Ronald T. Kneusel

How AI Works (eBook, ePUB)

From Sorcery to Science

• Format: ePub

Produktbeschreibung

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. You'll learn:

• 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.

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Produktdetails

• Verlag: No Starch Press
• Seitenzahl: 192
• Erscheinungstermin: 24. Oktober 2023
• Englisch
• ISBN-13: 9781718503731
• Artikelnr.: 68518984

Autorenporträt

Ronald T. Kneusel is a data scientist who builds deep-learning (AI) systems, as well as extensive experience with medical imaging and the development of medical devices. He earned a PhD in machine learning from the University of Colorado, Boulder, has nearly 20 years of machine learning experience in industry, and is presently pursuing deep-learning projects with L3Harris Technologies, Inc. Kneusel is also the author of Random Numbers and Computers (Springer 2018), in addition to Math for Deep Learning, Practical Deep Learning, Strange Code, and The Art of Randomness-all published by No Starch Press.

Inhaltsangabe

Acknowledgments
Preface
Chapter 1: And Away We Go: An AI Overview
Chapter 2: Why Now? A History of AI
Chapter 3: Classical Models: Old-School Machine Learning
Chapter 4: Neural Networks: Brain-Like AI
Chapter 5: Convolutional Neural Networks: AI Learns to See
Chapter 6: Generative AI: AI Gets Creative 
Chapter 7: Large Language Models: True AI at Last?
Chapter 8: Musings: The Implications of AI
Glossary
Resources
Index