Driving Intelligence: The Green Book

Routes to Autonomy
1. Auflage 2025
ISBN: 978-1-032-91122-9
Verlag: Taylor & Francis Ltd

Buch, Englisch, 264 Seiten, Format (B × H): 156 mm x 234 mm

Driving Intelligence takes a critical and captivating tour of autonomous driving, a phenomenon at the intersection of data-driven platforms, artificial (general) intelligence and the mind. The journey extends from Europe to key areas such as Japan, China, and the US, recognizing the global impact of AI & autonomous driving on high-tech and automotive sectors.

The significance of ‘Driving Intelligence’ resonates beyond specialized circles, encompassing a spectrum of perspectives – historical, economic, scientific, and philosophical. The book addresses the pressing question of success probabilities and socioeconomic impacts, not just for specialists but for a wider audience keen on understanding the evolution of AI and mobility in the 21st century.

Avoiding partial insights into this domain, the book provides a comprehensive and multifaceted overview which will appeal to a diverse audience including business leaders and policymakers in the mobility and tech industries, governmental bodies, and the general public globally.

Zielgruppe

Academic, General, Postgraduate, Professional Practice & Development, and Undergraduate Advanced

Autoren/Hrsg.

Seiberth, Gabriel

Bishop, J. Mark

Fachgebiete

• Technische Wissenschaften Verkehrstechnik | Transportgewerbe Fahrzeugtechnik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik
• Technische Wissenschaften Technik Allgemein Mess- und Automatisierungstechnik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Neuronale Netzwerke
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Robotik

Inhaltsverzeichnis

Foreword. Preface. About the Authors. Chapter 1 Self Driving to the Future. Chapter 2 Computing Machinery & Intelligence. Chapter 3 First Steps in Computer Vision. Chapter 4 The Tortoise and the Hare. Chapter 5 The DARPA Grand Challenges. Chapter 6 Forms of Machine Learning. Chapter 7 Types of ML models. Chapter 8 Second & Third DARPA Challenges. Chapter 9 Theory, Empiricism, and Data. Chapter 10 New Forms of Learning. Chapter 11 New Types of Model. Chapter 12 A Whole New Industry Unfolding. Chapter 13 Recurrent Sequence-To-Sequence Learning. Chapter 14 Attention Is All You Need. Chapter 15 LLMs and Multi-Modal Systems. Chapter 16 End-to-end Neural Networks. Chapter 17 A Question of Strategy. Chapter 18 Green Lights Ahead!. Bibliography. Index

Über Autor(innen)

J. Mark Bishop has served as Professor of Cognitive Computing and Director of The Centre for AI and Analytics (TCIDA), at Goldsmiths, University of London. Mark was elected Chair of the AISB (the UK professional body for AI, and the oldest such organisation in the world) from 2010-2014. He currently acts as Chief Scientific Advisor to Fact3602 and is an International Fellow of the Karel Capek Center, Praha, Czech Republic.

Gabriel Seiberth, MBA, is an automotive industry professional with almost 25 years of experience in management consulting and technology. He was recently appointed President Europe & a Member of the Executive Board at KPIT Technologies Ltd., Germany, a global partner in the Automotive and Mobility ecosystem. He was previously Managing Director at Accenture,