In this engaging conversation, Dr. Emily M. Bender, a Linguistics Professor at the University of Washington and co-host of Mystery AI Hype Theater 3000, explores how computers decode language. She reveals the difference between human and computer language learning, discussing the humorous failures that can arise. Emily emphasizes the significance of community over mere data, the ethical implications of AI training, and the creation of the Bender Rule, which promotes clarity in language use. Expect intriguing insights into the quirks of AI behavior and the future of linguistic technology!
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question_answer ANECDOTE
From Startup Job To Grammar Matrix
Emily Bender described building a Japanese grammar at a startup to automate customer service responses.
That work led her to create the Grammar Matrix starter kit to help build grammars for many languages.
insights INSIGHT
Language Is Many Separate Problems
Solving “language” is vague because language involves many distinct skills like sound, syntax, semantics, and pragmatics.
True human-like understanding requires far more than linguistics alone, including multimodal and pragmatic reasoning.
volunteer_activism ADVICE
Work With Communities First
Engage with communities first and ask what computational tools they want before building systems for their languages.
Train local community members as computational linguists rather than extracting their data without consent.
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When a human learns a new word, we're learning to attach that word to a set of concepts in the real world. When a computer "learns" a new word, it is creating some associations between that word and other words it has seen before, which can sometimes give it the appearance of understanding, but it doesn't have that real-world grounding, which can sometimes lead to spectacular failures: hilariously implausible from a human perspective, just as plausible from the computer's.
In this episode, your host Lauren Gawne gets enthusiastic about how computers process language with Dr. Emily M. Bender, who is a linguistics professor at the University of Washington, USA, and cohost of the podcast Mystery AI Hype Theater 3000. We talk about Emily's work trying to formulate a list of rules that a computer can use to generate grammatical sentences in a language, the differences between that and training a computer to generate sentences using the statistical likelihood of what comes next based on all the other sentences, and the further differences between both those things and how humans map language onto the real world. We also talk about paying attention to communities not just data, the labour practices behind large language models, and how Emily's persistent questions led to the creation of the Bender Rule (always state the language you're working on, even if it's English).
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Read the transcript here: lingthusiasm.com/post/767803835730231296/transcript-episode-98
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