Machine learning using neural networks has led to a remarkable leap forward in artificial intelligence, and the technological and social ramifications have been discussed at great length. To understand the origin and nature of this progress, it is useful to dig at least a little bit into the mathematical and algorithmic structures underlying these techniques. Anil Ananthaswamy takes up this challenge in his book Why Machines Learn: The Elegant Math Behind Modern AI. In this conversation we give a brief overview of some of the basic ideas, including the curse of dimensionality, backpropagation, transformer architectures, and more.

Blog post with transcript: https://www.preposterousuniverse.com/podcast/2025/11/24/336-anil-ananthaswamy-on-the-mathematics-of-neural-nets-and-ai/

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Anil Ananthaswamy received a Masters degree in electrical engineering from the University of Washington, Seattle. He is currently a freelance science writer and feature editor for PNAS Front Matter. He was formerly the deputy news editor for New Scientist, a Knight Science Journalism Fellow at MIT, and journalist-in-residence at the Simon Institute for the Theory of Computing, University of California, Berkeley. He organizes an annual science journalism workshop at the National Centre for Biological Sciences at Bengaluru, India.

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Welcome to the November 2025 Ask Me Anything episode of Mindscape! These monthly excursions are funded by Patreon supporters (who are also the ones asking the questions). We take questions asked by Patreons, whittle them down to a more manageable number -- based primarily on whether I have anything interesting to say about them, not whether the questions themselves are good -- and sometimes group them together if they are about a similar topic. Enjoy!

Blog post with AMA questions and transcript: https://www.preposterousuniverse.com/podcast/2025/11/17/ama-november-2025/

In the intro I mentioned a couple of my favorite TV shows of this year. Here is a more thought-out list (no particular order):

• Slow Horses
• Murderbot
• The Residence
• Poker Face
• Severance

Pluribus and Down Cemetery Road also look promising, but too early to tell. (There are a huge number of shows I've never seen, so feel free to add recommendations.)

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Science has an incredibly impressive track record of uncovering nonintuitive ideas about the universe that turn out to be surprisingly accurate. It can be tempting to think of scientific discoveries as being carefully constructed atop a rock-solid foundation. In reality, scientific progress is tentative and fallible. Scientists propose models, assign them probabilities, and run tests to see whether they succeed or fail. In cosmologist Andrew Jaffe's new book, The Random Universe, he illustrates how models and probability help us make sense of the cosmos.

Blog post with transcript: https://www.preposterousuniverse.com/podcast/2025/11/10/335-andrew-jaffe-on-models-probability-and-the-universe/

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Andrew Jaffe received his Ph.D. in physics from the University of Chicago. He is currently a professor of astrophysics and cosmology and Director of the Imperial Centre for Inference and Cosmology at Imperial College, London. His research lies at the intersection of theoretical and observational cosmology, including the Planck Surveyor, Euclid, LISA, and Simons Observatory collaborations.

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