But University of Wisconsin-Madison research psychiatrist Giulio Tononi, who was recently selected to take part in the creation of a "cognitive computer," says the goal of building a computer as quick and flexible as a small mammalian brain is more daunting than it sounds. Tononi, professor of psychiatry at the UW-Madison School of Medicine and Public Health and an internationally known expert on consciousness, is part of a team of collaborators from top institutions who have been awarded a $4.9 million grant from the Defense Advanced Research Projects Agency (DARPA) for the first phase of DARPA's Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE) project. Tononi and scientists from Columbia University and IBM will work on the "software" for the thinking computer, while nanotechnology and supercomputing experts from Cornell, Stanford and the University of California-Merced will create the "hardware." Dharmendra Modha of IBM is the principal investigator. "Every neuron in the brain knows that something has changed," Tononi explains. "It tells the brain, 'I got burned, and if you want to change, this is the time to do it.''

As cheap cloud computing services erode IBM's traditional hardware business with alarming speed, the company finds itself facing an uncertain future. If only there were some clever machine it could turn to for advice. Appropriately enough that's what a large part of IBM's research division is trying to create, by building on the research effort that led to Watson, the computer that won in the game show Jeopardy! in 2011. The hope is that this effort will lead to software and hardware that can answer complex questions by looking through vast amounts of information containing subtle and disparate clues. "We're betting billons of dollars, and a third of this division now is working on it," John Kelly, director of IBM Research, said of cognitive computing, a term the company uses to refer to artificial intelligence techniques related to Watson.

IBM sees cognitive computing as the new frontier of computing and is positioning its Watson architecture as the way forward in this new landscape, for both the company and its customers. In a New York event Thursday to launch the organization's new Watson business unit, IBM CEO Ginni Rometty touted the 2011 Watson victory on the "Jeopardy" game show as nothing less than a harbinger of a new era in computing. Today we are in the "programmable era" of computers, in which all the possible actions that a computer can take must be programmed in advance, she explained. In contrast, Watson is "a new species," Rometty said. Watson "is taught--it is not programmed. It runs by experience and from interaction. By design, it gets smarter over time and gives better judgments over time," Rometty said.

When Kenneth Wayne Jennings, noted for holding the record for the longest winning streak of 74 games on the U.S. syndicated game show, bowed to IBM's Watson as the new "Jeopardy!" That was probably one small step for a computer but a giant leap for computing. It's ironic to say that Watson's dominance on the game show didn't come out of the blue. The result was a culmination of over a decade of IBM's research. "It opened up a new chapter in information technology called cognitive computing--based on the idea of a natural interaction between systems and people," says Zachary (Zach) Lemnios, vice president of strategy for IBM Research.

IBM and four universities are planning a research project into cognitive computing, which seeks to build computers that operate in a manner closer to the human mind. The goal is to create systems that extend well beyond Watson, IBM's computer that famously competed on the trivia game show Jeopardy and defeated two former champions, IBM said in a news release. The project will be undertaken with Carnegie Mellon University, the Massachusetts Institute of Technology, New York University and Rensselaer Polytechnic Institute, IBM said. IBM linked the research with "big data," the term for using computers in new ways to process large volumes of structured and unstructured data in order to make it more accessible and useful. Topics to be explored include how applications can boost group decision making, how processing power and algorithms apply to artificial intelligence, how systems should be designed for more natural interaction and how deep learning impacts automated pattern recognition in science.

As one of the few genuine departments of cognitive science in the world, we offer unique and exciting opportunities for students to focus on the scientific study of mind, brain, and intelligence. Staffed by a core of cognitive-science oriented psychologists, philosophers, and computer scientists, the department complements Rensselaer's traditional strengths in science, engineering, and technology, and is widely regarded as a leader in the area of computational cognitive modeling. We offer a highly selective PhD program in Cognitive Science, and BS programs in both Psychology and Philosophy. Faculty research interests include computational cognitive modeling, artificial intelligence, human and machine reasoning, computational linguistics, perception and action, theoretical neuroscience, cognitive robotics, cognitive engineering and advanced synthetic characters.

Cognitive Science Ph.D. Program Accepting Applications for Fall 2017 The Cognitive Science Program is currently accepting applications for the Ph.D. program for 2017 fall matriculation. Information about the application procedure is available at Admission to the Cognitive Science Ph.D. Program. Deadline for international and domestic applicants is December 1, 2016. Information about this special program and the application procedure is available here. Deadline for applications for this special program is December 1, 2016.

IBM's California research lab sits atop a green hill here, 15 miles south of downtown San Jose. There aren't any signs that suggest if you drive up the narrow road that wraps around the hill you'll find a research facility at the top. No signs that the research center is home to a Fortune 500 company. No signs -- even inside -- that the company once dominated the personal computer industry.

We use empirical methods and formal tools to uncover the mechanisms of human learning and inference. We study the computational basis of human learning and inference. Through a combination of mathematical modeling, computer simulation, and behavioral experiments, we try to uncover the logic behind our everyday inductive leaps: constructing perceptual representations, separating "style" and "content" in perception, learning concepts and words, judging similarity or representativeness, inferring causal connections, noticing coincidences, predicting the future. We approach these topics with a range of empirical methods -- primarily, behavioral testing of adults, children, and machines -- and formal tools -- drawn chiefly from Bayesian statistics and probability theory, but also from geometry, graph theory, and linear algebra. Our work is driven by the complementary goals of trying to achieve a better understanding of human learning in computational terms and trying to build computational systems that come closer to the capacities of human learners.

This site was built up by Stephanie Warrick (formerly at the University College London) and is currently maintained by Uwe R. Zimmer uwe.zimmer@ieee.org . Updates and suggestions for additions are welcome from people engaged in academic or non-commercial scientific research.