Portal de Periódicos da CAPES

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

2019; Elsevier BV; Volume: 29; Linguagem: Inglês

10.1016/j.cobeha.2019.01.005

2352-1554

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

Domain Adaptation and Few-Shot Learning

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.