Филолог заявил о массовой отмене обращения на «вы» с большой буквы09:36
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.
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一群研究人員決定測試「正向思考」是否能提高人工智慧（AI）聊天機器人的準確率，結果卻出乎意料。他們向不同的聊天機器人提問，嘗試稱讚它們「聰明」，鼓勵它們認真思考，甚至在問題結尾加上一句「這會很有趣！」。然而，這些方法都沒有產生一致的效果，但其中一種方法脫穎而出。當他們讓AI假裝自己身處《星際迷航》場景，它的基礎數學能力竟然有所提升。看來，它真的能把我傳送上去。
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