The Challenges of Accuracy and Reliance

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The speakers explore the challenges of determining the accuracy of an AI language model's responses and highlight a recent news headline about a lawyer relying on the model's response for fact-checking. They discuss the potential consequences and limitations of relying too heavily on large language models like GPT.

Over the last year, AI large-language models (LLMs) like ChatGPT have demonstrated a remarkable ability to carry on human-like conversations in a variety of different concepts. But the way these LLMs "learn" is very different from how human beings learn, and the same can be said for how they "reason." It's reasonable to ask, do these AI programs really understand the world they are talking about? Do they possess a common-sense picture of reality, or can they just string together words in convincing ways without any underlying understanding? Computer scientist Yejin Choi is a leader in trying to understand the sense in which AIs are actually intelligent, and why in some ways they're still shockingly stupid.

Blog post with transcript: https://www.preposterousuniverse.com/podcast/2023/08/28/248-yejin-choi-on-ai-and-common-sense/

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Yejin Choi received a Ph.D. in computer science from Cornell University. She is currently the Wissner-Slivka Professor at the Paul G. Allen School of Computer Science & Engineering at the University of Washington and also a senior research director at AI2 overseeing the project Mosaic. Among her awards are a MacArthur fellowship and a fellow of the Association for Computational Linguistics.

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