“How Fast Can Algorithms Advance Capabilities? | Epoch Gradient Update” by henryj
I'm cross-posting my guest post on Epoch's Gradient Updates newsletter, in which I describe some new research from my team at UChicago's XLab — roughly, the algorithmic improvements that most improve capabilities at scale are the ones that require the most compute to find and validate.
This week's issue is a guest post by Henry Josephson, who is a research manager at UChicago's XLab and an AI governance intern at Google DeepMind.
In the AI 2027 scenario, the authors predict a fast takeoff of AI systems recursively self-improving until we have superintelligence in just a few years.
Could this really happen? Whether it's possible may depend on if a software intelligence explosion — a series of rapid algorithmic advances that lead to greater AI capabilities — occurs.
A key crux in the debate about the possibility of a software intelligence explosion comes down to whether key algorithmic improvements scale [...]
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Outline:
(01:53) Are the best algorithmic improvements compute-dependent?
(07:48) Can Capabilities Advance With Frozen Compute? DeepSeek-V3
(08:55) What This Means for AI Progress
(12:50) Limitations
(14:10) Conclusion
The original text contained 7 footnotes which were omitted from this narration.
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First published:
May 16th, 2025
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Narrated by TYPE III AUDIO.
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Senaste avsnitt
En liten tjänst av I'm With Friends. Finns även på engelska.