How to Zoom in on Emergence in Training Processes

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I've been kicking this question of emergence around from a bunch of different angles as well and trying to just figure out first of all what matters. What really matters for like users society companies is at the end of a training process what can a model do or not do and how general is that capability? That seems like the key thing that like matters most it does seem to be true that you know per that mirage paper that if you find one of these kind of surprises and then rewind and say well okay let me actually measure that performance at every you know increment of the training process then you can plot like a smoother curve. There's this kind of phase change that is often happening between like

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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