Building Machines that Learn and Think for Themselves: Commentary on Lake et al., Behavioral and Brain Sciences, 2017

TL;DR

This paper emphasizes the importance of autonomy in developing humanlike AI, advocating for agents that autonomously build and utilize internal models to adapt to complex real-world tasks.

Contribution

It highlights the role of autonomy in AI development and surveys progress and challenges in creating self-learning, humanlike agents.

Findings

Autonomous agents can develop internal models with minimal human intervention.

Progress has been made in building agents with humanlike reasoning abilities.

Challenges remain in scaling autonomous learning to complex real-world domains.

Abstract

We agree with Lake and colleagues on their list of key ingredients for building humanlike intelligence, including the idea that model-based reasoning is essential. However, we favor an approach that centers on one additional ingredient: autonomy. In particular, we aim toward agents that can both build and exploit their own internal models, with minimal human hand-engineering. We believe an approach centered on autonomous learning has the greatest chance of success as we scale toward real-world complexity, tackling domains for which ready-made formal models are not available. Here we survey several important examples of the progress that has been made toward building autonomous agents with humanlike abilities, and highlight some outstanding challenges.