Lee Spector, professor of computer science, holds a Ph.D. in computer science from the University of Maryland and a B.A. in philosophy from Oberlin College.
His main interests are artificial intelligence and the connections between cognition, computation, and evolution. He is also interested in the use of technology in music and other arts.
His recent research includes projects on the development of new genetic programming techniques, the use of artificial intelligence technologies in the study of quantum computation, the interdisciplinary study of human and machine cognition, and the development of technologies to support inquiry-based education.
Professor Spector is also an active editor, reviewer, and organizer for scientific journals and conferences. He recently received the highest honor bestowed by the National Science Foundation for excellence in both teaching and research, the NSF Director's Award for Distinguished Teaching Scholars. He has held the College's MacArthur Chair and has served as the dean of the School of Cognitive Science and as the elected faculty member of Hampshire's board of trustees.
Computers are commonly (and inconsistently) regarded as both omnipotent and as "stupid machines." In this course we will explore the real limits of computation from philosophical, logical, mathematical and public-policy perspectives. We begin with a discussion of the possibility of "artificial intelligence" (AI), covering the claims that have been made by AI scientists and the critiques of such claims that have arisen from the philosophical community. We then focus on the fundamental logic and mathematics of computation, including techniques for proving that certain problems are "intractable" or "unsolvable." In the third part of the course we turn to social and political questions on which an enlightened view of the limits of computation can have an impact. Students will be evaluated through a combination of short papers and problem sets, along with a final project.
This course is an introduction to computer science and programming framed by the question, "Is it possible for a computer to be creative?" The core areas of computer science will be introduced, including algorithms, complexity, computability, programming languages, data structures, systems, and artificial intelligence, with an eye toward the insights that they can provide about issues of computational creativity. Students will complete several programming projects to demonstrate developing technical skills and engagement with the themes of the course. No previous experience with computers or with programming is required.
Artificial Intelligence is a branch of computer science concerned with the development of computer systems that "think." In this course we will explore the core ideas of artificial intelligence through readings, presentations, discussions, and hands-on programming activities. A range of practical artificial intelligence techniques will be covered, and students will complete programming projects to demonstrate engagement with the themes of the course. Prerequisite: One programming course (in any language).
Genetic programming is a computational technique that harnesses the mechanisms of natural evolution -- including genetic recombination, mutation, and natural selection -- to synthesize computer programs automatically from input/output specifications. It has been applied to a wide range of problems spanning several areas of science, engineering, and the arts. In this course students will explore several variations of the genetic programming technique and apply them to problems of their choosing. Prerequisite: One programming course (any language)
Computation can be performed not only by silicon chips and electricity but also by many other things including tinker toys, billiard balls, water pipes, lights and mirrors, vats of chemicals, DNA, bacteria, and quantum mechanical systems. Furthermore, in some models of computation billions of events may take place simultaneously, with or without synchronization and with or without explicit programming. Some of these unconventional models of computing appear to provide advantages over current technology and may serve as the basis for more powerful computers in the future. In this course we will survey a wide range of unconventional computing concepts, we will consider their implications for the future of computing technology, and we will reconsider conventional computing concepts in this broader context. Prerequisite: At least two courses in computer science
Can androids fall in love? Could a planet have a mind of its own? How might we communicate with alien life forms? Will it ever be possible for two people to "swap minds"? How about a person and a robot? Might we someday be able to buy memories, record dreams, or "read" books by eating pills? Cognitive science research can shed light on many of these questions, with answers that are often as strange and as wonderful as the inventions of science fiction authors. In this course we will read and view science fiction while simultaneously reading current scientific literature about the mind, the brain, and intelligent machines. The science fiction will provide a framework for our discussions, but the real goal of the course is to provide a tour of issues in cognitive science that will prepare students for more advanced cognitive science courses.
Professor of Computer Science
Mail Code CS
Adele Simmons Hall 201
893 West Street
Amherst, MA 01002