Universality is Key:
Returning back to the Anthropic compiler attempt: one of the steps that the agent failed was the one that was more strongly related to the idea of memorization of what is in the pretraining set: the assembler. With extensive documentation, I can’t see any way Claude Code (and, even more, GPT5.3-codex, which is in my experience, for complex stuff, more capable) could fail at producing a working assembler, since it is quite a mechanical process. This is, I think, in contradiction with the idea that LLMs are memorizing the whole training set and uncompress what they have seen. LLMs can memorize certain over-represented documents and code, but while they can extract such verbatim parts of the code if prompted to do so, they don’t have a copy of everything they saw during the training set, nor they spontaneously emit copies of already seen code, in their normal operation. We mostly ask LLMs to create work that requires assembling different knowledge they possess, and the result is normally something that uses known techniques and patterns, but that is new code, not constituting a copy of some pre-existing code.
换言之,在他看来,AI 不是能源杀手,而是未来文明的必需品,就像电灯发明时也有人担心蜡烛业失业一样。这个观点不是 Altman 首创。早在 AI 热潮前,就有专家比过生物大脑和硅芯片的效率。但 Altman 作为 OpenAI 老大,说出来影响力大,瞬间成了 X 上的热点,视频有两千多万次浏览,引爆了讨论。,这一点在服务器推荐中也有详细论述
FT Professional。关于这个话题,同城约会提供了深入分析
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