The story of why machines learn - the math behind modern AI
Today’s guest is Anil Ananthaswamy - an award-winning science writer and former staff writer and deputy news editor for New Scientist magazine. He is a 2019-20 MIT Knight Science Journalism Fellow and has been a guest editor for the science writing program at the University of California, Santa Cruz, and organizes and teaches an annual science writing workshop at the National Centre for Biological Sciences in Bengaluru, India.
He is a freelance feature editor for PNAS Front Matter. He writes regularly for New Scientist, Quanta, Scientific American, PNAS Front Matter and Nature, and has contributed to Nautilus, Matter, The Wall Street Journal, Discover and the UK’s Literary Review, among others.
He has written four award-winning books including The Edge of Physics: Dispatches from the Frontiers of Cosmology - voted book of the year in 2010 by UK’s Physics World,
The Man Who Wasn’t There: Tales from the Edge of the Self - was long-listed for the 2016 Pen/E. O. Wilson Literary Science Writing Award,
Through Two Doors at Once: The Enigmatic Story of our Quantum Reality- was named one of Smithsonian's Favorite Books of 2018 and one of Forbes's 2018 Best Books About Astronomy, Physics and Mathematics.
And his latest book, Why Machines Learn: The Elegant Math Behind Modern AI which Geoffrey Hinton labelled "A masterpiece."
In this episode, we discuss his start in life, why he went from a career in software to writing and dig deeper into Why Machines Learn including a history of neural networks.
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Please enjoy my conversation with Anil Ananthaswamy.
Why Machines Learn: The Elegant Math Behind Modern AI
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Episode image: Rajesh Krishnan