It’s interesting, LeCun seems to have a blind spot around in-context learning. I didn’t find one mention in this paper (only skimmed the full paper so far so may have missed), which is odd as it is the way that agents come closest to autonomous learning in the real world.

I would say his core point does still apply; autonomous learning is not solved by ICL. But it seems a strawman to ignore the topic entirely and focus on training.

From what I see on the ground, some degree of autonomous learning is possible; Agents can already be set up to use meta-learning skills for skill authoring, introspection, rumination, etc - but these loops are not very effective currently.

I wonder if this is the myopic viewpoint of a scientist who doesn’t engage with the engineering of how these systems are actually used in the real world (ie “my work is done once Llama is released with X score on Y eval”) which results in a markedly different stance than the guys like Sutskever, Karpathy, Amodei who have built end-to-end systems and optimized for customer/business outcomes.