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.
great to see. as important as safety research is, if we don't get capabilities in time, most of humanity is going to be lost. long-termism requires aiming to preserve today's archeology, or the long-term future we hoped to preserve will be lost anyway. safety is also critical; differential acceleration of safe capabilities is important, so let's use this to try to contribute to capable safety.
I just wish lecun saw that facebook is catastrophically misaligned.