The Cognitive Revolution: Mechanistic Interpretability

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Arthur Conne: Mechanistic interpretability is the reverse engineering of the learned algorithms that neural networks implement into human understandable concepts. The idea here is that neural networks, like machine learning models, are an algorithm which turns inputs into outputs. In mechanistic interpretability, we aim to explain how that happens in terms of the internal components of that model in a human understandable way.

Arthur Conmy sits down with Nathan Labenz for an accessible deep dive into the state of interpretability research online today. They discuss how pioneering researchers have painstakingly worked to isolate the sub-circuits within transformers that are responsible for different aspects of AI capabilities. Arthur also introduces us to a new ACDC approach that he and his co-authors have taken to automating some of the most time-consuming parts of this work. If you’re looking for an ERP platform, check out our sponsor, NetSuite: http://netsuite.com/cognitive

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TIMESTAMPS:

(00:00) Episode Preview

(04:40) What attracted Arthur to mechanistic interpretability?

(07:49) LLM information processing: General Understanding vs Stochastic Parrot Paradigm

(14:00) ACDC paper: https://arxiv.org/abs/2304.14997

(14:45) Sponsors: NetSuite | Omneky

(24:30) Putting together data sets

(32:39) How to intervene in LLMs network activity

(36:00) Defining metrics to evaluate the production of correct completions

(44:20) The future of the mechanistic interpretability research

(50:00) Extracting upstream activations in the ACDC project and evaluating impact on downstream components.

(56:00) Anthropic research findings

(01:08:00) 3-Step process of the ACDC approach

(01:22:00) Setting a threshold and validation

(01:27:00) Goal of the approach

(01:32:00) Compute requirements

Correction: at (01:33:00), Arthur meant to say = "quadratic in nodes"

(01:35:30) Scaling laws for mechanistic interpretability

(01:40:00) Accessibility of this research for casual enthusiasts

(01:46:00) Emergence discourse

(01:56:00) Path to AI safety

LINKS:

https://arthurconmy.github.io/

https://arxiv.org/abs/2304.14997

@labenz (Nathan)

@arthurconmy (Arthur)

@cogrev_podcast

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