Doing more with less: meta-reasoning and meta-learning in humans and machines

Publication date: October 2019Source: Current Opinion in Behavioral Sciences, Volume 29Author(s): Thomas L Griffiths, Frederick Callaway, Michael B Chang, Erin Grant, Paul M Krueger, Falk LiederArtificial intelligence systems use an increasing amount of computation and data to solve very specific problems. By contrast, human minds solve a wide range of problems using a fixed amount of computation and limited experience. We identify two abilities that we see as crucial to this kind of general intelligence: meta-reasoning (deciding how to allocate computational resources) and meta-learning (modeling the learning environment to make better use of limited data). We summarize the relevant AI literature and relate the resulting ideas to recent work in psychology.
Source: Current Opinion in Behavioral Sciences - Category: Psychiatry & Psychology Source Type: research