Do Large Language Models Understand Us?
In: Daedalus: Journal of the American Academy of Arts & Sciences, Jg. 151 (2022-04-01), Heft 2, S. 183-197
Online
academicJournal
Zugriff:
Large language models (LLMs) represent a major advance in artificial intelligence and, in particular, toward the goal of human-like artificial general intelligence. It is sometimes claimed, though, that machine learning is "just statistics," hence that, in this grander ambition, progress in AI is illusory. Here I take the contrary view that LLMs have a great deal to teach us about the nature of language, understanding, intelligence, sociality, and personhood. Specifically: statistics do amount to understanding, in any falsifiable sense. Furthermore, much of what we consider intelligence is inherently dialogic, hence social; it requires a theory of mind. Complex sequence learning and social interaction may be a sufficient basis for general intelligence, including theory of mind and consciousness. Since the interior state of another being can only be understood through interaction, no objective answer is possible to the question of when an "it" becomes a "who," but for many people, neural nets running on computers are likely to cross this threshold in the very near future. [ABSTRACT FROM AUTHOR]
Copyright of Daedalus: Journal of the American Academy of Arts & Sciences is the property of MIT Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Titel: |
Do Large Language Models Understand Us?
|
---|---|
Autor/in / Beteiligte Person: | y Arcas, Blaise Agüera |
Link: | |
Zeitschrift: | Daedalus: Journal of the American Academy of Arts & Sciences, Jg. 151 (2022-04-01), Heft 2, S. 183-197 |
Veröffentlichung: | 2022 |
Medientyp: | academicJournal |
ISSN: | 0011-5266 (print) |
DOI: | 10.1162/daed_a_01909 |
Schlagwort: |
|
Sonstiges: |
|