Artificial intelligence : a guide for thinking humans : Mitchell, Melanie (Compu…
317 pages : 24 cm
317 pages : 24 cm
The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in si…
xvii, 317 p. : 24 cm. --
The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. Here we introduce a new approach to computer Go that uses 'v…
Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object r…
The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in si…
Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort<sup>1-4</sup>, the structures of around 100,000 unique proteins have been determined<sup>5</…
Artificial intelligence. Autonomous mental development by robots and animals
xix, 372 p. ; 24 cm
xv, 508 p. : 24 cm
287 pages : 24 cm
xviii, 345 pages : 21 cm
Using commercial knowledge bases for clinical decision support: opportunities, hurdles, and recommendations
This article described three heuristics that are employed in making judgements under uncertainty: (i) representativeness, which is usually employed when people are asked to judge the probability that an object or event A belongs to class or process B; (ii) ava…
RadioComics - Santa Claus and the future of radiology
Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artifi…
Google's AlphaGo program wins a competition against a human Go master, in what is seen as a landmark moment for artificial intelligence.
Google's AlphaGo program wins a competition against a human Go master, in what is seen as a landmark moment for artificial intelligence.
Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introduc…
In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes relevant work, much of it from the previous millennium. Shallow and…