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!

1st:	Romy Williamson	the convergence of perception
2nd:	Reuben Adams	Nook
3rd:	Pedro José Ferreira Moreira	UCL Summer School
4th:	Kai Biegun	In With The New
5th:	Roberta Chissich	Fores Escape

1st:	Adrian Gheorghiu & Pedro Moreira
Joint 2nd:	Lorenz Wolf. Mirgahney Mohamed & Jake Cunningham
4th:	Sierra Bonilla
5th:	Bernardo Perrone De Menezes Bulcao Ribeiro & Roberta Chissich

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.

 

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.

In September 2022 I had the amazing opportunity to participate in workshops in Rennes and in Sophia Antipolis, and in a doctoral symposium in Alicante!

In poster sessions and talks, I have presented my work on Aggregated Kernel Tests which covers three of my papers. The first one is MMD Aggregated Two-Sample Test where the two-sample problem is considered, in which one has access to samples from two distributions and is interested in detecting whether those come from the same or from different distributions. The second is KSD Aggregated Goodness-of-fit Test in which we consider the goodness-of-fit problem where one is given some samples and is asked whether these come from a given model (with access to its density or score function). In the third one, Efficient Aggregated Kernel Tests using Incomplete U-statistics, we propose computationally efficient tests for the two-sample, goodness-of-fit, and independence problems; this last one consists in detecting dependence between the two components of paired samples. We tackle these three testing problems using kernel-based statistics, in such a setting the performance of these tests is known to heavily depend on the choice of kernels or kernel parameters (i.e. bandwidth parameter). We propose tests which aggregate over a collection of kernels and retain test power, we theoretically prove optimality of our tests under some regularity assumptions, and empirically show that our aggregated tests outperform other state-of-the-art kernel-based tests.

I started the month of September by participating in GOFCP 2022, the 5th Workshop on Goodness-of-Fit, Change-Point and related problems, from 2nd to 4th September in ENSAI in Rennes (France). It was extremely interesting to hear about the latest research in this very specific research field which covers exactly the topics I had been working on since the start of my PhD.

I then went to EURECOM in Sophia Antipolis (France) for MLF 2022, the ELISE Theory Workshop on Machine Learning Fundamentals, from 5th to 7th September. Talks and poster sessions covered the theory of kernel methods, hypothesis testing, partial differential equations, optimisation, Gaussian processes, explainability and AI safety.

Finally, I participated in EDS 2022, the ELLIS Doctoral Symposium 2022, hosted by the ELLIS Alicante at the University of Alicante in Spain from 19th to 23rd September. It was an amazing experience to meet so many other PhD students working on diverse topics in Machine Learning. I especially enjoyed the numerous poster sessions which allowed to engage with other students and discuss their current research!

I am extremely grateful to Valentin Patilea, Motonobu Kanagawa and Aditya Gulati for the respective invitations, and to my CDT (UCL CDT in Foundational AI with funding from UKRI) which allowed me to participate in those workshops/symposium!