336 | Anil Ananthaswamy on the Mathematics of Neural Nets and AI

Sean Carroll's Mindscape: Science, Society, Philosophy, Culture, Arts, and Ideas

Sean Carroll

Physics, Science

4.7 • 4.7K Ratings

🗓️ 24 November 2025

⏱️ 74 minutes

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Summary

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.

Transcript

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0:00.0	Your first great love story is free when you sign up for a free 30-day trial at audible.co.
0:05.5	com.uk-Uk.w.wondery. That's audible.com.com. UK slash Wondery.
0:11.5	Hello, everyone. Welcome to the Mindscape podcast. I'm your host, Sean Carroll.
0:14.6	We all know that artificial intelligence in various forms has been exploding over the last couple of years. After decades of
0:23.1	effort and various summers and winters in AI research, we clearly have crossed some threshold
0:29.3	where AI is being put into use in all sorts of different places. Now, we can debate the words
0:36.5	artificial intelligence, right? Is it really
0:38.7	intelligence? You know, that's the large language models, which are a particular approach to
0:43.7	AI, which have really gotten all the attention lately, they're based on a broader idea called
0:49.1	neural networks or deep learning, which has been all over the place for a long time. Something like Google Maps
0:55.5	uses this kind of technology. But now with the more human-like behavior of AI in the form
1:04.3	of large language models, they've become much more ubiquitous, and there's been a wild
1:10.0	range of reactions to what is going on. Some people
1:13.5	saying that maybe they'll become super intelligent and take over the world and that's a danger.
1:18.8	Other people just complaining that they can't download a new software or an app without it being
1:23.9	infused with AI that they don't really want. So I'm not myself very sure what the
1:30.3	long-term impact of AI is going to be, at least in this sort of large language model incarnation.
1:36.4	A couple of years ago, when it first became a big thing, I said that it's probably somewhere
1:42.1	between the impact of cell phones and electricity.
1:47.1	And still, that's a lot of impact one way or the other, right?
1:50.4	Cell phones have had a lot of impact on our lives, but not really completely changing the way we live.
1:56.3	I think that's a minimal expectation for the impact that AI will have for better or for worse, whereas the larger thing of, you know, the impact level of electricity is maybe the upper level of where it could possibly reach.
	...

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