A Path Towards Autonomous Machine Intelligence

Each week Tenstorrent will be highlighting a paper that has inspired our product development.

A Path Towards Autonomous Machine Intelligence by Yann LeCun.

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.

Read the full white paper here.