The annual Neural Information Processing Systems (NIPS) conference is the flagship meeting on neural computation. It draws a diverse group of attendees—physicists, neuroscientists, mathematicians, statisticians, and computer scientists. The presentations are interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, brain imaging, vision, speech and signal processing, reinforcement learning and control, emerging technologies, and applications.
In What Is Thought? Eric Baum proposes a computational explanation of thought. Just as Erwin Schrodinger in his classic 1944 work What Is Life? argued ten years before the discovery of DNA that life must be explainable at a fundamental level by physics and chemistry, Baum contends that the present-day inability of computer science to explain thought and meaning is no reason to doubt there can be such an explanation.
Despite their apparently divergent accounts of higher cognition, cognitive theories based on neural computation and those employing symbolic computation can in fact strengthen one another. To substantiate this controversial claim, this landmark work develops in depth a cognitive architecture based in neural computation but supporting formally explicit higher-level symbolic descriptions, including new grammar formalisms.
Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning.
Within cognitive science, two approaches currently dominate the problem of modeling representations. The symbolic approach views cognition as computation involving symbolic manipulation. Connectionism, a special case of associationism, models associations using artificial neuron networks. Peter Gardenfors offers his theory of conceptual representations as a bridge between the symbolic and connectionist approaches.
Reasoning about knowledge—particularly the knowledge of agents who reason about the world and each other's knowledge—was once the exclusive province of philosophers and puzzle solvers. More recently, this type of reasoning has been shown to play a key role in a surprising number of contexts, from understanding conversations to the analysis of distributed computer algorithms.
The psychologist William James observed that "a native talent for perceiving analogies is ... the leading fact in genius of every order." The centrality and the ubiquity of analogy in creative thought have been noted again and again by scientists, artists, and writers, and understanding and modeling analogical thought have emerged as two of the most important challenges for cognitive science.
Classical computationalism—-the view that mental states are computational states—-has come under attack in recent years. Critics claim that in defining computation solely in abstract, syntactic terms, computationalism neglects the real-time, embodied, real-world constraints with which cognitive systems must cope. Instead of abandoning computationalism altogether, however, some researchers are reconsidering it, recognizing that real-world computers, like minds, must deal with issues of embodiment, interaction, physical implementation, and semantics.
Explanation and Interaction describes the problems and issues involved in generating interactive user-sensitive explanations. It presents a particular computational system that generates tutorial, interactive explanations of how simple electronic circuits work. However, the approaches and ideas in the book can be applied to a wide range of computer applications where complex explanations are provided, such as documentation, advisory, and expert systems.