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Publisher: The MIT Press
Published: 03 September 2024
DOI: 10.7551/mitpress/14940.003.0011
EISBN: 9780262380355
Publisher: The MIT Press
Published: 03 September 2024
DOI: 10.7551/mitpress/14940.003.0012
EISBN: 9780262380355
Publisher: The MIT Press
Published: 03 September 2024
DOI: 10.7551/mitpress/14940.003.0013
EISBN: 9780262380355
Publisher: The MIT Press
Published: 03 September 2024
DOI: 10.7551/mitpress/14940.003.0014
EISBN: 9780262380355
Publisher: The MIT Press
Published: 03 September 2024
DOI: 10.7551/mitpress/14940.003.0015
EISBN: 9780262380355
Publisher: The MIT Press
Published: 03 September 2024
DOI: 10.7551/mitpress/14940.001.0001
EISBN: 9780262380355
A novel approach to hybrid AI aimed at developing trustworthy agent collaborators. The vast majority of current AI relies wholly on machine learning (ML). However, the past thirty years of effort in this paradigm have shown that, despite the many things that ML can achieve, it is not an all-purpose solution to building human-like intelligent systems. One hope for overcoming this limitation is hybrid AI: that is, AI that combines ML with knowledge-based processing. In Agents in the Long Game of AI , Marjorie McShane, Sergei Nirenburg, and Jesse English present recent advances in hybrid AI with special emphases on content-centric computational cognitive modeling, explainability, and development methodologies. At present, hybridization typically involves sprinkling knowledge into an ML black box. The authors, by contrast, argue that hybridization will be best achieved in the opposite way: by building agents within a cognitive architecture and then integrating judiciously selected ML results. This approach leverages the power of ML without sacrificing the kind of explainability that will foster society's trust in AI. This book shows how we can develop trustworthy agent collaborators of a type not being addressed by the “ML alone” or “ML sprinkled by knowledge” paradigms—and why it is imperative to do so.
Publisher: The MIT Press
Published: 03 September 2024
DOI: 10.7551/mitpress/14940.003.0001
EISBN: 9780262380355
Publisher: The MIT Press
Published: 03 September 2024
DOI: 10.7551/mitpress/14940.003.0002
EISBN: 9780262380355
Publisher: The MIT Press
Published: 03 September 2024
DOI: 10.7551/mitpress/14940.003.0003
EISBN: 9780262380355
Publisher: The MIT Press
Published: 03 September 2024
DOI: 10.7551/mitpress/14940.003.0004
EISBN: 9780262380355
Publisher: The MIT Press
Published: 03 September 2024
DOI: 10.7551/mitpress/14940.003.0005
EISBN: 9780262380355
Publisher: The MIT Press
Published: 03 September 2024
DOI: 10.7551/mitpress/14940.003.0006
EISBN: 9780262380355
Publisher: The MIT Press
Published: 03 September 2024
DOI: 10.7551/mitpress/14940.003.0007
EISBN: 9780262380355
Publisher: The MIT Press
Published: 03 September 2024
DOI: 10.7551/mitpress/14940.003.0008
EISBN: 9780262380355
Publisher: The MIT Press
Published: 03 September 2024
DOI: 10.7551/mitpress/14940.003.0009
EISBN: 9780262380355
Publisher: The MIT Press
Published: 03 September 2024
DOI: 10.7551/mitpress/14940.003.0010
EISBN: 9780262380355
Publisher: The MIT Press
Published: 18 June 2024
DOI: 10.7551/mitpress/15192.003.0001
EISBN: 9780262378949
Publisher: The MIT Press
Published: 18 June 2024
DOI: 10.7551/mitpress/15192.003.0002
EISBN: 9780262378949
Publisher: The MIT Press
Published: 18 June 2024
DOI: 10.7551/mitpress/15192.003.0003
EISBN: 9780262378949
Publisher: The MIT Press
Published: 18 June 2024
DOI: 10.7551/mitpress/15192.003.0004
EISBN: 9780262378949
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