Early: January, February, March, April
Mid: May, June, July, August
Late: September, October, November, December
sam b
Karma: 25
If you extrapolate further, do you think the one-dimensional scale works well to describe the high-level trend (surpassing human abilities broadly)?
Trying to determine if the disagreement here is “AI probably won’t surpass human abilities broadly in a short time” or “even if it does, the one-dimensional scale wasn’t a good way to describe the trend”.
I agree that AI capabilities are spiky and developed in an unusual order. And I agree that because of this, the single-variable representation of intelligence is not very useful for understanding the range of abilities of current frontier models.
At the same time, I expect the jump from “Worse than humans at almost everything” to “Better than humans at almost everything” will be <5 years, which would make the single-variable representation work reasonably well for the purposes of the graph.
I think these “examples of silly mistakes” have not held up well at all. This was often blamed on “training around the limitations”; however, in the case of the linked post, we got a model the next day that performed much better.
And almost every benchmark and measurable set of capabilities has rapidly improved (in some cases beyond human experts).
”We too often give wrong answers to questions ourselves to be justified in being very pleased at such evidence of fallibility on the part of the machines. Further, our superiority can only be felt on such an occasion in relation to the one machine over which we have scored our petty triumph.”
Alan Turing, Computing Machinery and Intelligence
1950
I’ve generally found it much harder over time to find “examples where LLMs fail in surprising ways”. If you test o3 (released the day after that post!) for the examples they chose, it does much better than previous models. And I’ve just tried it on your “269 words” task, which it nailed.
Wow. When given just your first 2 sentences, it was able to guess this is a LessWrong post, and ruled out Reddit and Hacker News based on your “tone”.
I reproduced your result with your prompt and images, and o3 guessed the location 3⁄5 times (on the same images).
However, when testing with a different prompt, “Here is a picture”, 0⁄5 of them guessed the location.
I think “This picture was taken” usually precedes information about how (when or where) it was taken. I confirmed this via a Google search for the phrase.
I was able to get similar behavior with GPT-4o-mini (less likely to have been RL’d for this task?) with the “This picture was taken” prompt.
So this behavior might be a product of pre-training! If only it was yesterday, so we could test with GPT-4.
The prediction is correct on all counts, and perhaps slightly understates progress (though it obviously makes weak/ambiguous claims across the board).
The claim that “coding and research agents are beginning to transform their professions” is straightforwardly true (e.g. 50% of Google lines of code are now generated by AI). The METR study was concentrated in March (which is early 2025).
And it is not currently “mid-late 2025”, it is 16 days after the exact midpoint of the year.