PapersToAGI

Summary: Power concentration via AI is likely a greater near-term risk than classical misalignment, primarily because (a) it converges under a wider range of AI development scenarios including ones where alignment succeeds, (b) the timelines are faster and more empirically grounded, and (c) the three mechanisms society would normally use...

There is still a lingering belief from classical machine learning that bigger models overfit and thus don't generalize well. This is described by the bias-variance trade-off, but this no longer holds in the new age of machine learning. This is empirically shown by phenomena like double descent, where higher-complexity models...