@article{GriffithsEtAl2019,
  title = {Doing More with Less: Meta-Reasoning and Meta-Learning in Humans and Machines},
  journal = {Current Opinion in Behavioral Sciences},
  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.},
  volume = {29},
  pages = {24--30},
  month = oct,
  year = {2019},
  slug = {griffithsetal2019},
  author = {Griffiths, Thomas L. and Callaway, Frederick and Chang, Michael B. and Grant, Erin and Krueger, Paul M. and Lieder, Falk},
  month_numeric = {10}
}