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“Formal verification, heuristic explanations and surprise accounting” by paulfchristiano

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Manage episode 425840096 series 3364758
Content provided by LessWrong. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by LessWrong or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
ARC's current research focus can be thought of as trying to combine mechanistic interpretability and formal verification. If we had a deep understanding of what was going on inside a neural network, we would hope to be able to use that understanding to verify that the network was not going to behave dangerously in unforeseen situations. ARC is attempting to perform this kind of verification, but using a mathematical kind of "explanation" instead of one written in natural language.
To help elucidate this connection, ARC has been supporting work on Compact Proofs of Model Performance via Mechanistic Interpretability by Jason Gross, Rajashree Agrawal, Lawrence Chan and others, which we were excited to see released along with this post. While we ultimately think that provable guarantees for large neural networks are unworkable as a long-term goal, we think that this work serves as a useful springboard towards alternatives.
In this [...]
The original text contained 1 footnote which was omitted from this narration.
---
First published:
June 25th, 2024
Source:
https://www.lesswrong.com/posts/SyeQjjBoEC48MvnQC/formal-verification-heuristic-explanations-and-surprise
---
Narrated by TYPE III AUDIO.
  continue reading

307 episodes

Artwork
iconShare
 
Manage episode 425840096 series 3364758
Content provided by LessWrong. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by LessWrong or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
ARC's current research focus can be thought of as trying to combine mechanistic interpretability and formal verification. If we had a deep understanding of what was going on inside a neural network, we would hope to be able to use that understanding to verify that the network was not going to behave dangerously in unforeseen situations. ARC is attempting to perform this kind of verification, but using a mathematical kind of "explanation" instead of one written in natural language.
To help elucidate this connection, ARC has been supporting work on Compact Proofs of Model Performance via Mechanistic Interpretability by Jason Gross, Rajashree Agrawal, Lawrence Chan and others, which we were excited to see released along with this post. While we ultimately think that provable guarantees for large neural networks are unworkable as a long-term goal, we think that this work serves as a useful springboard towards alternatives.
In this [...]
The original text contained 1 footnote which was omitted from this narration.
---
First published:
June 25th, 2024
Source:
https://www.lesswrong.com/posts/SyeQjjBoEC48MvnQC/formal-verification-heuristic-explanations-and-surprise
---
Narrated by TYPE III AUDIO.
  continue reading

307 episodes

All episodes

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