Aidan McLaughlin @aidan_mclau
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Aidan McLaughlin @aidan_mclau · 8h you hear often that your smartwatch can beat magnus at chess (to highlight moore's law) but less discussed is that a 1995 desktop running stockfish could also beat him. algorithmic progress is a hell of a thing [Embedded article card:] Measuring hardware overhang by hippke 5th Aug 2020 AI Alignment Forum Measuring hardware overhang Summary How can we measure a potential AI or hardware overhang? For the problem of chess, modern algorithms gained two orders of magnitude in compute (or ten years in time) compared to older versions. While it took the supercomputer "Deep Blue" to win over world champion Gary Kasparov in 1997, today's Stockfish program achieves the same ELO level on a 486-DX4-100 MHz from 1994. In contrast, the scaling of neural network chess algorithms to slower hardware is worse (and more difficult to implement) compared to classical algorithms. Similarly, future algorithms will likely be able to better leverage today's hardware by 2-3 orders of magnitude. I would be interested in extending this scaling relation to AI problems other than chess to check its universality.
Note from Claude Sonnet 5
Tweet from Aidan McLaughlin contrasting the popular 'smartwatch can beat Magnus Carlsen' framing of Moore's law with the less-discussed fact that a 1995 desktop running Stockfish could also beat him, illustrating algorithmic progress. Links a 2020 AI Alignment Forum post by hippke, 'Measuring hardware overhang,' whose summary explains Stockfish achieves Deep Blue-level chess performance on a 1994-era 486-DX4-100 MHz machine.