ARC Prize – a $1M+ competition towards open AGI progress

ARC-AGI is (to our knowledge) the only eval which measures AGI: a system that can efficiently acquire new skill and solve novel, open-ended problems. Most AI evals measure skill directly vs the acquisition of new skill.

Francois created the eval in 2019, SOTA was 20% at inception, SOTA today is only 34%. Humans score 85-100%. 300 teams attempted ARC-AGI last year and several bigger labs have attempted it.

While most other skill-based evals have rapidly saturated to human-level, ARC-AGI was designed to resist “memorization” techniques (eg. LLMs)

Solving ARC-AGI tasks is quite easy for humans (even children) but impossible for modern AI. You can try ARC-AGI tasks yourself here: https://arcprize.org/play

ARC-AGI consists of 400 public training tasks, 400 public test tasks, and 100 secret test tasks. Every task is novel. SOTA is measured against the secret test set which adds to the robustness of the eval.

Solving ARC-AGI tasks requires no world knowledge, no understanding of language. Instead each puzzle requires a small set of “core knowledge priors” (goal directedness, objectness, symmetry, rotation, etc.)

At minimum, a solution to ARC-AGI opens up a completely new programming paradigm where programs can perfectly and reliably generalize from an arbitrary set of priors. At maximum, unlocks the tech tree towards AGI.

Our goal with this competition is:

1. Increase the number of researchers working on frontier AGI research (vs tinkering with LLMs). We need new ideas and the solution is likely to come from an outsider! 2. Establish a popular, objective measure of AGI progress that the public can use to understand how close we are to AGI (or not). Every new SOTA score will be published here: https://x.com/arcprize 3. Beat ARC-AGI and learn something new about the nature of intelligence.

Happy to answer questions!

ARC Prize – a $1M+ competition towards open AGI progress
arcprize.org

Not sure If I have the skills to make an entry, but I'll be watching at least.

To OP: I like your project goal. I think you should look at prior, reasoning engines that tried to build common sense. Cyc and OpenMind are examples. You also might find use for the list of AGI goals in Section 2 of this paper:

https://arxiv.org/pdf/2308.04445

When studying intros of brain function, I also noted many regions tie into the hippocampus which might do both sense-neutral storage of concepts and make inner models (or approximations) of external world. The former helps tie concepts together through various senses. The latter helps in planning when we are imagining possibilities to evaluate and iterate on them.

Seems like AGI should have these hippocampus-like traits and those in the Cyc paper. One could test if an architecture could do such things in theory or on a small scale. It shouldn’t tie into just one type of sensory input either. At least two with the ability to act on what only exists in one or what is in both.

Edit: Children also have an enormous amount of unsupervised training on visual and spatial data. They get reinforcement through play and supervised training by parents. A realistic benchmark might similarly require GB of prettaining.

However, I do disagree that this problem represents “AGI”. It’s just a different dataset than what we’ve seen with existing ML successes, but the approaches are generally similar to what’s come before. It could be that some truly novel breakthrough which is AGI solves the problem set, but I don’t think solving the problem set is a guaranteed indicator of AGI.