Debate: Making Models Emit Interpretable GOFAI Systems Beats Gradient Descent
SturnioloSimone · x · 2026-11-05
A technical exchange on model capability and interpretability. Replying to @davidmanheim, the author argues that having a model directly produce answers is somewhat of a party trick, and that the same result could be achieved more efficiently by generating synthetic training data and then using gradient descent as usual.
The genuinely useful goal, the author says, would be to have the model produce a GOFAI-style, interpretable system. Since there must be structure and logic inside what the model is doing, making that explicit would be a real advantage over yet another neural network we barely understand.
Related event: Researchers debate whether LLMs should generate interpretable GOFAI systems(3 posts)→