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Gary Marcus 与 Terence Tao 对 OpenAI 数学成果的补充评论
Gary Marcus 撰文评论 OpenAI 新发布的数学成果，认为真正的新闻不是结果本身，而是未被披露的信息：报告未说明具体流程、架构、失败率以及训练/后训练/数据增强细节，因此无法判断该结果在数学领域之外的泛化能力。文章还提及 Terence Tao 的看法，但正文片段未展开其具体观点。
AI推荐理由 Gary Marcus 对 OpenAI 数学成果的批评性评论，指出报告缺乏方法、架构、失败率和训练细节，无法判断结果在数学之外的泛化性；文中还提到 Terence Tao 的观点。属于对具体发布的方法论质疑，有明确论点，但正文为片段且未给出 Tao 的具体内容。
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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. § Part Two: Terence Tao’s take Subscribe now Share