This document is not a technical nor scholarly paper in the traditional sense, but a position
paper expressing my vision for a path towards intelligent machines that learn more like
animals and humans, that can reason and plan, and whose behavior is driven by intrinsic
objectives, rather than by hard-wired programs, external supervision, or external rewards.
Many ideas described in this paper (almost all of them) have been formulated by many
authors in various contexts in various form. The present piece does not claim priority for
any of them but presents a proposal for how to assemble them into a consistent whole. In
particular, the piece pinpoints the challenges ahead. It also lists a number of avenues that
are likely or unlikely to succeed.
The text is written with as little jargon as possible, and using as little mathematical
prior knowledge as possible, so as to appeal to readers with a wide variety of backgrounds
including neuroscience, cognitive science, and philosophy, in addition to machine learning,
robotics, and other fields of engineering. I hope that this piece will help contextualize some
of the research in AI whose relevance is sometimes difficult to see.