45 min
#15 James Zou: Collaborating with AI Future of Science by DeSci Foundation
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- Sciences
In this episode, we talked to Professor James Zou, who brought us his perspective on how academia might collaborate with AI. He covered how AI could help us ask better questions instead of answering them, how they can translate information for different levels of expertise, and how we can use them to make our feedback more diverse and specific instead of general. After that, we explored how AI is already changing science by increasing the number of papers, creating more general GPT-generated reviews, and also making writing a more accessible task for incoming PhDs.
To top it off, we all agreed that we would absolutely go to an AI-generated concert (especially if there were robots).
James Zou is an Associate Professor of Biomedical Data Science and, by courtesy, of Computer Science and Electrical Engineering at Stanford University. He works on making machine learning more reliable, human-compatible and statistically rigorous, and is especially interested in applications in human disease and health. Several of his algorithms are widely used in tech and biotech industries.
If you enjoyed this episode, check out our other seminars here.
In this episode, we talked to Professor James Zou, who brought us his perspective on how academia might collaborate with AI. He covered how AI could help us ask better questions instead of answering them, how they can translate information for different levels of expertise, and how we can use them to make our feedback more diverse and specific instead of general. After that, we explored how AI is already changing science by increasing the number of papers, creating more general GPT-generated reviews, and also making writing a more accessible task for incoming PhDs.
To top it off, we all agreed that we would absolutely go to an AI-generated concert (especially if there were robots).
James Zou is an Associate Professor of Biomedical Data Science and, by courtesy, of Computer Science and Electrical Engineering at Stanford University. He works on making machine learning more reliable, human-compatible and statistically rigorous, and is especially interested in applications in human disease and health. Several of his algorithms are widely used in tech and biotech industries.
If you enjoyed this episode, check out our other seminars here.
45 min