281–290 of 394 posts
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Planes aren't trying to replace birds. ML is trying to replace humans, so unless they also demonstrate that quick learning ability isn't necessary to perform the tasks a human does the measures still make sense.
> ML is trying to replace humans Are household appliances trying to replace humans?
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My sense is that a powerful enough AI would have the sense to think something like "ah, this sounds like a video game! Let me code up an interactive GUI, test it for myself, then use it to solve these puzzles..." and essentially self-harness (the way you would if you were reading a geometry problem, by drawing it out on paper).
Yeah but thats literally above ASI, let alone AGI. Average human scores <1% on this bench, opus scores 97.1% when given an actual vision access, which means agi was long ago achieved
Do you have a source for this? I would be very curious to see how top models do with vision.
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> desire for self-preservation and intrinsic motivation I’d be curious about how you’re showing they lack either of those
They don't try to prevent you from deleting them and they don't output anything unless prompted.
Unprompted they're not unlike a human sleeping or in a coma. Those states don't preclude consciousness in other states.
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I'm all for testing humans and AI on a fair basis; how about we restrict testing to robots physically coming to our testing center to solve the environments via keyboard / mouse / screen like our human testers? ;-) (This version of the benchmark would be several orders of magnitude harder wrt current capabilities...)
This counterpoint doesn't address the issue, and I would argue that it is partially bad faith. Yes, making it to the test center is significantly harder, but in fact the humans could have solved it from their home PC instead, and performed the exact same. However, if they were given the same test as the LLMs, forbidden from input beyond JSON, they would have failed. And although buying robots to do the test is unfeas…
I assume you did not develop the puzzles by visualizing JSON yourselves, and so there might be non obvious information that is lost in translation to JSON. Until humans optimally solve all the puzzles without ever having seen the visual version, there is no guarantee that this is even possible to do.
I think the only viable solution here is to release a version of the benchmark with a vision only harness. Otherwise it is impossible to interpret what LLM progress on this benchmark actually means.
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Will there be a point in that series of ARC-AGI tests where AI can design the next test, or is designing the next text always going to be a problem that can be solved by humans and not AI?
I don't see why AI couldn't design tests. But they can only be validated by humans, as they are intended to be possible and ideally easy for humans to solve.
Even with billions of dollars spent on training, we had this situation a few weeks ago where models were suggesting to walk instead of drive to a car wash in case you want to wash your car. While a 3 year old would know the answer to the question. And yet, we are designing elaborate tests to 'show whether AGI is here it not', while being fully aware of what these models represent under the hood.
https://x.com/scaling01 has called out a lot of issues with ARC-AGI-3, some of them (directly copied from tweets, with minimal editing): - Human baseline is "defined as the second-best first-run human by action count". Your "regular people" are people who signed up for puzzle solving and you don't compare the score against a human average but against the second best human solution - The scoring doesn't tell you how m…
Francois here. The scoring metric design choices are detailed in the technical report: https://arcprize.org/media/ARC_AGI_3_Technical_Report.pdf - the metric is meant to discount brute-force attempts and to reward solving harder levels instead of the tutorial levels. The formula is inspired by the SPL metric from robotics navigation, it's pretty standard, not a brand new thing. We tested ~500 humans over 90 minute se…
If I understand correctly the model can carry only very limited memory among tests, so it looks like it's not really possible for the model to self specialize itself under this assumptions.