Agents in the Long Game of AI
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 s...
Sábháilte in:
| Príomhchruthaitheoirí: | , , |
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| Formáid: | Online |
| Teanga: | Béarla |
| Foilsithe / Cruthaithe: |
The MIT Press
2024
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| Ábhair: | |
| Rochtain ar líne: | ONIX_20241025_9780262380355_156 |
| Clibeanna: |
Níl clibeanna ann, Bí ar an gcéad duine le clib a chur leis an taifead seo!
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| _version_ | 1869531389309747200 |
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| author | McShane, Marjorie Nirenburg, Sergei English, Jesse |
| author_browse | English, Jesse McShane, Marjorie Nirenburg, Sergei |
| author_facet | McShane, Marjorie Nirenburg, Sergei English, Jesse |
| author_sort | McShane, Marjorie |
| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-146778 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | The MIT Press |
| publisherStr | The MIT Press |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1467782024-10-25T13:20:40Z Agents in the Long Game of AI McShane, Marjorie Nirenburg, Sergei English, Jesse Cognitive Sciences/General Computer Science/General thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning 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. 2024-10-25T13:20:38Z 2024-10-25T13:20:38Z 2024 book ONIX_20241025_9780262380355_156 9780262380355 9780262549424 https://directory.doabooks.org/handle/20.500.12854/146778 eng The MIT Press image/jpeg n/a https://doi.org/10.7551/mitpress/14940.001.0001 The MIT Press The MIT Press 10.7551/mitpress/14940.001.0001 10.7551/mitpress/14940.001.0001 ae0cf962-f685-4933-93d1-916defa5123d 9780262380355 9780262549424 The MIT Press 336 Cambridge open access |
| spellingShingle | Cognitive Sciences/General Computer Science/General thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning McShane, Marjorie Nirenburg, Sergei English, Jesse Agents in the Long Game of AI |
| title | Agents in the Long Game of AI |
| title_full | Agents in the Long Game of AI |
| title_fullStr | Agents in the Long Game of AI |
| title_full_unstemmed | Agents in the Long Game of AI |
| title_short | Agents in the Long Game of AI |
| title_sort | agents in the long game of ai |
| topic | Cognitive Sciences/General Computer Science/General thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning |
| topic_facet | Cognitive Sciences/General Computer Science/General thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning |
| url | ONIX_20241025_9780262380355_156 |
| work_keys_str_mv | AT mcshanemarjorie agentsinthelonggameofai AT nirenburgsergei agentsinthelonggameofai AT englishjesse agentsinthelonggameofai |