Artificial Intelligence

Artificial Intelligence has evolved to address problems of probabilistic and numeric nature, leading to the incorporation of approaches from mathematics, engineering, operations research and economics. Our researchers scientifically build upon each others’ work, and research from other disciplines, creating analytical and highly systematic experimental work. The resulting AI systems can deal with uncertainty — both in an objective sense and in the sense that arises in those AI applications that include non-trivial perception.

The explosion of computerized information on the Internet, in data warehouses, and from physical sensory artifacts has also created large-scale problems for AI research. Carnegie Mellon has been at the forefront of that progress, with pioneering innovations in intelligent digital libraries, data representations and algorithms, market clearing technologies, and complex task-embedded robotic applications.

Carnegie Mellon faculty are recognized for their leadership in expanding beyond monolithic AI to multiagent systems, where the key challenge is the intelligent interaction of multiple parties. Finally, Carnegie Mellon faculty have led AI from the abstract (“Can one build a thinking machine?”) to a host of concrete applications and techniques that are significantly improving the world.