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A rich, narrative explanation of the mathematics that has brought us machine learning and the ongoing explosion of artificial intelligence
Machine learning systems are making life-altering decisions for us: approving mortgage loans, determining whether a tumor is cancerous, or deciding if someone gets bail. They now influence developments and discoveries in chemistry, biology, and physics the study of genomes, extrasolar planets, even the intricacies of quantum systems. And all this before large language models such as ChatGPT came on the scene.
We are living through a revolution in machine learning-powered AI that shows no signs of slowing down. This technology is based on relatively simple mathematical ideas, some of which go back centuries, including linear algebra and calculus, the stuff of seventeenth- and eighteenth-century mathematics. It took the birth and advancement of computer science and the kindling of 1990s computer chips designed for video games to ignite the explosion of AI that we see today. In this enlightening book, Anil Ananthaswamy explains the fundamental math behind machine learning, while suggesting intriguing links between artificial and natural intelligence. Might the same math underpin them both?
As Ananthaswamy resonantly concludes, to make safe and effective use of artificial intelligence, we need to understand its profound capabilities and limitations, the clues to which lie in the math that makes machine learning possible.
Machine learning systems are making life-altering decisions for us: approving mortgage loans, determining whether a tumor is cancerous, or deciding if someone gets bail. They now influence developments and discoveries in chemistry, biology, and physics the study of genomes, extrasolar planets, even the intricacies of quantum systems. And all this before large language models such as ChatGPT came on the scene.
We are living through a revolution in machine learning-powered AI that shows no signs of slowing down. This technology is based on relatively simple mathematical ideas, some of which go back centuries, including linear algebra and calculus, the stuff of seventeenth- and eighteenth-century mathematics. It took the birth and advancement of computer science and the kindling of 1990s computer chips designed for video games to ignite the explosion of AI that we see today. In this enlightening book, Anil Ananthaswamy explains the fundamental math behind machine learning, while suggesting intriguing links between artificial and natural intelligence. Might the same math underpin them both?
As Ananthaswamy resonantly concludes, to make safe and effective use of artificial intelligence, we need to understand its profound capabilities and limitations, the clues to which lie in the math that makes machine learning possible.
Produktdetails
Erscheinungsdatum
07. August 2024
Sprache
englisch
Seitenanzahl
464
Autor/Autorin
Anil Ananthaswamy
Verlag/Hersteller
Produktart
gebunden
Abbildungen
EQUATIONS, GRAPHS, AND TABLES
Gewicht
625 g
Größe (L/B/H)
235/167/42 mm
ISBN
9780593185742
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Pressestimmen
A Next Big Idea Club Must-Read Title for July
One of The Information's 5 Best AI Books of 2024
A Winner of the Artificiality Book Awards 2024
Why Machines Learn, by the award-winning science writer Anil Ananthaswamy, takes the reader on an entertaining journey into the mind of a machine [The book] demystifies the underlying mechanisms behind machine learning, which may possibly lead to a better understanding of the learning process itself and the development of improved AI.
Physics World
A skillful primer makes sense of the mathematics beneath AI's hood.
New Scientist
Whether Ananthaswamy is talking of ML algorithms or manipulation of matrices, he maintains a lightness of language and invokes historical accounts to advance a compelling narrative A must-read for anyone who is curious to understand 'the elegant math behind modern AI' [and] an inspirational guide for teachers of math and mathematical sciences who can adopt these techniques and methods to make classrooms lively.
Shaastra, IIT-Madras
Some books about the development of neural networks describe the underlying mathematics while others describe the social history. This book presents the mathematics in the context of the social history. It is a masterpiece. The author is very good at explaining the mathematics in a way that makes it available to people with only a rudimentary knowledge of the field, but he is also a very good writer who brings the social history to life.
Geoffrey Hinton, Nobel Laureate, deep learning pioneer, Turing Award winner, former VP at Google, and Professor Emeritus at University of Toronto
After just a few minutes of reading Why Machines Learn, you ll feel your own synaptic weights getting updated. By the end you will have achieved your own version of deep learning with deep pleasure and insight along the way.
Steven Strogatz, New York Times bestselling author of Infinite Powers and professor of mathematics at Cornell University
If you were looking for a way to make sense of the AI revolution that is well underway, look no further. With this comprehensive yet engaging book, Anil Ananthaswamy puts it all into context, from the origin of the idea and its governing equations to its potential to transform medicine, quantum physics and virtually every aspect of our life. An essential read for understanding both the possibilities and limitations of artificial intelligence.
Sabine Hossenfelder, physicist and New York Times bestselling author of Existential Physics: A Scientist's Guide to Life's Biggest Questions
Why Machines Learn is a masterful work that explains in clear, accessible, and entertaining fashion the mathematics underlying modern machine learning, along with the colorful history of the field and its pioneering researchers. As AI has increasingly profound impacts in our world, this book will be an invaluable companion for anyone who wants a deep understanding of what s under the hood of these often inscrutable machines.
Melanie Mitchell, author of Artificial Intelligence and Professor at the Santa Fe Institute
Generative AI, with its foundations in machine learning, is as fundamental an advance as the creation of the microprocessor, the Internet, and the mobile phone. But almost no one, outside of a handful of specialists, understands how it works. Anil Ananthaswamy has removed the mystery by giving us a gentle, intuitive, and human-oriented introduction to the math that underpins this revolutionary development.
Peter E. Hart, AI pioneer, entrepreneur, and co-author of Pattern Classification
Anil Ananthaswamy s Why Machines Learn embarks on an exhilarating journey through the origins of contemporary machine learning. With a captivating narrative, the book delves into the lives of influential figures driving the AI revolution while simultaneously exploring the intricate mathematical formalism that underpins it. As Anil traces the roots and unravels the mysteries of modern AI, he gently introduces the underlying mathematics, rendering the complex subject matter accessible and exciting for readers of all backgrounds.
Björn Ommer, Professor at the Ludwig Maximilian University of Munich and leader of the original team behind Stable Diffusion
An inspiring introduction to the mathematics of AI.
Arthur I. Miller, author of The Artist in the Machine: The World of AI-Powered Creativity
"Will there be math? Oh, yes, there will be math. But Ananthaswamy is
the best guide you could ask for on such a perilous journey."
The Information
"This book is the ultimate explainer. . . What I love most is how [Ananthaswamy] threads history into the equations. You get why these methods matter, how they were discovered, and why they ve stuck around. I felt like I was part of the journey, not just staring at some abstract formula. If you re curious about how machines learn but feel like math is a wall you can t climb, this book is your ladder. Highly recommended."
Helen Edwards, The Artificiality Institute
[An] illuminating overview of how machine learning works.
Kirkus Reviews
One of The Information's 5 Best AI Books of 2024
A Winner of the Artificiality Book Awards 2024
Why Machines Learn, by the award-winning science writer Anil Ananthaswamy, takes the reader on an entertaining journey into the mind of a machine [The book] demystifies the underlying mechanisms behind machine learning, which may possibly lead to a better understanding of the learning process itself and the development of improved AI.
Physics World
A skillful primer makes sense of the mathematics beneath AI's hood.
New Scientist
Whether Ananthaswamy is talking of ML algorithms or manipulation of matrices, he maintains a lightness of language and invokes historical accounts to advance a compelling narrative A must-read for anyone who is curious to understand 'the elegant math behind modern AI' [and] an inspirational guide for teachers of math and mathematical sciences who can adopt these techniques and methods to make classrooms lively.
Shaastra, IIT-Madras
Some books about the development of neural networks describe the underlying mathematics while others describe the social history. This book presents the mathematics in the context of the social history. It is a masterpiece. The author is very good at explaining the mathematics in a way that makes it available to people with only a rudimentary knowledge of the field, but he is also a very good writer who brings the social history to life.
Geoffrey Hinton, Nobel Laureate, deep learning pioneer, Turing Award winner, former VP at Google, and Professor Emeritus at University of Toronto
After just a few minutes of reading Why Machines Learn, you ll feel your own synaptic weights getting updated. By the end you will have achieved your own version of deep learning with deep pleasure and insight along the way.
Steven Strogatz, New York Times bestselling author of Infinite Powers and professor of mathematics at Cornell University
If you were looking for a way to make sense of the AI revolution that is well underway, look no further. With this comprehensive yet engaging book, Anil Ananthaswamy puts it all into context, from the origin of the idea and its governing equations to its potential to transform medicine, quantum physics and virtually every aspect of our life. An essential read for understanding both the possibilities and limitations of artificial intelligence.
Sabine Hossenfelder, physicist and New York Times bestselling author of Existential Physics: A Scientist's Guide to Life's Biggest Questions
Why Machines Learn is a masterful work that explains in clear, accessible, and entertaining fashion the mathematics underlying modern machine learning, along with the colorful history of the field and its pioneering researchers. As AI has increasingly profound impacts in our world, this book will be an invaluable companion for anyone who wants a deep understanding of what s under the hood of these often inscrutable machines.
Melanie Mitchell, author of Artificial Intelligence and Professor at the Santa Fe Institute
Generative AI, with its foundations in machine learning, is as fundamental an advance as the creation of the microprocessor, the Internet, and the mobile phone. But almost no one, outside of a handful of specialists, understands how it works. Anil Ananthaswamy has removed the mystery by giving us a gentle, intuitive, and human-oriented introduction to the math that underpins this revolutionary development.
Peter E. Hart, AI pioneer, entrepreneur, and co-author of Pattern Classification
Anil Ananthaswamy s Why Machines Learn embarks on an exhilarating journey through the origins of contemporary machine learning. With a captivating narrative, the book delves into the lives of influential figures driving the AI revolution while simultaneously exploring the intricate mathematical formalism that underpins it. As Anil traces the roots and unravels the mysteries of modern AI, he gently introduces the underlying mathematics, rendering the complex subject matter accessible and exciting for readers of all backgrounds.
Björn Ommer, Professor at the Ludwig Maximilian University of Munich and leader of the original team behind Stable Diffusion
An inspiring introduction to the mathematics of AI.
Arthur I. Miller, author of The Artist in the Machine: The World of AI-Powered Creativity
"Will there be math? Oh, yes, there will be math. But Ananthaswamy is
the best guide you could ask for on such a perilous journey."
The Information
"This book is the ultimate explainer. . . What I love most is how [Ananthaswamy] threads history into the equations. You get why these methods matter, how they were discovered, and why they ve stuck around. I felt like I was part of the journey, not just staring at some abstract formula. If you re curious about how machines learn but feel like math is a wall you can t climb, this book is your ladder. Highly recommended."
Helen Edwards, The Artificiality Institute
[An] illuminating overview of how machine learning works.
Kirkus Reviews
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