Название: How AI Works: From Sorcery to Science Автор: Ronald T. Kneusel Издательство: No Starch Press Год: 2024 Страниц: 240 Язык: английский Формат: epub Размер: 19.3 MB
AI isn’t magic. How AI Works demystifies the explosion of Artificial Intelligence by explaining—without a single mathematical equation—what happened, when it happened, why it happened, how it happened, and what AI is actually doing "under the hood."
Artificial Intelligence is everywhere—from self-driving cars, to image generation from text, to the unexpected power of language systems like ChatGPT—yet few people seem to know how it all really works. How AI Works unravels the mysteries of Artificial Intelligence, without the complex math and unnecessary jargon.
Many books teach you how to do Artificial Intelligence (AI). Similarly, many popular books tell you about AI. However, what seems to be missing is a book that teaches you how AI works at a conceptual level. AI isn’t magic; you can understand what it’s doing without burying yourself in complex mathematics. This book fills that void with a math-free explanation of how AI works. While some books are down in the weeds and others offer a bird’s-eye view, this book is at treetop level. It aims to provide you with enough detail to understand the approach without getting bogged down in nitty-gritty mathematics. If that piques your interest, I invite you to read on.
Deep Learning is a subfield of machine learning, which is a subfield of Artificial Intelligence. This relationship implies that AI involves concepts that are neither machine learning nor Deep Learning. Machine Learning builds models from data. For us, a model is an abstract notion of something that accepts inputs and generates outputs, where the inputs and outputs are related in some meaningful way. The primary goal of Machine Learning is to condition a model using known data so that the model produces meaningful output when given unknown data. That’s about as clear as muddy water, but bear with me; the mud will settle in time. Deep Learning uses large models of the kind previously too big to make useful. More muddy water, but I’m going to argue that there’s no strict definition of deep learning other than that it involves neural networks with many layers.
The relationship between artificial intelligence, machine learning, and deep learning The history behind AI and why the artificial intelligence revolution is happening now How decades of work in symbolic AI failed and opened the door for the emergence of neural networks What neural networks are, how they are trained, and why all the wonder of modern AI boils down to a simple, repeated unit that knows how to multiply input numbers to produce an output number. The implications of large language models, like ChatGPT and Bard, on our society—nothing will be the same again
AI isn’t magic. If you’ve ever wondered how it works, what it can do, or why there’s so much hype, How AI Works will teach you everything you want to know.