TY  - BOOK
AU  - Sejnowski,Terrence J.
TI  - The deep learning revolution
SN  - 9780262038034
AV  - Q 325.5 .S45 2018
U1  - 006.3/1 23
PY  - 2018///]
CY  - Cambridge, Massachusetts
PB  - The MIT Press
KW  - Machine learning
KW  - Big data
KW  - Artificial intelligence
KW  - Social aspects
KW  - COMPUTERS
KW  - Database Management
KW  - Data Mining
KW  - bisacsh
KW  - fast
KW  - K�unstliche Intelligenz
KW  - gnd
KW  - Neuronales Netz
KW  - Maschinelles Lernen
N1  - Includes bibliographical references (pages 285-319) and index; Preface --; Part I; Intelligence reimagined; The rise of machine learning; The rebirth of artificial intelligence; The dawn of neural networks; Brain-style computing; Insights from the visual system --; Part II; Many ways to learn; The cocktail party problem; The Hopfield net and Boltzmann machine; Backpropagating errors; Convolutional learning; Reward learning; Neural information processing systems --; Part III; Technological and scientific impact; The future of machine learning; The age of algorithms; Hello, Mr. Chips; Inside information; Consciousness; Nature is cleverer than we are; Deep intelligence --; Glossary
N2  - How deep learning;from Google Translate to driverless cars to personal cognitive assistants;is changing our lives and transforming every sector of the economy. The deep learning revolution has brought us driverless cars, the greatly improved Google Translate, fluent conversations with Siri and Alexa, and enormous profits from automated trading on the New York Stock Exchange. Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy. Sejnowski played an important role in the founding of deep learning, as one of a small group of researchers in the 1980s who challenged the prevailing logic-and-symbol based version of AI. The new version of AI Sejnowski and others developed, which became deep learning, is fueled instead by data. Deep networks learn from data in the same way that babies experience the world, starting with fresh eyes and gradually acquiring the skills needed to navigate novel environments. Learning algorithms extract information from raw data; information can be used to create knowledge; knowledge underlies understanding; understanding leads to wisdom. Someday a driverless car will know the road better than you do and drive with more skill; a deep learning network will diagnose your illness; a personal cognitive assistant will augment your puny human brain. It took nature many millions of years to evolve human intelligence; AI is on a trajectory measured in decades. Sejnowski prepares us for a deep learning future
ER  -