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Mohrbooks Literary Agency
Sebastian Ritscher |
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WHY MACHINES LEARN
The Elegant Math Behind Modern AI
A superb writer on the most challenging subjects, Anil now tackles the math behind the hottest technology exploding right now.
As the irresistibly eloquent Anil Ananthaswamy shows, getting under the mathematical skin of machine learning reveals the power of the technology. Machine learning systems are already making life-altering decisions for us: whether it's approving mortgage loans, determining whether a tumor is cancerous, diagnosing someone with Alzheimer's, or deciding whether someone gets bail. Machine learning systems now influence discoveries in chemistry, biology, and physics - the study of genomes, extra-solar planets, even the intricacies of quantum systems. Very obviously this revolution in intelligence is not slowing down.
The story weaves in simple math that goes back centuries, math that you may have learned in high school - elementary algebra, logarithms, and calculus, the stuff of eighteenth century mathematics. Indeed by the mid-1850s, the groundwork was all done. Yet, those brilliant mathematicians never dreamed their work would deliver such earth-changing tech. It took the kindling of 1990s computer chips designed for video games to ignite the explosion of AI that we see all around us today.
Understanding the various types of math behind machine learning provokes profound questions. Ultimately, there is no difference between natural and artificial intelligence because artificial neurons and the ones in your head follow the same mathematical rules. Both have biases for the same reasons. As Ananthaswamy resonantly concludes, to make the most of our most wondrous technologies we need to understand how their profound limitations mirror our own. Math is the surprising key.
Anil Ananthaswamy is an award-winning journalist and former staff writer and deputy news editor for the London-based New Scientist magazine. He has been a guest editor for the science writing program at the University of California, Santa Cruz, and organizes and teaches an annual science journalism workshop at the National Centre for Biological Sciences in Bengaluru, India. He is a freelance feature editor for the Proceedings of the National Academy of Science's Front Matter. He contributes regularly to the New Scientist, and has also written for Nature, National Geographic News, Discover, Nautilus, Matter, The Wall Street Journal and the UK's Literary Review. His first book, The Edge of Physics, was voted book of the year in 2010 by Physics World, and his second book, The Man Who Wasn't There, won a Nautilus Book Award in 2015 and was long-listed for the 2016 Pen/E. O. Wilson Literary Science Writing Award.
His most recent book, Through Two Doors at Once was named one of Smithsonian's Favorite Books of 2018 and one of Forbes's 2018 Best Books About Astronomy, Physics and Mathematics.
The story weaves in simple math that goes back centuries, math that you may have learned in high school - elementary algebra, logarithms, and calculus, the stuff of eighteenth century mathematics. Indeed by the mid-1850s, the groundwork was all done. Yet, those brilliant mathematicians never dreamed their work would deliver such earth-changing tech. It took the kindling of 1990s computer chips designed for video games to ignite the explosion of AI that we see all around us today.
Understanding the various types of math behind machine learning provokes profound questions. Ultimately, there is no difference between natural and artificial intelligence because artificial neurons and the ones in your head follow the same mathematical rules. Both have biases for the same reasons. As Ananthaswamy resonantly concludes, to make the most of our most wondrous technologies we need to understand how their profound limitations mirror our own. Math is the surprising key.
Anil Ananthaswamy is an award-winning journalist and former staff writer and deputy news editor for the London-based New Scientist magazine. He has been a guest editor for the science writing program at the University of California, Santa Cruz, and organizes and teaches an annual science journalism workshop at the National Centre for Biological Sciences in Bengaluru, India. He is a freelance feature editor for the Proceedings of the National Academy of Science's Front Matter. He contributes regularly to the New Scientist, and has also written for Nature, National Geographic News, Discover, Nautilus, Matter, The Wall Street Journal and the UK's Literary Review. His first book, The Edge of Physics, was voted book of the year in 2010 by Physics World, and his second book, The Man Who Wasn't There, won a Nautilus Book Award in 2015 and was long-listed for the 2016 Pen/E. O. Wilson Literary Science Writing Award.
His most recent book, Through Two Doors at Once was named one of Smithsonian's Favorite Books of 2018 and one of Forbes's 2018 Best Books About Astronomy, Physics and Mathematics.
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