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AI+Science: Accelerating Discovery is an interdisciplinary conference bringing together researchers across physics, mathematics, chemistry, biology, neuroscience, and more to examine how AI is reshaping scientific discovery. Experts will separate hype from reality, spotlighting where AI is already enabling genuine breakthroughs and where its limits and risks remain.

AI+Science: Accelerating Discovery is an interdisciplinary conference bringing together researchers across physics, mathematics, chemistry, biology, neuroscience, and more to examine how AI is reshaping scientific discovery. Experts will separate hype from reality, spotlighting where AI is already enabling genuine breakthroughs and where its limits and risks remain.
Despite the rapid adoption of LLM chatbots, little is known about how they are used. We approach this question theoretically and empirically, modeling a user who chooses whether to complete a task herself, ask the chatbot for information that reduces decision noise, or delegate execution to the chatbot...
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Despite the rapid adoption of LLM chatbots, little is known about how they are used. We approach this question theoretically and empirically, modeling a user who chooses whether to complete a task herself, ask the chatbot for information that reduces decision noise, or delegate execution to the chatbot...
The question of whether machines can really understand. Lovelace and Searle thought not. Turing thought yes. (Ok that’s not totally accurate, but let’s not ruin a good story.) With the advent of LLMs the question has resurfaced in force, again with some strong skeptics such as Bender et al. In this seminar, Yoav Shoham shares his views, based mostly on work at AI21 Labs. Spoilers: (1) He’s with Turing. (2) LLMs, as currently built, are necessary but not sufficient. (3) The question is more interesting than the answer.