Rohan Paul· @rohanpaul_ai · X·· 2026-08-05AI 评分51
AI 导读
Google DeepMind首席战略官Jasjeet Sekhon称,当前AI基础设施支出是在为期望中的自我改进循环买单。其最强形式下,递归自我改进(RSI)将让AI以越来越少的人类监督设计、评估和训练更强的后继者。目前证据仍较窄:如Google AlphaEvolve提出算法后由自动评估器按人类目标打分,其内核改动仅将Gemini训练时间缩短1%,属有限改进而非自主设计后继者。
正文 · 原文
Recursive self-improvement → hyperexponential capability growth → the investment thesis behind present AI spending.
Google DeepMind Chief Strategy Officer Jasjeet Sekhon says AI infrastructure spending is financing a hoped-for self-improvement loop.
----
From "Berkeley RDI" YouTube channel, (full video link in comment)
“If you hit recursive self-improvement, that curve will go to hyperexponential, and that is a key part of the investment thesis, the scientific thesis, and a key part of why society’s investing what it’s currently investing in.” Google DeepMind Chief Strategy Officer Jasjeet Sekhon says AI infrastructure spending is financing a hoped-for self-improvement loop. In its strongest form, recursive self-improvement (RSI) will let AI design, evaluate, and train more capable successors with progressively less human supervision. The current evidence is still narrower: e.g. Google's AlphaEvolve proposes algorithms, then automated evaluators run and score them against human-defined objectives. Google says its resulting kernel changes reduced Gemini training time by 1%, an example of bounded improvement rather than autonomous successor design. ---- From "Berkeley RDI" YouTube channel, (full video link in comment)在 X 查看被引用的帖子
来源:Rohan Paul · x.com