The Road to General Intelligence
Humans have always dreamed of automating laborious physical and intellectual tasks, but the latter has proved more elusive than naively suspected. Seven decades of systematic study of Artificial Intelligence have witnessed cycles of hubris and despair. The successful realization of General Intellige...
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| Главные авторы: | , , , , , |
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| Формат: | Online |
| Язык: | английский |
| Опубликовано: |
Springer Nature
2022
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| Предметы: | |
| Online-ссылка: | ONIX_20220713_9783031080203_55 |
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| _version_ | 1869531595409457152 |
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| author | Swan, Jerry Nivel, Eric Kant, Neel Hedges, Jules Atkinson, Timothy Steunebrink, Bas |
| author_browse | Atkinson, Timothy Hedges, Jules Kant, Neel Nivel, Eric Steunebrink, Bas Swan, Jerry |
| author_facet | Swan, Jerry Nivel, Eric Kant, Neel Hedges, Jules Atkinson, Timothy Steunebrink, Bas |
| author_sort | Swan, Jerry |
| collection | Directory of Open Access Books |
| description | Humans have always dreamed of automating laborious physical and intellectual tasks, but the latter has proved more elusive than naively suspected. Seven decades of systematic study of Artificial Intelligence have witnessed cycles of hubris and despair. The successful realization of General Intelligence (evidenced by the kind of cross-domain flexibility enjoyed by humans) will spawn an industry worth billions and transform the range of viable automation tasks.The recent notable successes of Machine Learning has lead to conjecture that it might be the appropriate technology for delivering General Intelligence. In this book, we argue that the framework of machine learning is fundamentally at odds with any reasonable notion of intelligence and that essential insights from previous decades of AI research are being forgotten. We claim that a fundamental change in perspective is required, mirroring that which took place in the philosophy of science in the mid 20th century. We propose a framework for General Intelligence, together with a reference architecture that emphasizes the need for anytime bounded rationality and a situated denotational semantics. We given necessary emphasis to compositional reasoning, with the required compositionality being provided via principled symbolic-numeric inference mechanisms based on universal constructions from category theory. • Details the pragmatic requirements for real-world General Intelligence. • Describes how machine learning fails to meet these requirements. • Provides a philosophical basis for the proposed approach. • Provides mathematical detail for a reference architecture. • Describes a research program intended to address issues of concern in contemporary AI. The book includes an extensive bibliography, with ~400 entries covering the history of AI and many related areas of computer science and mathematics.The target audience is the entire gamut of Artificial Intelligence/Machine Learning researchers and industrial practitioners. There are a mixture of descriptive and rigorous sections, according to the nature of the topic. Undergraduate mathematics is in general sufficient. Familiarity with category theory is advantageous for a complete understanding of the more advanced sections, but these may be skipped by the reader who desires an overall picture of the essential concepts This is an open access book. |
| format | Online |
| id | doab-20.500.12854ir-87730 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Springer Nature |
| publisherStr | Springer Nature |
| record_format | ojs |
| spelling | doab-20.500.12854ir-877302025-03-16T15:13:17Z The Road to General Intelligence Swan, Jerry Nivel, Eric Kant, Neel Hedges, Jules Atkinson, Timothy Steunebrink, Bas Computational Intelligence Artificial Intelligence Machine Learning General Intelligence AI Humans have always dreamed of automating laborious physical and intellectual tasks, but the latter has proved more elusive than naively suspected. Seven decades of systematic study of Artificial Intelligence have witnessed cycles of hubris and despair. The successful realization of General Intelligence (evidenced by the kind of cross-domain flexibility enjoyed by humans) will spawn an industry worth billions and transform the range of viable automation tasks.The recent notable successes of Machine Learning has lead to conjecture that it might be the appropriate technology for delivering General Intelligence. In this book, we argue that the framework of machine learning is fundamentally at odds with any reasonable notion of intelligence and that essential insights from previous decades of AI research are being forgotten. We claim that a fundamental change in perspective is required, mirroring that which took place in the philosophy of science in the mid 20th century. We propose a framework for General Intelligence, together with a reference architecture that emphasizes the need for anytime bounded rationality and a situated denotational semantics. We given necessary emphasis to compositional reasoning, with the required compositionality being provided via principled symbolic-numeric inference mechanisms based on universal constructions from category theory. • Details the pragmatic requirements for real-world General Intelligence. • Describes how machine learning fails to meet these requirements. • Provides a philosophical basis for the proposed approach. • Provides mathematical detail for a reference architecture. • Describes a research program intended to address issues of concern in contemporary AI. The book includes an extensive bibliography, with ~400 entries covering the history of AI and many related areas of computer science and mathematics.The target audience is the entire gamut of Artificial Intelligence/Machine Learning researchers and industrial practitioners. There are a mixture of descriptive and rigorous sections, according to the nature of the topic. Undergraduate mathematics is in general sufficient. Familiarity with category theory is advantageous for a complete understanding of the more advanced sections, but these may be skipped by the reader who desires an overall picture of the essential concepts This is an open access book. 2022-07-14T04:03:53Z 2022-07-14T04:03:53Z 2022-07-13T12:28:16Z 2022 book ONIX_20220713_9783031080203_55 OCN: 1333445371 https://library.oapen.org/handle/20.500.12657/57384 9783031080203 https://directory.doabooks.org/handle/20.500.12854/87730 eng Studies in Computational Intelligence open access image/jpeg image/jpeg image/jpeg n/a n/a n/a https://library.oapen.org/bitstream/20.500.12657/57384/1/978-3-031-08020-3.pdf https://library.oapen.org/bitstream/20.500.12657/57384/1/978-3-031-08020-3.pdf https://library.oapen.org/bitstream/20.500.12657/57384/1/978-3-031-08020-3.pdf Springer Nature Springer International Publishing 10.1007/978-3-031-08020-3 10.1007/978-3-031-08020-3 9fa3421d-f917-4153-b9ab-fc337c396b5a 9783031080203 Springer International Publishing 136 Cham open access |
| spellingShingle | Computational Intelligence Artificial Intelligence Machine Learning General Intelligence AI Swan, Jerry Nivel, Eric Kant, Neel Hedges, Jules Atkinson, Timothy Steunebrink, Bas The Road to General Intelligence |
| title | The Road to General Intelligence |
| title_full | The Road to General Intelligence |
| title_fullStr | The Road to General Intelligence |
| title_full_unstemmed | The Road to General Intelligence |
| title_short | The Road to General Intelligence |
| title_sort | road to general intelligence |
| topic | Computational Intelligence Artificial Intelligence Machine Learning General Intelligence AI |
| topic_facet | Computational Intelligence Artificial Intelligence Machine Learning General Intelligence AI |
| url | ONIX_20220713_9783031080203_55 |
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