An Unconventional Look at AI: Why Today’s Machine Learning Systems are not Intelligent

In LINKs: The Art of Linking, an Annual Transdisciplinary Review, Special Edition 1, Unconventional Computing. pp. 62-67 (2020)
  Copy   BIBTEX

Abstract

Machine learning systems (MLS) that model low-level processes are the cornerstones of current AI systems. These ‘indirect’ learners are good at classifying kinds that are distinguished solely by their manifest physical properties. But the more a kind is a function of spatio-temporally extended properties — words, situation-types, social norms — the less likely an MLS will be able to track it. Systems that can interact with objects at the individual level, on the other hand, and that can sustain this interaction, can learn responses to increasingly abstract properties, including representational ones. This representational capacity, arguably the mark of intelligence, then, is not available to current MLS’s

Author Profiles

Analytics

Added to PP
2022-07-15

Downloads
128 (#78,483)

6 months
46 (#76,011)

Historical graph of downloads since first upload
This graph includes both downloads from PhilArchive and clicks on external links on PhilPapers.
How can I increase my downloads?