After Digital

Michael J. Tarr, PhD, Professor and Head, Department of Psychology, Carnegie Mellon University
After Digital is an incisive and eloquent milepost in our accelerating journey to human-like artificial intelligence. Anderson, a seminal figure in the field of neural networks and one of its most prescient practitioners, gives us a first hand look at how we got here and where we are going. An accessible and essential book that will appeal to both scientists and non-scientists alike.>

If you want to understand the arrival of the computer age in the 20th century, you will value this book. To chart the course we are on, Anderson combines a physicist's knowledge of the technology with a psychologist's appreciation of human aspirations and cognitive capabilities. Informative, thoughtful, accessible.-Pentti Kanerva, PhD, Redwood Center for Theoretical Neuroscience


Richard M. Golden, PhD, Professor of Cognitive Science, University of Texas at Dallas
Professor James A. Anderson presents historical and evolutionary arguments emphasizing current attempts to understand biological computation within a digital computer context [that] may lead to computationally easy or sociologically popular solutions which unfortunately are dangerously misleading or even wrong. Anderson's writing style is best described as a fun, fascinating, and entertaining dinner conversation where seemingly innocent conversation threads consisting of interesting anecdote sequences incrementally reveal novel insights about the strengths, limits, and future of both biological and digital computation. Required reading for anyone interested in how biological systems compute as well as anyone interested in exploiting biological constraints for developing smart machines!>

James A. Anderson has been a member of the faculty of Brown University since 1973 and is now Professor in the Department of Cognitive, Linguistic and Psychological Sciences. He received an SB in physics and PhD in physiology both from MIT. He has published extensively in the area of computational models for cognition and memory and computational neuroscience.