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Doing More with Less: Meta-Reasoning and Meta-Learning in Humans and Machines




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.

Author(s): Thomas L. Griffiths and Frederick Callaway and Michael B. Chang and Erin Grant and Paul M. Krueger and Falk Lieder
Journal: Current Opinion in Behavioral Sciences
Volume: 29
Pages: 24--30
Year: 2019
Month: October

Department(s): Rationality Enhancement
Bibtex Type: Article (article)
Paper Type: Journal

DOI: 10.1016/j.cobeha.2019.01.005
Language: English
State: Published


  title = {Doing More with Less: Meta-Reasoning and Meta-Learning in Humans and Machines},
  author = {Griffiths, Thomas L. and Callaway, Frederick and Chang, Michael B. and Grant, Erin and Krueger, Paul M. and Lieder, Falk},
  journal = {Current Opinion in Behavioral Sciences},
  volume = {29},
  pages = {24--30},
  month = oct,
  year = {2019},
  month_numeric = {10}