Goedecke's post gains traction on Hacker News
2 Nov 2 5:26 PM · 5d ago · 2 posts · 2 sources · development 2 of 2
The analysis is shared on Hacker News where it reaches 51 points and sparks discussion. The post gains visibility as the technical community engages with the argument that weight-editing, while a neat capability, does not represent a paradigm shift in AI research.
Sean Goedecke ML researcher and analystGPT-6 Astra Frontier LLM model
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One could say they might be "superhuman" here in the same way that LLMs might be said to be superhuman at math. Perhaps they do this work faster and more effectively than any person could manually. But they are certainly still doing it the "human" way ie reasoning with legible and coherent thoughts, rather than communing through some Matrix-like…
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False, in a way that genuinely matters.True: they are superhuman at every one of these tasks, in the boring way they are better—according to a wall clock and the coupled standard of actual results—at other things, just not in some mystical way.
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> the best way to work with binaries is still to write source code and compile it. Likewise, the best way to update the weights of a model is to train it normally.This is why the distinction between open weights and open source is important.
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I would have gotten none of them right. Now if you said "here are the four possible games, now match them up" then I probably could do decently. But if you gave me nothing and said tell me the game -- I'd get 0 out of the 4.
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"If someone showed you that heatmap without the game labels, I think you’d be able to immediately identify at least the first three games" -- absolutely not. Pac-Man in particular seems like a huge stretch to me.
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We keep moving the goalposts, I love it. A few years ago LLMs writing simple functions was downplayed and now this. I lovehate the future this will bring.
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Really? Pac-Man seemed easiest to me.(One possible disconnect: each row in the grid is one game, so Pac-Man is the third row, not the third image.)
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I disagree with the last paragraph. Training is costly because data is costly. Reduced reliance on training is a significant development.
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"I think a smart human ML researcher could perform all of these tasks."And do that for how long?
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