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Why Machines Learn

The Elegant Math Behind Modern AI

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Hardcover
$32.00 US
On sale Jul 16, 2024 | 480 Pages | 9780593185742
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 tumour is cancerous, or deciding whether someone gets bail. They now influence developments and discoveries in chemistry, biology, and physics—the study of genomes, extra-solar 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 artifical 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.
Chapter 1
 
Desperately Seeking Patterns
 
When he was a child, the Austrian scientist Konrad Lorenz, enamored by tales from a book called The Wonderful Adventures of Nils-the story of a boy's adventures with wild geese written by the Swedish novelist and winner of the Nobel Prize for Literature, Selma Lagerlöf-"yearned to become a wild goose." Unable to indulge his fantasy, the young Lorenz settled for taking care of a day-old duckling his neighbor gave him. To the boy's delight, the duckling began following him around: It had imprinted on him. "Imprinting" refers to the ability of many animals, including baby ducks and geese (goslings), to form bonds with the first moving thing they see upon hatching. Lorenz would go on to become an ethologist and would pioneer studies in the field of animal behavior, particularly imprinting. (He got ducklings to imprint on him; they followed him around as he walked, ran, swam, and even paddled away in a canoe.) He won the Nobel Prize for Physiology or Medicine in 1973, jointly with fellow ethologists Karl von Frisch and Nikolaas Tinbergen. The three were celebrated "for their discoveries concerning organization and elicitation of individual and social behavior patterns."
 
Patterns. While the ethologists were discerning them in the behavior of animals, the animals were detecting patterns of their own. Newly hatched ducklings must have the ability to make out or tell apart the properties of things they see moving around them. It turns out that ducklings can imprint not just on the first living creature they see moving, but on inanimate things as well. Mallard ducklings, for example, can imprint on a pair of moving objects that are similar in shape or color. Specifically, they imprint on the relational concept embodied by the objects. So, if upon birth the ducklings see two moving red objects, they will later follow two objects of the same color (even if those latter objects are blue, not red), but not two objects of different colors. In this case, the ducklings imprint on the idea of similarity. They also show the ability to discern dissimilarity. If the first moving objects the ducklings see are, for example, a cube and a rectangular prism, they will recognize that the objects have different shapes and will later follow two objects that are different in shape (a pyramid and a cone, for example), but they will ignore two objects that have the same shape.
 
Ponder this for a moment. Newborn ducklings, with the briefest of exposure to sensory stimuli, detect patterns in what they see, form abstract notions of similarity/dissimilarity, and then will recognize those abstractions in stimuli they see later and act upon them. Artificial intelligence researchers would offer an arm and a leg to know just how the ducklings pull this off.
 
While today's AI is far from being able to perform such tasks with the ease and efficiency of ducklings, it does have something in common with the ducklings, and that's the ability to pick out and learn about patterns in data. When Frank Rosenblatt invented the perceptron in the late 1950s, one reason it made such a splash was because it was the first formidable "brain-inspired" algorithm that could learn about patterns in data simply by examining the data. Most important, given certain assumptions about the data, researchers proved that Rosenblatt's perceptron will always find the pattern hidden in the data in a finite amount of time; or, put differently, the perceptron will converge upon a solution without fail. Such certainties in computing are like gold dust. No wonder the perceptron learning algorithm created such a fuss.
 
But what do these terms mean? What are “patterns” in data? What does “learning about these patterns” imply?
© Rajesh Krishnan
Anil Ananthaswamy is an award-winning science writer and former staff writer and deputy news editor for New Scientist. He is the author of several popular science books including The Man Who Wasn’t There, which was long-listed for the Pen/E. O. Wilson Literary Science Writing Award. He was a 2019-20 MIT Knight Science Journalism Fellow and the recipient of the Distinguished Alum Award, the highest award given by IIT-Madras to its graduates, for his contributions to science writing. View titles by Anil Ananthaswamy

About

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 tumour is cancerous, or deciding whether someone gets bail. They now influence developments and discoveries in chemistry, biology, and physics—the study of genomes, extra-solar 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 artifical 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.

Excerpt

Chapter 1
 
Desperately Seeking Patterns
 
When he was a child, the Austrian scientist Konrad Lorenz, enamored by tales from a book called The Wonderful Adventures of Nils-the story of a boy's adventures with wild geese written by the Swedish novelist and winner of the Nobel Prize for Literature, Selma Lagerlöf-"yearned to become a wild goose." Unable to indulge his fantasy, the young Lorenz settled for taking care of a day-old duckling his neighbor gave him. To the boy's delight, the duckling began following him around: It had imprinted on him. "Imprinting" refers to the ability of many animals, including baby ducks and geese (goslings), to form bonds with the first moving thing they see upon hatching. Lorenz would go on to become an ethologist and would pioneer studies in the field of animal behavior, particularly imprinting. (He got ducklings to imprint on him; they followed him around as he walked, ran, swam, and even paddled away in a canoe.) He won the Nobel Prize for Physiology or Medicine in 1973, jointly with fellow ethologists Karl von Frisch and Nikolaas Tinbergen. The three were celebrated "for their discoveries concerning organization and elicitation of individual and social behavior patterns."
 
Patterns. While the ethologists were discerning them in the behavior of animals, the animals were detecting patterns of their own. Newly hatched ducklings must have the ability to make out or tell apart the properties of things they see moving around them. It turns out that ducklings can imprint not just on the first living creature they see moving, but on inanimate things as well. Mallard ducklings, for example, can imprint on a pair of moving objects that are similar in shape or color. Specifically, they imprint on the relational concept embodied by the objects. So, if upon birth the ducklings see two moving red objects, they will later follow two objects of the same color (even if those latter objects are blue, not red), but not two objects of different colors. In this case, the ducklings imprint on the idea of similarity. They also show the ability to discern dissimilarity. If the first moving objects the ducklings see are, for example, a cube and a rectangular prism, they will recognize that the objects have different shapes and will later follow two objects that are different in shape (a pyramid and a cone, for example), but they will ignore two objects that have the same shape.
 
Ponder this for a moment. Newborn ducklings, with the briefest of exposure to sensory stimuli, detect patterns in what they see, form abstract notions of similarity/dissimilarity, and then will recognize those abstractions in stimuli they see later and act upon them. Artificial intelligence researchers would offer an arm and a leg to know just how the ducklings pull this off.
 
While today's AI is far from being able to perform such tasks with the ease and efficiency of ducklings, it does have something in common with the ducklings, and that's the ability to pick out and learn about patterns in data. When Frank Rosenblatt invented the perceptron in the late 1950s, one reason it made such a splash was because it was the first formidable "brain-inspired" algorithm that could learn about patterns in data simply by examining the data. Most important, given certain assumptions about the data, researchers proved that Rosenblatt's perceptron will always find the pattern hidden in the data in a finite amount of time; or, put differently, the perceptron will converge upon a solution without fail. Such certainties in computing are like gold dust. No wonder the perceptron learning algorithm created such a fuss.
 
But what do these terms mean? What are “patterns” in data? What does “learning about these patterns” imply?

Author

© Rajesh Krishnan
Anil Ananthaswamy is an award-winning science writer and former staff writer and deputy news editor for New Scientist. He is the author of several popular science books including The Man Who Wasn’t There, which was long-listed for the Pen/E. O. Wilson Literary Science Writing Award. He was a 2019-20 MIT Knight Science Journalism Fellow and the recipient of the Distinguished Alum Award, the highest award given by IIT-Madras to its graduates, for his contributions to science writing. View titles by Anil Ananthaswamy