[FergusArgyll]

I'd love to hear from the ARC guys:

These benchmarks, and specifically the constraints placed on solving them (compute etc) seem to me to incentivize the opposite of "general intelligence"

Have any of the technical contributions used to win the past competition been used to advance general AI in any way?

We have transformer based systems constantly gaining capabilities. On the other hand have any of the Kaggle submissions actually advanced the field in any way outside of the ARC Challenge?

To me (a complete outsider, admittedly) the ARC prize seems like an operationalization of the bitter lesson

[gkamradt]

Good question! This was one of the main motivations of our "Paper Prize" track. We wanted to reward conceptual progress vs leaderboard chasing. In fact, when we increased the prizes mid year we awarded more money towards the paper track vs top score.

We had 40 papers submitted last year and 8 were awarded prizes. [1]

On of the main teams, MindsAI, just published their paper on their novel test time fine tuning approach. [2]

Jan/Daniel (1st place winners last year) talk all about their progress and journey building out here [3]. Stories like theirs help push the field forward.

[1] https://arcprize.org/blog/arc-prize-2024-winners-technical-r...

[2] https://github.com/MohamedOsman1998/deep-learning-for-arc/bl...

[3] https://www.youtube.com/watch?v=mTX_sAq--zY

[jononor]

Not the team, just follow ARC on-and-off as a ML engineer. I think it will take a few years (at least) to see the impact of ARC, especially the more conceptual works. Those are more close to basic research than applied - It will take time before the lessons are transferred to applications (that also requires considerable R&D). But more importantly, current LLM-based systems and the in-the-spirit-of-ARC-systems have quite different goals. The ARC challenge is intended to measure and build system which can learn efficiently - that is, be able to solve a novel task with very little new data. Ref F. Chollet paper "On the Measure of Intelligence". Current LLMs do not care for learning efficiency at all - actually the strategy is completely opposite - they aim to utilize ss much data and compute as possible to make the most capable system (at least on task that are somehow spanned by the training data). Which works well, but is for sure quite costly and it might also limit applications to those that not require a lot of learning at runtime (we still do not know how far we can take in-context learning). ARC brings in a fresh perspective, but I expect it to take several years for the approaches to really start cross-pollinating.