Cognitive Science and Artificial Intelligence. Understanding adaptive and intelligent behavior is one of the central scientific questions of our time. The answer to this question promises to revolutionize the way we understand ourselves as humans and how we develop technology. Whereas artificial intelligence (AI) aims at developing intelligent computer programs and machines, its twin science, cognitive science, aims at understanding and predicting human adaptive and intelligent behavior. Since their birth in the 1950ies, these two scientific fields have continuously and fruitfully influenced each other. While artificial intelligence has been inspired by the progress in studying and understanding the human mind and brain, cognitive science has adopted methods from artificial intelligence and machine learning to model and understand human perception, cognition, and behavior as information processing.
Why is it important? The challenge for the future will be to better understand the human mind, its computations, algorithms, and implementational solutions to complex problems. Successes in AI in recent years have led to the development of technology that has reached our every-day lives such as voice controlled virtual assistants, image recognition systems, autonomous vehicles, and medical diagnostics. But these systems require very large amounts of labeled data, their ability to generalize is extremely limited, and importantly, their predictions are neither explainable nor predictable. Because humans still surpass the ability of machines in numerous areas by a very large margin, studying how and why this is the case will undoubtedly help to develop better artificial intelligence algorithms. On the other hand, as natural and artificial intelligence are interacting in our everyday lives more and more, it becomes crucial to understand how this technology can support us humans better, how humans interact with such systems, and how AI can be made to be beneficial for our societies.
Projects and collaborations of the Centre’s members span basic research in cognitive science, artificial intelligence, and applications as diverse as: teaching robots to learn to interact with the elderly, understanding social biases from text collections, developing AI algorithms that explain their decisions by design, capturing plant physiological intuitions by machines, understanding how students learn new concepts and solve problems, developing smart prostheses, detecting pedestrians from carbound video, identify global patterns of interest from social media use, predictive analysis of human attentional and visuomotor behavior, and extracting physical models from collective behavior.
