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

Thomas L. Griffiths, Frederick Callaway, Michael B. Chang, Erin Grant, Paul M. Krueger, Falk Lieder

Research output: Contribution to journalReview articlepeer-review

4 Scopus citations

Abstract

Artificial 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.

Original languageEnglish (US)
Pages (from-to)24-30
Number of pages7
JournalCurrent Opinion in Behavioral Sciences
Volume29
DOIs
StatePublished - Oct 2019

All Science Journal Classification (ASJC) codes

  • Cognitive Neuroscience
  • Psychiatry and Mental health
  • Behavioral Neuroscience

Fingerprint Dive into the research topics of 'Doing more with less: meta-reasoning and meta-learning in humans and machines'. Together they form a unique fingerprint.

Cite this