The AI Conundrum
MIT Press (Verlag)
978-0-262-04899-6 (ISBN)
Artificial intelligence, or AI, can recognize a pattern from any set of data it is given, which is what makes it such an extraordinarily powerful tool. But because not all patterns are authentic or reliable, AI’s pattern-finding superpower can lead to spurious patterns—and to disastrous results for business and government entities that rely on them. Hence the conundrum at the heart of AI: its greatest strength can also be its greatest weakness. Targeting the businessperson who needs to know how to use AI profitably and responsibly, Caleb Briggs and Rex Briggs offer in this book a foundational understanding of AI that is easy to grasp yet thorough enough to be used effectively.
The AI Conundrum:
• Draws on the authors’ diverse expertise—in pure math, computer science, marketing, data science, and business—to make AI concepts and applications approachable for readers of all tech levels.
• Provides a framework for comparing AI to the next best alternative, and for gauging where AI is likely be successful—or to pose greater risk than benefits.
• Includes dozens of real-world case studies highlighting the successes and failures of AI applications across various industries.
• Offers actionable insights for responsible implementation and risk mitigation.
• Provides a worksheet for identifying potential problem areas, a cost-benefit analysis, and a companion website.
The AI Conundrum is an invaluable resource for professionals and students seeking a full understanding of AI—its applications, limitations, and ethical considerations—as we enter a brave new era.
Caleb Briggs began coding at 10 and developing AI at 14. He has created several AI applications from scratch, building experience in genetic algorithms, machine vision, natural language, and more. Caleb is currently studying pure math and computer science at Reed College in Portland, Oregon. Rex Briggs is an award-winning AI and data expert who holds five patents and has helped build multiple AI businesses. He currently serves as subject matter expert in AI for the marketing trade association MMA Global. He is the coauthor of What Sticks and the author of SIRFs-Up.
Foreword
Greg Stuart
Preface
Introduction
Part 1: The Fundamentals of Artificial Intelligence
1. Artificial Intelligence Is Not Human Intelligence
2. How AI Fits Patterns
3. How AI Uses Gradient Descent
4. Edge Cases, Compression, and the Limits of Associative Intelligence
5. Precision, Input Control and the Rationale for Decisions
6. Assessing Risk in AI Applications
Part 2: Opportunities, Risks, Countermeasures, and & Critical Questions
7. Case Studies in AI: The AI Revolution and the Sales and Marketing Case Studies
8. Case Studies in AI: Translations, MRIs, Fraud Detection, Autonomous Vehicles, and the Impact of AI on Labor
9. Case Studies in AI: Using AI to Trade in Markets
10. Cases Studies in AI: Bias in Facial Recognition, Hiring and Advertising
11. The Conundrum
Acknowledgements
Endnotes
Index
| Erscheinungsdatum | 26.07.2024 |
|---|---|
| Zusatzinfo | 11 BLACK AND WHITE PHOTOS, 21 LINE DRAWING S, 40 CHARTS |
| Sprache | englisch |
| Maße | 152 x 229 mm |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Naturwissenschaften | |
| Wirtschaft ► Betriebswirtschaft / Management ► Unternehmensführung / Management | |
| Wirtschaft ► Volkswirtschaftslehre | |
| ISBN-10 | 0-262-04899-X / 026204899X |
| ISBN-13 | 978-0-262-04899-6 / 9780262048996 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
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