Gary Marcus 与 Terence Tao 评 OpenAI 数学模型发布：报告缺乏可审查细节
Complementary remarks from Gary Marcus and Terence Tao on OpenAI’s giant math drop
Gary Marcus 评 OpenAI 新数学成果，认为真正的新闻不是结果本身，而是报告完全没有说明验证流程、模型架构、失败率和训练细节，无法判断其通用性，也可能是仅适用于可验证领域的 Lean 与合成数据技巧。他还批评社交媒体的讨论沦为不加追问的喝彩，并附上 Terence Tao 转发的恶搞新闻稿作为第二部分。
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
来源：Gary Marcus：The Road to AI We Can Trust · garymarcus.substack.com