Public repo for The Alan Turing Institute's reading group on fundamental AI research.
If you're based at the Turing, follow #robots-in-disguise on the Turing Slack for the most recent updates.
To see all the slides and reading materials for previous sessions, see the archive.
Note that this originated from the Research Engineering Team's reading group on Transformers.
The group meets every week on Mondays at 11-12. Everyone is welcome to join! If you have any questions email Ryan Chan, Fede Nanni or Giulia Occhini and remember to go through our Code of Conduct before joining.
Please get in touch if you would like to give a talk (either about your research or a topic you think is relevant to the reading group) add suggestions and emoji preferences to the list of proposed topics on HackMD!
| Date | Topic | Room | Lead |
|---|---|---|---|
| 18/11/24 | Biological neural networks | David Blackwell | Balázs Mészáros , Jess Yu |
| 25/11/24 | Application of foundation models in time series tasks | David Blackwell | Gholamali Aminian |
| 02/12/24 | Can language models play the Wikipedia game? | David Blackwell | Alex Hickey, Jo Knight |
| 03/12/24 | Mechanistic Interpretability | David Blackwell | Neel Nanda |
| 09/12/24 | Scaling laws of neural networks | David Blackwell | Edmund Dable-Heath |
| 16/12/24 | TBC | David Blackwell | TBC |
Two talks:
- Event-Based Learning of Synaptic Delays in Spiking Neural Networks
- Information-theoretic Analysis of Brain Dynamics & Neural Network Models Informed by Information Theory
This project examines how Language Models can navigate Wikipedia. Which tests their ability to link semantically similar topics in a practical way. We have run experiments with a large variety of sentence embedding and large language models for comparison. We have also seen how the performance varies when transversing Wikipedia in other languages and when navigating between scientific papers, which allows an assessment of the breadth of the model's abilities.