Chollet on X: Meta-Learning and the Path to General Intelligence

Chollet identifies meta-learning — learning to learn efficiently — as a promising direction for addressing the fundamental limitation of current ML systems. Meta-learning approaches train systems not to perform well on specific tasks but to quickly adapt to new tasks using few examples. This is directly aligned with the intelligence definition in his 2019 paper: efficiency of skill acquisition. The thread surveys existing meta-learning approaches (MAML, ProtoNets, few-shot learning), their current limitations, and why he believes combining meta-learning with program synthesis could produce systems capable of solving ARC-level tasks.