The Surface and the Generality of Frontier Capabilities

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There are cases when models are reasoning or performing the correct algorithm to produce certain completions of problems. But after the frontier of capabilities where you suddenly have the next size of model that can do something that previous models couldn't do, such as GPT4 surprising many people with its coding abilities, it's unclear whether this is at that scale when the ability first emerges. The emergence of capabilities and the unpredictability of new things that models can do is quite important for the future risks of systems. If we can't predict what's going to come, then we would at least like to know that there'sHopefully a surface level heuristic maybe, then a completely general solution to something that we thought

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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