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).
Click here for a link to this episode in your podcast player of choice: episodes.fm/1186056137/episode/dGFnOnNvdW5kY2xvdWQsMjAxMDp0cmFja3MvMTk2NDIxOTY5OQ
Read the transcript here: lingthusiasm.com/post/767803835730231296/transcript-episode-98
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For links to things mentioned in this episode: lingthusiasm.com/post/767803572750581760/lingthusiasm-episode-98-helping-computers-decode
