Do Large Language Models Understand Us

Bibliographic Data

ID9685095
AuthorsBlaise Agüera y Arcas (0000-0003-2256-9823, corresponding author)
Year2022
Volume151
Issue2
Pages183-197
Publication date2022-05-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueDaedalus (JOURNAL)
Journal identifiersISSN: 0011-5266 • E-ISSN: 1548-6192
PublisherMIT Press (PUBLISHER • US)
DOI10.1162/daed_a_01909
OpenAlexW4225479391
LanguageDE
Citations received20

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

Cognitive science · Consciousness · Dialogic · Epistemology · Falsifiability · Human intelligence · Social intelligence · Sociality · Sociology · Artificial Intelligence · Computational Physics and Python Applications · Computer Science · Ecology · Philosophy · Psychology · Social Psychology · Topic Modeling

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