]This podcast discusses a new research paper from China titled "AlphaGo Moment for model architecture discovery" [00:05]. The paper introduces ASIArch, an AI system that autonomously innovates its own architecture, claiming that humans are the bottleneck in AI research [01:12, 01:21]. ASIArch reportedly conducted almost 2,000 autonomous experiments, discovering 106 innovative linear attention architectures, and the researchers claim to have established the first empirical scaling law for scientific discovery, suggesting that increased computation can lead to better architectures and more innovation [02:54, 03:31]. The research found that a small number of approaches yielded the majority of breakthroughs, with a significant contribution from the AI's own experience [08:29, 09:42]. While the implications are significant, some experts have expressed skepticism regarding the methodology [15:30, 15:56]. The research is open source, and the general trend of self-improving AI research is growing [17:18, 17:32]. [Gemini 2.5 Flash assisted with generating the summary of the this podcast]
https://youtu.be/QGeql15rcLo?
