Jack and Mohammed explain their winning approach, which involves fine-tuning a language model on a large, specifically-generated dataset and then doing additional fine-tuning at test-time, a technique known in this context as "active inference". They use various strategies to represent the data for the language model and believe that with further improvements, the accuracy could reach above 50%. Michael talks about his work on generating new ARC-like tasks to help train the models.

They also debate whether their methods stay true to the "spirit" of Chollet's measure of intelligence. Despite some concerns, they agree that their solutions are promising and adaptable for other similar problems.

Note:

Jack's team is still the current official winner at 33% on the private set. Ryan's entry is not on the private leaderboard or eligible.

Chollet invented ARC in 2019 (not 2017 as stated)

"Ryan's entry is not a new state of the art. We don't know exactly how well it does since it was only evaluated on 100 tasks from the evaluation set and does 50% on those, reportedly. Meanwhile Jacks team i.e. MindsAI's solution does 54% on the entire eval set and it is seemingly possible to do 60-70% with an ensemble"

Jack Cole:

https://x.com/Jcole75Cole

https://lab42.global/community-interview-jack-cole/

Mohamed Osman:

Mohamed is looking to do a PhD in AI/ML, can you help him?

Email: [email protected]

https://www.linkedin.com/in/mohamedosman1905/

Michael Hodel:

https://arxiv.org/pdf/2404.07353v1

https://www.linkedin.com/in/michael-hodel/

https://x.com/bayesilicon

https://github.com/michaelhodel

Getting 50% (SoTA) on ARC-AGI with GPT-4o - Ryan Greenblatt

https://redwoodresearch.substack.com/p/getting-50-sota-on-arc-agi-with-gpt

Neural networks for abstraction and reasoning: Towards broad generalization in machines [Mikel Bober-Irizar, Soumya Banerjee]

https://arxiv.org/pdf/2402.03507

Measure of intelligence:

https://arxiv.org/abs/1911.01547

YT version: https://youtu.be/jSAT_RuJ_Cg