41–50 of 103 posts
Probably openai will be >60% in three months if not immediately with these $1000/question level compute (which is the way tbh we should throw compute whenever possible that's the main advantage of silicon intelligence)
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Sorry, I probably phrased the question poorly. My question is more along the lines of "when you already scored e.g. OpenAI's o3 on ARC AGI 2 how did you guarantee OpenAI can't just look at its server logs to see question set 4"?
Ah yes, two things 1. We had a no-data retention agreement with them. We were assured by the highest level of their company + security division that the box our test was run on would be wiped after testing 2. We only tested o3 against the semi-private set. We didn't test it with the private eval.
> and was the only benchmark to pinpoint the exact moment in late 2024 when AI moved beyond pure memorization This is self-referential, the benchmark pinpointed the time when AI went from memorization to problem solving, because the benchmark requires problem solving to complete. How do we know it requires problem solving skills? Because memorization-only LLMs can't do it but humans can. I think ARC are producing som…
The reason these tasks require fluid intelligence is because they were designed this way -- with task uniqueness/novelty as the primary goal. ARC 1 was released long before in-context learning was identified in LLMs (and designed before Transformer-based LLMs existed), so the fact that LLMs can't do ARC was never a design consideration. It just turned out this way, which confirmed our initial assumption.
I think a similar claim could be levelled against other benchmarks or LLM evaluation tasks. One could say that the Turing test was designed to assess human intelligence, and LLMs pass it, therefore LLMs have human intelligence. This is generally considered to be false now, because we can plainly see that LLMs do not have intelligence in the same way as humans (yet? debatable, not the point), and instead we concluded that the Turing test was not the right benchmark. That's not to diminish its importance, it was hugely important as a part of AI education and possibly even AI development for decades.
ARC does seem to be pushing the boundaries, I'm just not convinced that it's testing a provable step change.
Reasoner passed on first try.
“Correct!”
(See screenshot that shows one rated “hard” -- https://www.linkedin.com/posts/waynechang_tried-reasoner-on-...)
Hey HN, Greg from ARC Prize Foundation here. Alongside Mike Knoop and François Francois Chollet, we’re launching ARC-AGI-2, a frontier AI benchmark that measures a model’s ability to generalize on tasks it hasn’t seen before, and the ARC Prize 2025 competition to beat it. In Dec ‘24, ARC-AGI-1 (2019) pinpointed the moment AI moved beyond pure memorization as seen by OpenAI's o3. ARC-AGI-2 targets test-time reasoning.…
You have my wheels turning on how to get computers better at these. Looking forward to see G the first computer tech that can get 30-50% on these!
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ARC 3 is still spatially 2D, but it adds a time dimension, and it's interactive.
I think a lot of people got discouraged, seeing how openai solved arc agi 1 by what seems like brute forcing and throwing money at it. Do you believe arc was solved in the "spirit" of the challenge? Also all the open sourced solutions seem super specific to solving arc. Is this really leading us to human level AI at open ended tasks?
I'd encourage you to review the definition of "brute force", and then consider the absolutely immense combinatoric space represented by the grids these puzzles use.
"Brute force" simply cannot touch these puzzles. An amount of understanding and pattern recognition is strictly required, even with the large quantities of test-time compute that were used against arc-agi-1.
Hey HN, Greg from ARC Prize Foundation here. Alongside Mike Knoop and François Francois Chollet, we’re launching ARC-AGI-2, a frontier AI benchmark that measures a model’s ability to generalize on tasks it hasn’t seen before, and the ARC Prize 2025 competition to beat it. In Dec ‘24, ARC-AGI-1 (2019) pinpointed the moment AI moved beyond pure memorization as seen by OpenAI's o3. ARC-AGI-2 targets test-time reasoning.…
Earlier quoted context omitted.
Ah yes, two things 1. We had a no-data retention agreement with them. We were assured by the highest level of their company + security division that the box our test was run on would be wiped after testing 2. We only tested o3 against the semi-private set. We didn't test it with the private eval.
>> We were assured by the highest level of their company + security division that the box our test was run on would be wiped after testing Yuri Geller assured us he was bending the spoons with his mind. Somehow it was only when the Amazing Randi was present that Yuri Geller couldn't bend the spoons with his mind.
Hey HN, Greg from ARC Prize Foundation here. Alongside Mike Knoop and François Francois Chollet, we’re launching ARC-AGI-2, a frontier AI benchmark that measures a model’s ability to generalize on tasks it hasn’t seen before, and the ARC Prize 2025 competition to beat it. In Dec ‘24, ARC-AGI-1 (2019) pinpointed the moment AI moved beyond pure memorization as seen by OpenAI's o3. ARC-AGI-2 targets test-time reasoning.…
Earlier quoted context omitted.
I think a lot of people got discouraged, seeing how openai solved arc agi 1 by what seems like brute forcing and throwing money at it. Do you believe arc was solved in the "spirit" of the challenge? Also all the open sourced solutions seem super specific to solving arc. Is this really leading us to human level AI at open ended tasks?
It's useful to know what current AI systems can achieve with unlimited test-time compute resources. Ultimately though, the "spirit of the challenge" is efficiency, which is why we're specifically looking for solutions that are at least within 1-2 order of magnitude of cost from being competitive with humans. The Kaggle leaderboard is very resource-constrained, and on the public leaderboard you need to use less than $…
$10000 in compute is a moving target, today's GPUs are much much better than 10 years ago.