"Demystifies AI for business professionals, highlighting its strengths, weaknesses, and real-world applications, while providing actionable insights for responsible implementation and risk mitigation"--
"Demystifies AI for business professionals, highlighting its strengths, weaknesses, and real-world applications, while providing actionable insights for responsible implementation and risk mitigation"--Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
Inhaltsangabe
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
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
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