Rapid Changes in AI & Word Representations

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The chapter discusses the evolving AI landscape and the belief in sentient language models, as well as the impact of word vectors on language understanding. It explores the advantages of using vectors for analogical reasoning and the limitations of chat GPT in handling higher-level analogies.

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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