Complementary remarks from Gary Marcus and Terence Tao on OpenAI's giant math drop - Marcus on AI | Substack

Summary

OpenAI’s massive new math drop: [This essay was written in extreme haste before a very long wifi-less flight; please forgive typos.] Part One: My take The real news here isn’t the result; it’s not what we were told. 1. AI once tried to be a science. Now we get stuff like the completely vague report from OpenAI below: “Same procedure”? “Using an unreleased model”? This would never pass peer review. We don’t know what the procedure was. We know nothing about the architecture. For esxample, were the proofs generated in one shot, and then verified by the symbolic system Lean? Was there an iterative process?) We know nothing about the failure rate. We know nothing about the training/post training/data augmention. 2. As a result, we have zero idea of how generalizable the result is outside math. 3. A lot of the discussion on social media has been reduced to an ignorant cheering section that applauds without knowing what it is applauding or what it might mean— without ever asking basic scientific questions. The new system could be a legitimate step toward AGI. Or it could just be a clever leveraging of Lean and synthetic data in a verifiable domain with no generality whatsoever. From the initial report, we can tell almost nothing. §