Artificial Intelligence is the study of computational models of the mind. At Yale there are a wide variety of topics studied, including vision, robotics, planning, learning, and computational neuroscience.
The term ‘artificial intelligence’ is somewhat misleading because the focus of research in the field is often on more mundane activities, such as simple visual perception, than the word ‘intelligence’ would suggest. The field has learned over the years that the effortlessness of a skill such as vision is deceptive, that in fact the brain does a great deal of hard labor behind the scenes to allow us to see without conscious effort. It will take us years to duplicate the skills that nature evolved over eons.
In general we think it is a mistake for AI research to focus on central mental function and ignore input and output. In the long run machines will not be treated as intelligent unless they can perceive and manipulate the objects around them. Real perception and action impose stubborn constraints on thinking. Sophisticated robot planning is wasted if the robot crashes into the wall while trying to generate a predicate-calculus description of the world in front of it. Hence our focus is on real-time perceptual control of behavior, in both natural and artificial systems.
AI uses many of the same techniques as other areas of computer science application, from numerical optimization to symbolic indexing. The key to solving any problem is always the algorithm and its analysis. The goal is always to characterize precisely a set of problems and demonstrate an algorithm that solves them with reasonable efficiency. But, at least at its current state of development, AI is of necessity more exploratory than other areas. We are often forced to define a problem at the same time that we try to solve it. It often happens that we don't know how to analyze the performance of an algorithm with existing tools, but we believe that its average-case performance is much better than its worst-case performance, and this belief must be backed up with experiments.
Faculty members working in this area are Drew McDermott, Brian Scassellati, and Steven Zucker. Michael Hines is a research scientist.