The Joy of Why How Is AI Changing the Science of Prediction?
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Nov 7, 2024 
Emmanuel Candès, Chair of Mathematics and Statistics at Stanford, dives into the transformative role AI plays in making predictions across various fields. He discusses how traditional models like weather forecasts are evolving, embracing 'black box' systems for significant accuracy gains. Topics include the integration of AI in everything from college admissions to drug discovery and the challenges of interpreting machine learning outputs. Candès also highlights the crucial importance of teaching statistical reasoning for better understanding and reliability in predictive analytics.
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Black Box Models
- Machine learning models, often called "black boxes," can analyze large datasets and find patterns humans can't.
- These complex algorithms are difficult to analyze mathematically.
Interpreting Black Boxes
- Statisticians are developing methods to interpret the output of black box models without needing to understand their internal workings.
- This involves treating the outputs as statistical objects and calibrating them to quantify uncertainty.
College Admissions Example
- Imagine colleges using black boxes to assess applicants, predicting their success based on various features.
- By analyzing the model's performance on a test set, we can estimate its accuracy and provide prediction intervals, not just point estimates.