Linguistics for the Age of AI
A human-inspired, linguistically sophisticated model of language understanding for intelligent agent systems. One of the original goals of artificial intelligence research was to endow intelligent agents with human-level natural language capabilities. Recent AI research, however, has focused on appl...
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| Главные авторы: | , |
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| Формат: | Online |
| Язык: | английский |
| Опубликовано: |
The MIT Press
2022
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| Предметы: | |
| Online-ссылка: | ONIX_20220221_9780262363136_128 |
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Нет меток, Требуется 1-ая метка записи!
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| _version_ | 1869521358660042752 |
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| author | McShane, Marjorie Nirenburg, Sergei |
| author_browse | McShane, Marjorie Nirenburg, Sergei |
| author_facet | McShane, Marjorie Nirenburg, Sergei |
| author_sort | McShane, Marjorie |
| collection | Directory of Open Access Books |
| description | A human-inspired, linguistically sophisticated model of language understanding for intelligent agent systems. One of the original goals of artificial intelligence research was to endow intelligent agents with human-level natural language capabilities. Recent AI research, however, has focused on applying statistical and machine learning approaches to big data rather than attempting to model what people do and how they do it. In this book, Marjorie McShane and Sergei Nirenburg return to the original goal of recreating human-level intelligence in a machine. They present a human-inspired, linguistically sophisticated model of language understanding for intelligent agent systems that emphasizes meaning—the deep, context-sensitive meaning that a person derives from spoken or written language. With Linguistics for the Age of AI, McShane and Nirenburg offer a roadmap for creating language-endowed intelligent agents (LEIAs) that can understand,explain, and learn. They describe the language-understanding capabilities of LEIAs from the perspectives of cognitive modeling and system building, emphasizing “actionability”—which involves achieving interpretations that are sufficiently deep, precise, and confident to support reasoning about action. After detailing their microtheories for topics such as semantic analysis, basic coreference, and situational reasoning, McShane and Nirenburg turn to agent applications developed using those microtheories and evaluations of a LEIA's language understanding capabilities. McShane and Nirenburg argue that the only way to achieve human-level language understanding by machines is to place linguistics front and center, using statistics and big data as contributing resources. They lay out a long-term research program that addresses linguistics and real-world reasoning together, within a comprehensive cognitive architecture. |
| format | Online |
| id | doab-20.500.12854ir-78608 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | The MIT Press |
| publisherStr | The MIT Press |
| record_format | ojs |
| spelling | doab-20.500.12854ir-786082024-04-14T10:28:26Z Linguistics for the Age of AI McShane, Marjorie Nirenburg, Sergei natural language understanding computational semantics computational pragmatics computational linguistics intelligent agents cognitive modelling cognitive systems AI artificial intelligence language-endowed intelligent agents natural language processing NLP language-endowed intelligent agent systems linguistic and extralinguistic scope understanding Extracting and representing meaning theories systems and models actionability explanation Theory and methodology knowledge bases incrementality microtheories Pre-semantic analysis error recovery managing complexity Modification proposition-level semantic enhancements constructions indirect speech acts non-literal language ellipsis fragments unknown words personal pronouns broad referring expressions definite descriptions anaphoric event coreference Residual ambiguities incongruities underspecification incorporating OntoAgent cognitive architecture fractured syntax treating underspecified elements Integrated NLU applications Maryland Virtual Patient cognitive robotics Model and system evaluation component-level evaluation holistic evaluation thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning thema EDItEUR::C Language and Linguistics::CF Linguistics::CFM Lexicography thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GT Interdisciplinary studies::GTK Cognitive studies A human-inspired, linguistically sophisticated model of language understanding for intelligent agent systems. One of the original goals of artificial intelligence research was to endow intelligent agents with human-level natural language capabilities. Recent AI research, however, has focused on applying statistical and machine learning approaches to big data rather than attempting to model what people do and how they do it. In this book, Marjorie McShane and Sergei Nirenburg return to the original goal of recreating human-level intelligence in a machine. They present a human-inspired, linguistically sophisticated model of language understanding for intelligent agent systems that emphasizes meaning—the deep, context-sensitive meaning that a person derives from spoken or written language. With Linguistics for the Age of AI, McShane and Nirenburg offer a roadmap for creating language-endowed intelligent agents (LEIAs) that can understand,explain, and learn. They describe the language-understanding capabilities of LEIAs from the perspectives of cognitive modeling and system building, emphasizing “actionability”—which involves achieving interpretations that are sufficiently deep, precise, and confident to support reasoning about action. After detailing their microtheories for topics such as semantic analysis, basic coreference, and situational reasoning, McShane and Nirenburg turn to agent applications developed using those microtheories and evaluations of a LEIA's language understanding capabilities. McShane and Nirenburg argue that the only way to achieve human-level language understanding by machines is to place linguistics front and center, using statistics and big data as contributing resources. They lay out a long-term research program that addresses linguistics and real-world reasoning together, within a comprehensive cognitive architecture. 2022-02-21T15:13:20Z 2022-02-21T15:13:20Z 2021 book ONIX_20220221_9780262363136_128 9780262363136 9780262045582 https://directory.doabooks.org/handle/20.500.12854/78608 eng The MIT Press image/jpeg n/a https://doi.org/10.7551/mitpress/13618.001.0001 The MIT Press The MIT Press ae0cf962-f685-4933-93d1-916defa5123d 9780262363136 9780262045582 The MIT Press 448 Cambridge open access |
| spellingShingle | natural language understanding computational semantics computational pragmatics computational linguistics intelligent agents cognitive modelling cognitive systems AI artificial intelligence language-endowed intelligent agents natural language processing NLP language-endowed intelligent agent systems linguistic and extralinguistic scope understanding Extracting and representing meaning theories systems and models actionability explanation Theory and methodology knowledge bases incrementality microtheories Pre-semantic analysis error recovery managing complexity Modification proposition-level semantic enhancements constructions indirect speech acts non-literal language ellipsis fragments unknown words personal pronouns broad referring expressions definite descriptions anaphoric event coreference Residual ambiguities incongruities underspecification incorporating OntoAgent cognitive architecture fractured syntax treating underspecified elements Integrated NLU applications Maryland Virtual Patient cognitive robotics Model and system evaluation component-level evaluation holistic evaluation thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning thema EDItEUR::C Language and Linguistics::CF Linguistics::CFM Lexicography thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GT Interdisciplinary studies::GTK Cognitive studies McShane, Marjorie Nirenburg, Sergei Linguistics for the Age of AI |
| title | Linguistics for the Age of AI |
| title_full | Linguistics for the Age of AI |
| title_fullStr | Linguistics for the Age of AI |
| title_full_unstemmed | Linguistics for the Age of AI |
| title_short | Linguistics for the Age of AI |
| title_sort | linguistics for the age of ai |
| topic | natural language understanding computational semantics computational pragmatics computational linguistics intelligent agents cognitive modelling cognitive systems AI artificial intelligence language-endowed intelligent agents natural language processing NLP language-endowed intelligent agent systems linguistic and extralinguistic scope understanding Extracting and representing meaning theories systems and models actionability explanation Theory and methodology knowledge bases incrementality microtheories Pre-semantic analysis error recovery managing complexity Modification proposition-level semantic enhancements constructions indirect speech acts non-literal language ellipsis fragments unknown words personal pronouns broad referring expressions definite descriptions anaphoric event coreference Residual ambiguities incongruities underspecification incorporating OntoAgent cognitive architecture fractured syntax treating underspecified elements Integrated NLU applications Maryland Virtual Patient cognitive robotics Model and system evaluation component-level evaluation holistic evaluation thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning thema EDItEUR::C Language and Linguistics::CF Linguistics::CFM Lexicography thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GT Interdisciplinary studies::GTK Cognitive studies |
| topic_facet | natural language understanding computational semantics computational pragmatics computational linguistics intelligent agents cognitive modelling cognitive systems AI artificial intelligence language-endowed intelligent agents natural language processing NLP language-endowed intelligent agent systems linguistic and extralinguistic scope understanding Extracting and representing meaning theories systems and models actionability explanation Theory and methodology knowledge bases incrementality microtheories Pre-semantic analysis error recovery managing complexity Modification proposition-level semantic enhancements constructions indirect speech acts non-literal language ellipsis fragments unknown words personal pronouns broad referring expressions definite descriptions anaphoric event coreference Residual ambiguities incongruities underspecification incorporating OntoAgent cognitive architecture fractured syntax treating underspecified elements Integrated NLU applications Maryland Virtual Patient cognitive robotics Model and system evaluation component-level evaluation holistic evaluation thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning thema EDItEUR::C Language and Linguistics::CF Linguistics::CFM Lexicography thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GT Interdisciplinary studies::GTK Cognitive studies |
| url | ONIX_20220221_9780262363136_128 |
| work_keys_str_mv | AT mcshanemarjorie linguisticsfortheageofai AT nirenburgsergei linguisticsfortheageofai |