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.

In the exciting realm of Model-based Reinforcement Learning (MBRL), researchers are constantly pushing the boundaries of what robots can learn to achieve when given access to an internal model of the environment. One key challenge in this field is ensuring that robots can perform tasks safely and reliably, especially in situations where they lack prior data or knowledge about the environment. That’s where the work of Sicelukwanda Zwane comes into play.

Background

In MBRL, robots use small sets of data to learn a dynamics model. This model is like a crystal ball that predicts how the system will respond to a given sequence of different actions. With MBRL, we can train policies from simulated trajectories sampled from the dynamics model instead of first generating them by executing each action on the actual system, a process that can take extremely long periods of time on a physical robot and possibly cause wear and tear.

One of the tools often used in MBRL is the Gaussian process (GP) dynamics model. GPs are fully-Bayesian models that not only model the system but also account for the uncertainty in state observations. Additionally, they are flexible and are able to learn without making strong assumptions about the underlying system dynamics [1].

The Challenge of Learning Safely

When we train robots to perform tasks, it’s not enough to just predict what will happen; we need to do it safely. As with most model classes in MBRL, GPs don’t naturally incorporate safety constraints. This means that they may produce unsafe or unfeasible trajectories. This is particularly true during early stages of learning, when the model hasn’t seen much data, it can produce unsafe and seemingly random trajectories.

For a 7 degree of freedom (DOF) manipulator robot, bad trajectories may contain self-collisions.

Distributional Trajectory Sampling

In standard GP dynamics models, the posterior is represented in distributional form – using its parameters, the mean vector and covariance matrix. In this form, it is difficult to reason about

about the safety of entire trajectories. This is because trajectories are generated through iterative random sampling. Furthermore, this kind of trajectory sampling is limited to cases where the intermediate state marginal distributions are Gaussian distributed.

Pathwise Trajectory Sampling

Zwane uses an innovative alternative called “pathwise sampling” [3]. This approach draws samples from GP posteriors using an efficient method called Matheron’s rule. The result is a set of smooth, deterministic trajectories that aren’t confined to Gaussian distributions and are temporally correlated.

Adding Safety

The beauty of pathwise sampling [3] is that it has a particle representation of the GP posterior, where individual trajectories are smooth, differentiable, and deterministic functions. This allows for the isolation of constraint-violating trajectories from safe ones. For safety, rejection sampling is performed on trajectories that violate safety constraints, leaving behind only the safe ones to train the policy. Additionally, soft constraint penalty terms are added to the reward function.

Sim-Real Robot Experiments

To put this approach to the test, Zwane conducted experiments involving a 7-DoF robot arm in a simulated constrained reaching task, where the robot has to avoid colliding with a low ceiling. The method successfully learned a reaching policy that adhered to safety constraints, even when starting from random initial states.

In this constrained manipulation task, the robot is able to reach the goal (shown by the red sphere – bottom row) without colliding with the ceiling (blue – bottom row) using less than 100 seconds of data in simulation.

Summary

Sicelukwanda Zwane’s research makes incremental advances on the safety of simulated trajectories by incorporating safety constraints while keeping the benefits of using fully-Bayesian dynamics models such as GPs. This method promises to take MBRL out of simulated environments and make it more applicable to real-world settings. If you’re interested in this work, we invite you to dive into the full paper, published at the recent IEEE CASE 2023 conference.

References

1. M. P. Deisenroth and C. E. Rasmussen. PILCO: A Model-based and Data-efficient Approach to Policy Search. ICML, 2011.
2. S. Kamthe and M. P. Deisenroth. Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control. AISTATS, 2018.
3. J. T. Wilson, V. Borovitskiy, A. Terenin, P. Mostowsky, and M. P. Deisenroth. Pathwise Conditioning of Gaussian Processes. JMLR, 2021.

We are delighted to announce the successful conclusion of our recent workshop on Distance-based Methods in Machine Learning. Held at the historical Bentham House on 27-28th of June, the event brought together approximately 60 delegates, including leading experts and researchers from statistics and machine learning.

The workshop showcased a diverse range of speakers who shared their knowledge and insights on the theory and methodology behind machine learning approaches utilising kernel-based and Wasserstein distances. Topics covered included parameter estimation, generalised Bayes, hypothesis testing, optimal transport, optimization, and more.

The interactive sessions and engaging discussions created a vibrant learning environment, fostering networking opportunities and collaborations among participants. We extend our gratitude to the organising committee, speakers, and attendees for their valuable contributions to this successful event. Stay tuned for future updates on similar initiatives as we continue to explore the exciting possibilities offered by distance-based methods in machine learning.

Happy attendees at the Distance-based learning workshop

We recently organised an AI hackathon, attended by both the members of our CDT and students from AI-focused CDTs at other universities. The hackathon was the main component of a two-night retreat hosted at Cumberland Lodge, a country house and conference venue in the beautiful Windsor Great Park. The event was student-led, and an exciting opportunity to explore new research directions, brainstorm start-up ideas, and build connections with other PhD students in the field.

During the hackathon we split into small groups, each working on their own projects which had been proposed in advance by the attendees. Lots of ambitious projects were suggested, and it was impressive to see them carried out successfully. These included a web app for language learners that uses speech recognition to judge and correct Mandarin tone pronunciation; an investigation into the capabilities of large language models for solving cryptic crosswords, culminating in a thrilling live demo; and mapping out gaps in the market for waste manipulation robotics start-up. 

Most excitingly, a couple of the teams have decided to continue developing their projects after the event, with new apps and conference papers in the works! 

In addition to the hackathon, the students attending from outside of the CDT in Foundational AI presented their PhD research during a poster session. G-Research also attended the retreat, kindly providing welcome drinks on the first night, and hosting a prize giving for their research competition. There were also ample opportunities for socialising over meals and in the bar, and exploring the sunny surroundings of the park. 

Thank you to the CDT management for helping with organising the event, and all the attendees for making it a success. We hope to arrange something similar next year! 

Authors 

 Oscar Key and Robert Kirk

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.