorative qualities of nature. It aims to transport viewers to a serene forest environment, providing a momentary escape from the hustle and bustle of everyday life, capturing the essence of nature’s tranquility.
Roberta’s comments. This animated river scene, created in Blender, showcases the power of combining human creativity with advanced tools. By using OptiX rendering, the video achieves a higher level of visual fidelity, capturing the intricate details of light and water. The use of procedural scattering has simplified the placement of grass, leaves, and trees, making the natural landscape come to life effortlessly.
My motivation for this piece comes from the belief that art and technology are not in opposition, but are powerful allies; AI-enhanced tools can aid artists in their creative process. This artwork embodies the idea that we can use these innovations to elevate our creative expression. It’s not about replacing human artistry, it’s about how these tools can help us amplify our imagination, making the impossible possible, and turning complex visions into reality. Together, we can craft a future where human spirit and technological prowess unite to create beauty.

Thank you to everyone who participated. Each entry brought something special to the event and helped create a vibrant and memorable experience for all involved!

It is hard to believe that we are already reaching the end of another term at UCL. Since October we have welcomed 13 new students had PhD submissions, congratulated new Drs and seen a significant number of students have their work accepted for some of the most prestigious conferences in the field of artificial intelligence and machine learning!

Before the CDT breaks up for the year, we felt it was only fitting to have our last student catch up session be one that taxed their minds and put their cohort collaboration skills to the test via an Escape Room journey!

We had 18 students divided into 4 teams to try and solve puzzles galore to escape in good time to eat mince pies and share their recent work and future plans.

We wish all students, staff and families happy holidays and the best for a successful 2024.

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.