We (EffiSciences) ran a hackathon at the end of the Turing Seminar in ENS Paris-Saclay and ENS Ulm, an academic course inspired by the AGISF, with 28 projects submitted by 44 participants between the 11th and 12th November.

We share a selection of projects. See them all here.

I think some of them could even be turned into valuable blog posts, and I’ve learnt a lot by reading everything. Here are a few extracts.

Towards Monosemanticity: Decomposing Vision Models with Dictionary Learning

David HEURTEL-DEPEIGES [link]

Basically an adaptation of the famous dictionary learning paper on vision CNN.

“When looking at 100 random features, 46 were found to be interpretable and monosemantic, [...] When doing the same experiment with 100 random neurons, 2 were found to be interpretable and monosemantic”. Very good execution.

Open, closed, and everything in between: towards human-centric regulations for safer large models

Théo SAULUS [link]

Imho, an almost SOTA summary of the current discourse on open sourcing models.

A Review of The Debate on Open-Sourcing Powerful AI Models

Tu Duyen NGUYEN, Adrien RAMANANA RAHARY [link]

“What is the goal of this document? Our goal is to clarify, and sometimes criticize, the arguments regularly put forward in this, both from the proponents and the opponents of open-sourced AI models. In an effort to better understand both stances, we have classified the most common arguments surrounding this debate in five main topics:

Safety evaluation of powerful AI models through audits or open-research
Competition in the private sector and beyond
Safety risks of open-sourcing strongly capable models
Preserving open science for its own sake
The feasibility of closing the weights of AI models

In an effort to highlight the strengths and weaknesses of each stance, we will present and challenge for each family the arguments of both sides, as well as include in each family some arguments which we think are relevant, but have not been mentioned in most discussions on these topics.”

They tried to be exhaustive, but there are still some gaps. The format is nevertheless interesting (even if it could be even more concise).

AI safety media analysis

Gurvan Richardeau, Raphaël Pesah [link]

“The first analysis we’ve done involved examining major themes by employing text data analysis and ML methods (such as tf-idf analysis and topic modeling) within a corpus o...

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