My hypothesis is that poor performance on ARC is largely due to lack of training data. If there were billions of diverse input/output examples to train on, I would guess standard techniques would work.
Efficiently learning from just a few examples is something that humans are still relatively good at, especially in simple cases where system1and system 2 synergize well. I’m not aware of many cases where AI approaches human level without orders of magnitude more training data than a human ever sees in a lifetime.
I think the ARC challenge can be solved within a year or two, but doing so won’t be super interesting to me unless it breaks new ground in sample efficiency (not trained on billions of synthetic examples) or generalization (e.g. solved using existing LLMs rather than a specialized net).
My hypothesis is that poor performance on ARC is largely due to lack of training data. If there were billions of diverse input/output examples to train on, I would guess standard techniques would work.
Efficiently learning from just a few examples is something that humans are still relatively good at, especially in simple cases where system1and system 2 synergize well. I’m not aware of many cases where AI approaches human level without orders of magnitude more training data than a human ever sees in a lifetime.
I think the ARC challenge can be solved within a year or two, but doing so won’t be super interesting to me unless it breaks new ground in sample efficiency (not trained on billions of synthetic examples) or generalization (e.g. solved using existing LLMs rather than a specialized net).