Geoffrey Hinton – författare

This book provides the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts of pattern recognition, the book describes techniques for modelling probability density functions, and discusses the properties and relative merits of the multi-layer perceptron and radial basis function network models. It also motivates the use of various forms of error functions, and reviews the principal algorithms for error function minimization. As well as providing a detailed discussion of learning and generalization in neural networks, the book also covers the important topics of data processing, feature extraction, and prior knowledge. The book concludes with an extensive treatment of Bayesian techniques and their applications to neural networks.

When Geoffrey Hinton, the pioneering architect of artificial neural networks, resigned from Google in 2023 following an epiphany about the dangers of artificial intelligence, it made headlines around the world. Smarter Than Us is his long-awaited call to arms. As he shows, only by fully understanding how AI works, can you see how dangerous it is becoming. Here he equips us with the scientific knowledge we urgently need. Grounding his arguments in neuroscience and computer science, he explains why we cannot expect superintelligent machines to remain subservient to us. AI, he argues, will change our view of our place in the universe as much as the Copernican or Darwinian revolutions. We must prepare for a future where our achievements are eclipsed by those of our creations. Survival depends not on dominating these systems, but on ensuring they care about our wellbeing. The future is uncertain, but we can shape it through our choices, including demanding governments regulate, and tech companies behave transparently and accountably. Deeply authoritative, persuasive, and humane, Smarter Than Us sets out what we can do while we are still in control.