Over the last couple of years, mechanistic interpretability has seen substantial progress. Part of this progress has been enabled by the identification of superposition as a key barrier to understanding neural networks (Elhage et al., 2022) and the identification of sparse autoencoders as a solution to superposition (Sharkey et al., 2022; Cunningham et al., 2023; Bricken et al., 2023).

From our current vantage point, I think there's a relatively clear roadmap toward a world where mechanistic interpretability is useful for safety. This post outlines my views on what progress in mechanistic interpretability looks like and what I think is achievable by the field in the next 2+ years. It represents a rough outline of what I plan to work on in the near future.

My thinking and work is, of course, very heavily inspired by the [...]

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Outline:

(01:33) Key frameworks for understanding the agenda

(01:38) Framework 1: The three steps of mechanistic interpretability

(03:57) Framework 2: The description accuracy vs. description length tradeoff

(07:54) The unreasonable effectiveness of SAEs for mechanistic interpretability

(10:38) Framework 3: Big data-driven science vs. Hypothesis-driven science

(15:14) Sparsify: The Agenda

(17:33) Objective 1: Improving SAEs

(17:57) Benchmarking SAEs

(18:19) Fixing SAE pathologies

(20:46) Applying SAEs to attention

(22:40) Better hyperparameter selection methods

(23:21) Computationally efficient sparse coding

(24:39) Objective 2: Decompiled networks

(27:28) Policy goals for network decompilation

(29:17) Objective 3: Abstraction above raw decompilations

(31:41) Objective 4: Deep Description

(35:23) A sketch of an automated process for deep description: The Iterative-Forward-Backwards procedure

(38:30) Objective 5: Mechanistic interpretability-based evals and other applications of mechanistic interpretability

The original text contained 4 footnotes which were omitted from this narration.

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Narrated by TYPE III AUDIO.