Scientists routinely build quantitative models — of, say, the weather or an epidemic — and then use them to make predictions, which they can then test against the real thing. This work can reveal how well we understand complex phenomena, and also dictate where research should go next. In recent years, the remarkable successes of “black box” systems such as large language models suggest that it is sometimes possible to make successful predictions without knowing how something works at all. In this episode, noted statistician Emmanuel Candès and host Steven Strogatz discuss using statistics, data science and AI in the study of everything from college admissions to election forecasting to drug discovery. 

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