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mentaleap

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A member registered Nov 03, 2022 · View creator page →

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It's pretty cool, I would try implementing something similar using TRACR and also look for circuits in bigger / other models. This might indicate what is crucial for the computation. Maybe this graph is invariant across different models due to its success, or it is actually completely different for some other reasons -> Both results would be super interesting and give evidence to a fundamental  question of MI: 'to what extent can we study toy/small models to learn how large models work?'


I will also add, that often when you reverse software you end up with a computational graph and its structure alone is enough to give insights of the computation. 


That's gold! I (Itay) would love to know if you are planning to do more work on that, and if you are looking for collaboration especially on "The Road to Automation".

Loved the research question!! try have a look on TCAV and our results from the previous hackathon (where we looked for concepts in connect-four RL agent).

In Deepmind's RL lectures there is also a fair comparison between several methods from a statistical inference point-of-view and less of a empirical one. (it is also probably backup up with their hands-on experience).


I would love a clarification on how you see it in connection to the hackathon.

Very cool! can you maybe copy this capability from gpt-2 large to smaller models? 

We have studied properties of soft-tuning or prompt-tuning. We managed to show that task tokens are a convex set! 

We explored how a Deep RL agent uses human interpretable concepts to solve connect-four.

Based on 'Acquisition of Chess Knowledge in AlphaZero' paper by DeepMind and Google Brain, we used TCAV to explore concepts detection in RL agent for connect four.

Our agent architecture was inspired by AlphaZero and trained using the OpenSpiel library by DeepMind.

Our novelty is in the decision to study connect four as it was solved with a knowledge based approach in 1988. Which means that to some extent we understand this game better than chess!

loved this one!

In any case, very cool idea!