Sharing our concepts with machines
As AI systems become increasingly competent language users, it is an apt moment to consider what it would take for machines to understand human languages. This paper considers whether either language models such as GPT-3 or chatbots might be able to understand language, focusing on the question of w...
| Main Author: | |
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| Format: | Journal article |
| Language: | English |
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Springer
2021
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| _version_ | 1797111050231873536 |
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| author | Butlin, P |
| author_facet | Butlin, P |
| author_sort | Butlin, P |
| collection | OXFORD |
| description | As AI systems become increasingly competent language users, it is an apt moment to consider what it would take for machines to understand human languages. This paper considers whether either language models such as GPT-3 or chatbots might be able to understand language, focusing on the question of whether they could possess the relevant concepts. A significant obstacle is that systems of both kinds interact with the world only through text, and thus seem ill-suited to understanding utterances concerning the concrete objects and properties which human language often describes. Language models cannot understand human languages because they perform only linguistic tasks, and therefore cannot represent such objects and properties. However, chatbots may perform tasks concerning the non-linguistic world, so they are better candidates for understanding. Chatbots can also possess the concepts necessary to understand human languages, despite their lack of perceptual contact with the world, due to the language-mediated concept-sharing described by social externalism about mental content. |
| first_indexed | 2024-03-07T08:04:48Z |
| format | Journal article |
| id | oxford-uuid:365f0d84-49ab-42cb-b13c-fbbff487aef6 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T08:04:48Z |
| publishDate | 2021 |
| publisher | Springer |
| record_format | dspace |
| spelling | oxford-uuid:365f0d84-49ab-42cb-b13c-fbbff487aef62023-10-24T09:13:44ZSharing our concepts with machinesJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:365f0d84-49ab-42cb-b13c-fbbff487aef6EnglishSymplectic ElementsSpringer2021Butlin, PAs AI systems become increasingly competent language users, it is an apt moment to consider what it would take for machines to understand human languages. This paper considers whether either language models such as GPT-3 or chatbots might be able to understand language, focusing on the question of whether they could possess the relevant concepts. A significant obstacle is that systems of both kinds interact with the world only through text, and thus seem ill-suited to understanding utterances concerning the concrete objects and properties which human language often describes. Language models cannot understand human languages because they perform only linguistic tasks, and therefore cannot represent such objects and properties. However, chatbots may perform tasks concerning the non-linguistic world, so they are better candidates for understanding. Chatbots can also possess the concepts necessary to understand human languages, despite their lack of perceptual contact with the world, due to the language-mediated concept-sharing described by social externalism about mental content. |
| spellingShingle | Butlin, P Sharing our concepts with machines |
| title | Sharing our concepts with machines |
| title_full | Sharing our concepts with machines |
| title_fullStr | Sharing our concepts with machines |
| title_full_unstemmed | Sharing our concepts with machines |
| title_short | Sharing our concepts with machines |
| title_sort | sharing our concepts with machines |
| work_keys_str_mv | AT butlinp sharingourconceptswithmachines |