C++ for embedded systems

AI isnt magic. How AI Works demystifies the explosion of artificial intelligence by explainingwithout a single mathematical equationwhat happened, when it happened, why it happened, how it happened, and what AI is actually doing "under the hood." How AI Works is for people who want to understand the process without burying themselves in the mathematics and coding required to implement AI. For that, see my books Practical Deep Learning: A Python-Based Introduction and Math for Deep Learning: What You Need to Know to Understand Neural Networks, both from No Starch Press. Artificial intelligence is everywherefrom self-driving cars, to image generation from text, to the unexpected power of language systems like ChatGPTyet 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. Youll 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 societynothing will be the same again AI isnt magic. If youve ever wondered how it works, what it can do, or why theres so much hype, How AI Works will teach you everything you want to know. Cover Page Title Page Copyright Page Dedication Page About the Author About the Technical Reviewer CONTENTS Acknowledgments Preface Chapter 1: And Away We Go: An AI Overview Chapter 2: Why Now? A History of AI Pre-1900 1900 to 1950 1950 to 1970 1980 to 1990 1990 to 2000 2000 to 2012 2012 to 2021 2021 to Now Speed Algorithm Data 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? GPT-4 GPT-3.5 Bard GPT-4 GPT-3.5 Bard 7 billion 13 billion 30 billion Chapter 8: Musings: The Implications of AI Glossary Resources Index