Import AI 456: RSI and Economic Growth; Radical Optionality for AI Regulation; Neuromorphic Computing

The article discusses the concept of "radical optionality" for AI regulation proposed by researchers at the Institute for Law and AI. This concept implies that governments should now invest significant resources in creating institutions, information channels, and legal authorities to flexibly respond to the development of transformative AI in the future, while avoiding premature overregulation. Recommended measures include transparency requirements, whistleblower protections, information sharing between governments, flexible rules, risk assessments, protection of model weights and algorithmic secrets, and recruitment of technical talent. Separately, a study on neurocomputers from Meta and KAIST is discussed, which proposes replacing traditional software with a single neural network that performs computation, memory, and input/output functions. Prototypes based on the Wan 2.1 generative video model demonstrated the ability to work with command-line and graphical interfaces. The third topic is an economic model showing that recursive self-improvement of AI could lead to explosive economic growth. The authors from Forethought, Columbia University, and the University of Virginia found that automating 13% of economic sectors is sufficient to trigger explosive growth, with hardware automation having the greatest impact. Full automation of software research and 5% automation of other sectors could lead to a singularity in about six years.