AI HUB IN GENERATIVE MODELS
Lead Research Organisation:
UNIVERSITY COLLEGE LONDON
Department Name: Computer Science
Abstract
Generative Models are AI models that can generate data. Recently researchers have shown that by training these models on large amounts of data (text data from the internet and images) these models learn to understand the regularities of our text and image world so well that they can generate responses to questions and create new images with surprising fidelity. This heralds a new era in which computers can assist humans to carry out tasks more efficiently than ever with significant opportunities for society, science and industry. However, these advances need significant research still -- how to make them train efficiently on different problems, how to understand their reliability and adherence to ethical norms.
Organisations
- UNIVERSITY COLLEGE LONDON (Lead Research Organisation)
- IBM (United Kingdom) (Project Partner)
- Creator Fund (Project Partner)
- Wellcome Sanger Institute (Project Partner)
- Unilever UK & Ireland (Project Partner)
- International Council on Environmental E (Project Partner)
- BT plc (Project Partner)
- Synthesia (Project Partner)
- Albion Capital (Project Partner)
- Open Source Imaging Consortium (Project Partner)
- EleutherAI (Project Partner)
- Women in AI (Project Partner)
- Orbital Media & Advertising Ltd (Project Partner)
- Huawei Technologies (UK) Co Ltd (Project Partner)
- PGIM Real Estate (Project Partner)
- G-Research (Project Partner)
- Adobe Systems Incorporated (Project Partner)
- AstraZeneca (Project Partner)
- DeepMind Technologies Limited (Project Partner)
- Mishcon de Reya (Project Partner)
- Conception X Limited (Project Partner)
- GSK (Project Partner)
- Sonos (Project Partner)
- Cohere (Project Partner)
- Dyson Technology Limited (Project Partner)
- LEGO Group (Project Partner)
- Chicago ARC (Project Partner)
- AMPLYFi Ltd (Project Partner)
- Welsh Water (Dwr Cymru) (Project Partner)
- Pindrop (UK) (Project Partner)
- ActiveQuote Ltd (Project Partner)
- Masakhane (Project Partner)
- Evolution Artificial Intelligence Ltd (Project Partner)
- Microsoft Research Asia (Project Partner)
- UiPath (Project Partner)
- 4J Studios Ltd (Project Partner)
- Cisco Systems Inc (Project Partner)
- HumanLoop (Project Partner)
Publications
Biegun K
(2024)
RotRNN: Modelling Long Sequences with Rotations
Chen W
(2024)
Diffusive Gibbs Sampling
Chen, W.
(2024)
Diffusive Gibbs Sampling
in Proceedings of Machine Learning Research
Dolga R
(2024)
Latte: Latent Attention for Linear Time Transformers
Gao L
(2024)
Real-time Large-scale Deformation of Gaussian Splatting
in ACM Transactions on Graphics
Koshiyama A
(2024)
Towards algorithm auditing: managing legal, ethical and technological risks of AI, ML and associated algorithms.
in Royal Society open science
Muldrew W
(2024)
Active Preference Learning for Large Language Models
Muldrew, W.
(2024)
Active Preference Learning for Large Language Models
in Proceedings of Machine Learning Research
| Description | Hub Launch |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Industry/Business |
| Results and Impact | Hub Launch to the public. We had around 200 people from across industry and academia attend our vision for the Hub and how they can get involved. |
| Year(s) Of Engagement Activity | 2025 |
| URL | https://www.genai.ac.uk/news-feed/gen-ai-hub-launches-at-the-crick |