In this episode of "The Joy of Why, " host Steven Strogatz discusses the application of statistics, data science, and AI with statistician Emmanuel Candès. They explore the use of quantitative models to predict complex phenomena like weather, epidemics, college admissions, election forecasts, and drug discovery, revealing both successes and limitations. They delve into the concept of "black box" models used in machine learning, describing their ability to predict without understanding the underlying processes.
Candès explains how statistical techniques can quantify uncertainty and improve predictions, offering real-world examples such as college admissions, election night forecasting, and drug discovery. The conversation touches on pressing issues like the reproducibility crisis in scientific research, emphasizing the need for statistical safeguards. The episode concludes with a discussion about the role of statistics within the broader field of data science and the importance of teaching statistical reasoning as part of education for fostering an understanding of inductive reasoning.
The Joy of Why: Exploring Statistics and AI with Emmanuel Candès
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