Authors: Joshua Qin, Mohammad Khan, Jaray Liu
This blog post covers material for the sixth lecture of Harvard CS 2881r: AI Safety and Alignment, taught by Boaz Barak. We provide an outline of the lecture below.
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The idea of recursive self-improvement lies at the core of contemporary debates over AI takeoff dynamics. As models become active participants in their own development pipelines, such as optimizing architectures, generating training data, and automating research, the feedback loop between capability and capability growth begins to close. Recent analyses, from Epoch’s Gate Model to Anthropic’s “Takeoff Speeds” and the Three Types of Intelligence Explosion framework, attempt to formalize this transition: when does scaling shift from... (read 1735 more words →)