Former employee
BBC事實查核（BBC Verify） 對這些說法進行了核實。
。关于这个话题，91视频提供了深入分析
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.。WPS下载最新地址是该领域的重要参考
过去AI进不了工厂，不是因为没有需求，而是因为模型能力还不够，加上工业企业的数据从来没有被系统化利用过。每一次设备维修、每一条生产记录、每一次质检结果，都沉睡在各自的系统里，没有人去碰。但现在模型能力的天花板已经大幅抬高，工业企业也开始意识到，自己手里握着的操作数据对AI公司来说是真金白银。这个意识一旦觉醒，工业AI的商业化就会加速。