dynomight's Shortform

dynomight

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In June 2025, I suggested that you could take the METR data, and instead of trying to fit a mathematical model, you could just scribble curves on top of it:

I still feel semi-good about this general forecasting strategy, in the sense that I think a mathematical model needs quite a lot of development before you should really trust it more than this kind of scribbling. On the other hand... only a 10% chance of reaching the 1 month threshold by September, 2028? That seems extremely hard to justify at this point.

You could argue that that's fine, I'm just updating on new data. But I think that lets me off the hook too easily.

I think a better explanation is that I screwed up, in the sense that the curves I drew don't give enough weight to the possibility of acceleration. Furthermore, I think this was knowable at the time. If you'd asked me in June 2025, "what's the chance that the curve continues to accelerate?" I'm pretty sure I would have said at least 10%. But the curves I drew don't reflect that.

3dynomight3mo

I agree that this is an accurate description. The reason I find it helpful is that I find it much easier to inspect my internal probability distribution through the API of "sample one possible future" than by directly trying to postulate number like "what is the probability that the METR timeline reaches X horizon by date Y".

1Avi Brach-Neufeld3mo

I guess to each their own. Personally, I don’t even know how I’d go about sampling one possible future without first making my probability distribution explicit. It’s hard for me to imagine someone else being able to do that in a meaningful way, but that very well may be a failure of imagination!

Meanwhile, I don't even know what it would mean to make a probability distribution explicit. The space of curves a trendline might follow in the future is a high dimensional object. Stating a distribution over that space of curves is not a simple thing to do.

dynomight's Shortform

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dynomight's Shortform

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