How Is AI Changing the Science of Prediction?
The Joy of Why
Steven Strogatz, Janna Levin and Quanta Magazine
4.9 • 577 Ratings
🗓️ 7 November 2024
⏱️ 37 minutes
🧾️ Download transcript
Summary
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.
Transcript
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| 0:00.0 | Making predictions is a challenge woven into every part of our lives, often in ways we don't even think about. Will it rain this afternoon? How will the stock market respond to the latest news? |
| 0:21.4 | What would mom like for her birthday? |
| 0:24.4 | Typically, we build up a knowledge base and a theoretical understanding, at least in science, |
| 0:29.4 | and apply what we know to predict future outcomes. |
| 0:32.7 | But that approach faces sharp limitations, especially when the systems, to be analyzed, are profoundly complex |
| 0:39.3 | and poorly understood. |
| 0:41.3 | I'm Steve Strogatz, and this is The Joy of Why, a podcast from Quantum Magazine, where |
| 0:46.8 | I take turns at the mic with my co-host, Jan 11, exploring the biggest unanswered questions |
| 0:52.7 | in math and science today. |
| 1:00.3 | For this episode, we're joined by mathematician and statistician Emmanuel Kandas to ask, |
| 1:07.1 | how are data science and machine learning helping us approach complex prediction problems like never before? |
| 1:13.6 | And how confident or skeptical should we be in their predictions? Can we figure out ways to quantify that uncertainty? Emmanuel Kandes is a chair and professor of mathematics and statistics |
| 1:20.6 | at Stanford University. His work lies at the interface of math, statistics, information theory, |
| 1:26.6 | signal processing, and scientific computing. |
| 1:30.2 | He's a member of the U.S. National Academy of Sciences and has received a MacArthur Fellowship, |
| 1:35.9 | a Colotte Prize, and a Lagrange Prize. |
| 1:38.8 | Emmanuel, welcome to the joy of why. |
| 1:41.2 | Thank you very much for having me, And since you mentioned the National Academy, |
| 1:45.8 | let me start by congratulating you on your election. This is truly awesome. Oh, you're too kind. |
| 1:50.8 | Thank you. I'm honored to be joining you and all of our other esteemed colleagues. Well, so let us |
| 1:56.1 | begin here by talking about something on the mind of just about everybody nowadays, machine learning models, |
| 2:01.7 | we keep hearing so much about them. |
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