Building Machines that Learn and Think for Themselves: Commentary on Lake et al., Behavioral and Brain Sciences, 2017
This commentary addresses the challenge of scaling AI to real-world complexity for researchers in cognitive science and AI, but it is incremental as it builds on existing ideas without introducing new methods.
The authors argue for autonomy as a key ingredient in building humanlike intelligence, emphasizing agents that can create and use their own internal models with minimal human intervention to tackle complex real-world domains lacking formal models.
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.