Excellent post about applying insights from ML (overfitting control) to a much broader class of systems that optimize against an objective: politics, science, orgs, daily life. Underfitting is underrated.
@karpathy
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MLPerf benchmark requires implementation of mitigations
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MLPerf benchmark needs some of these mitigations https://
x.com/jaschasd/statu
s/1589424193946648576?s=46&t=Yrwu6ciEa83A6HcKopDkNw
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AI Pub Aspires to Akhaliq’s Level of Twitter Usefulness
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AI Pub reaching for that @_akhaliq level of usefulness on AI twitter 🙂
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Stable Diffusion’s Reasonable Interpretation of My Image
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Base stable diffusion has a decent guess about me
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Using StableBoost to refine image generation through visual selection
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e.g. I used stableboost for this earlier tweet 🙂 – the prompt by itself gives bad, too diverse, not amazing results, but once I generated ~1000 I could visually narrow in on the composition I liked. Not sure how I'd get that by tuning the prompt alone
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Iterative approach to building and refining positive datasets
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from my own experience you want something interactive and change your mind around quite a bit. so you're building the positive set, seeing the results, then tweaking your positive set over time. it's an incremental iterative thing.
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Visual Iteration Over Text Prompts for AI Image Generation
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Sometimes it's difficult to put the look&feel of what you're after into text. You end up re-rolling results over and over again, looking for the needle in a haystack. stableboost flips it around – you create a large haystack of variations, then narrow in on the needle visually.