Abstract
How could machines learn as efficiently as humans and animals? How could machines learn to reason and plan? How could machines learn representations of percepts and action plans at multiple levels of abstraction, enabling them to reason, predict, and plan at multiple time horizons? This position paper proposes an architecture and training paradigms with which to construct autonomous intelligent agents. It combines concepts such as configurable predictive world model, behavior driven through intrinsic motivation, and hierarchical joint embedding architectures trained with self-supervised learning.
Meta's Chief AI Scientist Yann Lecun lays out his vision for what an architecture for generally intelligent agents might look like.
My impression is that he's trying to do GOFAI with fully differentiable neural networks. I'm also not sure he's describing a GAI — I think he's starting by aiming for parity with the capabilities of a typical mammal, not human-level, and that's why he uses self-driving cars as an example.
Personally I think a move towards GOFAI-like ideas is a good intuition, but that insisting on keeping things fully differentiable is too constraining. I believe that at some level, we are going to need to move away from doing everything with gradient descent, and use something more like approximate Bayesianism, or at least RL.
I also think he's underestimating the influence of genetics in mammalian mental capabilities. He talks about the step of babies learning that the world is 3D not 2D — I think it's very plausible that adaptations for processing sensory data from a 3D rather than 2D world are already encoded in our genome, brain structure, and physiology in a many places.
If this is going to be a GAI architecture, then I think he's massively underthinking alignment.