Summary of The Cognitive Revolution in Interpretability: From Explaining Behavior to Interpreting Representations and Algorithms, by Adam Davies et al.
The Cognitive Revolution in Interpretability: From Explaining Behavior to Interpreting Representations and Algorithms
by Adam Davies, Ashkan Khakzar
First submitted to arxiv on: 11 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper aims to bridge the gap between artificial neural networks and cognitive science by grounding mechanistic interpretability (MI) in the context of studying “black box” intelligent systems. The authors argue that current MI methods are ripe for a transition, echoing the “cognitive revolution” in 20th-century psychology. They propose a taxonomy mirroring key parallels in computational neuroscience to describe two broad categories of MI research: semantic interpretation and algorithmic interpretation. The authors elaborate on the parallels and distinctions between various approaches, analyzing their strengths and weaknesses, assumptions, challenges, and potential for unifying them under a common framework. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how artificial neural networks work inside. It connects this to how we study human brains, which are also “black boxes” in some ways. The researchers say that the current methods for understanding these AI models are ready for a big change, like what happened in psychology when it switched from just looking at behavior to studying what’s going on inside people’s minds. They create a way to categorize different approaches to understanding AI models, and they compare them to how we study human brains. This helps us see the strengths and weaknesses of each approach and figure out if they can be connected in some way. |
Keywords
» Artificial intelligence » Grounding
