In projecting ARC challenge progress with a naive regression from the latest cycle of improvement (from 34% to 54%), it seems that a plausible estimate as to when the 85% target will be reached is sometime between late 2025 & mid 2026.
Supposing ARC challenge target is reached in the coming years, does this update your model of 'AI risk'? // Would this cause you to consider your article on 'The implausibility of intelligence explosion' to be outdated?
There is a distinction between solving ARC, creating AGI, and creating an AI that would represent an existential risk. ARC is a stepping stone towards AGI, so the first model that solves ARC should have taught us something fundamental about how to create truly general intelligence that can adapt to never-seen-before problem, but it will likely not itself be AGI (due to be specialized in the ARC format, for instance). Its architecture could likely be adapted into a genuine AGI, after a few iterations -- a system capable of solving novel scientific problems in any domain.
Even this would not clearly lead to "intelligence explosion". The points in my old article on intelligence explosion are still valid -- while AGI will lead to some level of recursive self-improvement (as do many other systems!) the available evidence just does not point to this loop triggering an exponential explosion (due to diminishing returns and the fact that "how intelligent one can be" has inherent limitations brought about by things outside of the AI agent itself). And intelligence on its own, without executive autonomy or embodiment, is just a tool in human hands, not a standalone threat. It can certainly present risks, like any other powerful technology, but it isn't a "new species" out to get us.
I too, believe that ARC will soon be solved: in the same way that the Winograd Schema Challenge was solved. Someone will finally decide to generate a large enough dataset to fine-tune a big, deep, bad LLM and go to town, and I do mean on the private test set. If ARC was really, really a test of intelligence and therefore protected against Big Data approaches, then it wouldn't need to have a super secret hidden test set. Bongard Problems don't and they still stand undefeated (although the ANN community has sidestepped them in a sense, by generating and solving similar, but not identical, sets of problems, then claiming triumph anyway).
ARC will be solved and we won't learn anything at all from it, except that we still don't know how to test for intelligence, let alone artificial intelligence.
The worst outcome of all this is the collateral damage to the reputation of symbolic program synthesis which you have often name-dropped when trying to steer the efforts of the community towards it (other times calling it "discrete program search" etc). Once some big, compensating, LLM solves ARC, any mention of program synthesis will elicit nothing but sneers. "Program synthesis? Isn't that what Chollet thought would solve ARC? Well, we don't need that, LLMs can solve ARC just fine". Talk about sucking out all the air from the room, indeed.
Are there benchmarks that you prefer?