Episode 103: Why Machines Learn: The Math Behind AI
In this episode Autumn and Anil Ananthaswamy discuss the inspiration behind his book “Why Machines Learn” and the importance of understanding the math behind machine learning. He explains that the book aims to convey the beauty and essential concepts of machine learning through storytelling, history, sociology, and mathematics. Anil emphasizes the need for society to become gatekeepers of AI by understanding the mathematical basis of machine learning. He also explores the history of machine learning, including the development of neural networks, support vector machines, and kernel methods. Anil highlights the significance of the backpropagation algorithm and the universal approximation theorem in the resurgence of neural networks.
About Our Guest
Anil Ananthaswamy
Anil Ananthaswamy is an award-winning science writer and former staff writer and deputy news editor for the London-based New Scientist magazine. He is a 2019-20 MIT Knight Science Journalism fellow. He writes for Quanta, Scientific American, Nature, New Scientist, and other publications.
His first book, The Edge of Physics, was voted book of the year in 2010 by UK’s Physics World, and his second book, The Man Who Wasn’t There, was long listed for the 2016 Pen/E. O. Wilson Literary Science Writing Award. His third book, Through Two Doors at Once was named one of Forbes's 2018 Best Books About Astronomy, Physics and Mathematics. His new book, Why Machines Learn: The Elegant Math Behind Modern AI, was just published.
Anil trained as an electronics and computer engineer at IIT-Madras (BTech) and the University of Washington, Seattle (MSEE), and worked as a distributed systems software engineer and architect before switching to writing. He studied science journalism at UC Santa Cruz.
How to Support Us
Stay Connected: You can find Anil Ananthaswamy on Twitter @anilananth, his website https://anilananthaswamy.com/, and his new book “Why Machines Learn” on Amazon and everywhere books are sold! :)
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