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Driving Intelligence: The Green Book: Routes to Autonomy [Hardcover]

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  • Category: Books (Technology & Engineering)
  • Author:  Bishop, J. Mark, Seiberth, Gabriel
  • Author:  Bishop, J. Mark, Seiberth, Gabriel
  • ISBN-10:  1032911220
  • ISBN-10:  1032911220
  • ISBN-13:  9781032911229
  • ISBN-13:  9781032911229
  • Publisher:  Routledge
  • Publisher:  Routledge
  • Pages:  264
  • Pages:  264
  • Binding:  Hardcover
  • Binding:  Hardcover
  • SKU:  1032911220-11-MPOD
  • SKU:  1032911220-11-MPOD
  • Item ID: 107081183
  • Seller: ShopSpell
  • Ships in: 2 business days
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  • Delivery by: Sep 28 to Sep 30
  • Notes: Brand New Book. Order Now.

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.

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 apek Center, Praha, Czech Republic.

Gabriel Seiberth?is a distinguished automotive industry professional with 25 years of experience in management consulting and technology. He currently holds a top management position at a leading global electronics and services company for the automotive industry. Previously, he served as Managing Director at a leading global technology and consulting firm, where he authored several influential thought leadership pieces around autonomous driving.

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 Challenge for Autonomous Vehicles. Chapter 6 Forms of Machine Learning. Chapter 7 Types of Machine-Learning 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 SequenceToSequence Learning. Chapter 14 Attention Is All You Need. Cl³'

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