Will Machine Learning Yield Machine Intelligence?

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Abstract

This paper outlines the non-behavioral Algorithmic Similarity criterion for machine intelligence, and assesses the likelihood that it will eventually be satisfied by computers programmed using Machine Learning (ML). Making this assessment requires overcoming the Black Box Problem, which makes it difficult to characterize the algorithms that are actually acquired via ML. This paper therefore considers Explainable AI’s prospects for solving the Black Box Problem, and for thereby providing a posteriori answers to questions about the possibility of machine intelligence. In addition, it suggests that the real-world nurture and situatedness of ML-programmed computers constitute a priori reasons for thinking that they will not only learn to behave like humans, but that they will also eventually acquire algorithms similar to the ones that are implemented in human brains.

Original languageEnglish
Title of host publicationPhilosophy and Theory of Artificial Intelligence 2017
PublisherSpringer
Pages225-227
Number of pages3
Volume44
ISBN (Print)978-3-319-96447-8
DOIs
Publication statusPublished - 2018
Externally publishedYes

Publication series

NameStudies in Applied Philosophy, Epistemology and Rational Ethics
Volume44
ISSN (Print)2192-6255
ISSN (Electronic)2192-6263

Keywords

  • Machine Learning
  • Artificial Intelligence
  • Turing Test

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