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Razib Khan's Unsupervised Learning

Jan 31, 2024

Today, Razib talks to Seth Stephens-Davidowitz, author of Who Makes the NBA?: Data-Driven Answers to Basketball's Biggest Questions and Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are. Stephens-Davidowitz, formerly of Google and The New York Times, is a freelance data scientist and author. He has a degree in philosophy from Stanford and a PhD in economics from Harvard. In this episode, he discusses his process of writing Who Makes the NBA?, which he crafted in a month using ChatGPT’s code interpreter feature, and the biggest insights from his book.

Razib probes Stephens-Davidowitz on the relationship between height and athletic ability, and why success in the NBA has the largest heritable component of any major league sport. They also discuss the finding that children of NBA players enjoy a non-genetic advantage in basketball, and why those who make it into the league and succeed are from higher socioeconomic strata. Stephens-Davidowitz also discusses why international basketball is popular in the former Yugoslavia and Lithuania, and how the popularity of volleyball in Iran and Brazil affects the pipeline of talent from those nations.

The episode concludes with the author’s detailed thoughts about what it was like to write a book assisted by AI, and the feasibility of this sort of content creation over the next decade. Razib and Stephens-Davidowitz discuss the possibility of massive productivity gains from AI over the next few years and the long-term feasibility of writing careers if AI keeps improving at the current rate. Finally, Stephens-Davidowitz lays out his plan to write his next few years’ of books at a far faster clip, relying on AI assistance..

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