Mechanistic interpretability aims to reverse engineer neural networks in a way that is analogous to reverse engineering a compiled binary computer program. Achieving this level of interpretability for a neural network like GPT-2 would involve creating a binary computer program that is interpretable by expert human programmers and can emulate the input-output behavior of GPT-2 with high accuracy.

Before January 1st, 2030, will mechanistic interpretability be essentially solved for GPT-2, resulting in a binary computer program that is interpretable by ordinary expert human programmers and emulates GPT-2's input-output behavior up to a high level of accuracy?

This question will resolve positively if, before January 1st, 2030, a binary computer program is developed that meets the following criteria:

This question will resolve negatively if, before January 1st, 2030, no binary computer program meeting the interpretability and accuracy criteria is developed and verified according to the above requirements. If there is ambiguity or debate about whether a particular program meets the resolution criteria, I will use my discretion to determine the appropriate resolution.