How Is AI Changing the Science of Prediction?
With lots of data, a strong model and statistical thinking, scientists can make predictions about all sorts of complex phenomena. Today, this practice is evolving to harness the power of machine learning and massive datasets. In this episode, co-host Steven Strogatz speaks with statistician Emmanuel Candès about black boxes, uncertainty and the power of inductive reasoning.
The Joy of Why · Steven Strogatz, Janna Levin and Quanta Magazine
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Show Notes
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.