Ted Gibson: The Structure and Purpose of Language

Ted Gibson: The Structure and Purpose of Language

Author: Daniel Bashir January 18, 2024 Duration: 2:13:24

In episode 107 of The Gradient Podcast, Daniel Bashir speaks to Professor Ted Gibson.

Ted is a Professor of Cognitive Science at MIT. He leads the TedLab, which investigates why languages look the way they do; the relationship between culture and cognition, including language; and how people learn, represent, and process language.

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Outline:

* (00:00) Intro

* (02:13) Prof Gibson’s background

* (05:33) The computational linguistics community and NLP, engineering focus

* (10:48) Models of brains

* (12:03) Prof Gibson’s focus on behavioral work

* (12:53) How dependency distances impact language processing

* (14:03) Dependency distances and the origin of the problem

* (18:53) Dependency locality theory

* (21:38) The structures languages tend to use

* (24:58) Sentence parsing: structural integrations and memory costs

* (36:53) Reading strategies vs. ordinary language processing

* (40:23) Legalese

* (46:18) Cross-dependencies

* (50:11) Number as a cognitive technology

* (54:48) Experiments

* (1:03:53) Why counting is useful for Western societies

* (1:05:53) The Whorf hypothesis

* (1:13:05) Language as Communication

* (1:13:28) The noisy channel perspective on language processing

* (1:27:08) Fedorenko lab experiments—language for thought vs. communication and Chomsky’s claims

* (1:43:53) Thinking without language, inner voices, language processing vs. language as an aid for other mental processing

* (1:53:01) Dependency grammars and a critique of Chomsky’s grammar proposals, LLMs

* (2:08:48) LLM behavior and internal representations

* (2:12:53) Outro

Links:

* Ted’s lab page and Twitter

* Re-imagining our theories of language

* Research — linguistic complexity and dependency locality theory

* Linguistic complexity: locality of syntactic dependencies (1998)

* The Dependency Locality Theory: A Distance-Based Theory of Linguistic Complexity (2000)

* Consequences of the Serial Nature of Linguistic Input for Sentential Complexity (2005)

* Large-scale evidence of dependency length minimization in 37 languages (2015)

* Dependency locality as an explanatory principle for word order (2020)

* Robust effects of working memory demand during naturalistic language comprehension in language-selective cortex (2022)

* A resource-rational model of human processing of recursive linguistic structure (2022)

* Research — language processing / communication and cross-linguistic universals

* Number as a cognitive technology: Evidence from Pirahã language and cognition (2008)

* The communicative function of ambiguity in language (2012)

* The rational integration of noisy evidence and prior semantic expectations in sentence interpretation (2013)

* Color naming across languages reflects color use (2017)

* How Efficiency Shapes Human Language (2019)



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Hosted by Daniel Bashir, The Gradient: Perspectives on AI moves beyond surface-level headlines to explore the intricate machinery and human ideas shaping artificial intelligence. Each episode is built on a foundation of deep research, leading to conversations that are both technically substantive and broadly accessible. You'll hear from researchers, engineers, and philosophers who are actively building and critiquing our technological future, discussing not just how AI systems work, but the larger implications of their integration into society. This isn't about speculative hype; it's a grounded examination of real progress, persistent challenges, and ethical considerations from those on the front lines. The discussions peel back layers on topics like model architecture, policy, and the fundamental science behind the algorithms becoming part of our daily lives. For anyone curious about the substance behind the buzz-whether you have a technical background or are simply keen to understand a defining technology of our age-this podcast offers a crucial and thoughtful resource. Tune in for a consistently detailed and nuanced take that treats artificial intelligence with the complexity it deserves.
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