Will Machine Learning Yield Machine Intelligence?

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

2 Citations (Scopus)


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
Number of pages3
ISBN (Electronic)978-3-319-96448-5
ISBN (Print)978-3-319-96447-8, 978-3-030-07194-3
Publication statusPublished - 2018
Externally publishedYes
Event3rd Conference on Philosophy and Theory of Artificial Intelligence, PT-AI 2017 - Leeds, United Kingdom
Duration: 4 Nov 20175 Nov 2017
Conference number: 3

Publication series

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


Conference3rd Conference on Philosophy and Theory of Artificial Intelligence, PT-AI 2017
Country/TerritoryUnited Kingdom


  • Machine Learning
  • Artificial Intelligence
  • Turing Test


Dive into the research topics of 'Will Machine Learning Yield Machine Intelligence?'. Together they form a unique fingerprint.

Cite this