Intelligent Tutoring Systems (ITS) are computer-based instructional tools that rely on artificial intelligence techniques to generate individualized interactions tailored to a student's learning needs. Cristina Conati [University of British Columbia] discusses how the scope and effectiveness of ITS can be increased by extending the range of features captured in a student model to include domain independent, meta-cognitive skills and affective states. October 19, 2004. (more)

"Artificial intelligence systems must be able to learn, reason logically, and handle uncertainty. Research has focused on each of these goals individually, and only recently have attempts been made to achieve all three at once. In this colloquium, Pedro Domingos, UW Computer Science & Engineering, describes Markov logic: a representation that combines the full power of first-order logic and probabilistic graphical models, and algorithms for learning and inference in it. Experiments in a real-world university domain." November 2, 2004. (more)

Natural language processing offers a rich problem domain for machine learning approaches. Many NLP problems require the induction of a mapping that involves complex, discrete structures such as strings, labeled sequences, or trees. In this distinguished lecture, Michael Collins [Massachusetts Institute of Technology] describes how 'large margin' methods in machine learning can be generalized to 'structured' problems found in NLP. December 7, 2005. (more)

In this Turing Center distinguished lecture, Lenhart Schubert [University of Rochester] explains that there is a set of clear-cut challenges for artificial intelligence, all centering around knowledge. The solution to those challenges could realize Alan M. Turing's dream - the dream of a machine capable of intelligent human-like response and interaction. Schubert presents preliminary results of recent efforts to extract 'shallow' general knowledge about the world from large text corpora. November 10, 2005. (more)

"Women and other underrepresented groups represent a vast amount of untapped human resource potential needed to fuel both industry and academic research needs. Professor Andrew Williams [Spelman College, Atlanta, Georgia] describes a cohesive, integrated approach to increase the participation and education of women and African Americans using innovative robotics and computer curriculum and competitions. Williams provides several examples, including how the all-women Spelman College's SpelBots RoboCup Four-Legged robot soccer team, and the joint Spelman and Carnegie Mellon University NSF-sponsored project, C.A.R.E. [Computer and Robotics Education for African American Students], have inspired young girls to pursue education and research in robotics and artificial intelligence." November 14, 2006. (more)

"Over 70 studies on human-machine interaction in the last decade have pointed to an intriguing phenomenon: People interact with machines in a way that is basically social, even when the interaction was not designed to be that way. This program will describe how we're giving computers some social skills, specifically the ability to recognize and respond appropriately to human emotion. Examples are shown on keyboard-mouse-monitor systems that try to assess user frustration for usability feedback, and wearable systems that classify affective state based on skin-surface measurements." Questions from the audience follow the talk. October 18, 2001. (more)

"This talk [given by Judea Pearl, University of California, Los Angeles] summarizes concepts, principles, and inference tools that are useful in modeling aspects of causal reasoning. The principles are based on structural-model semantics, in which modifiable functional relationships, representing autonomous physical processes are the fundamental building blocks." Questions from the audience follow the talk. October 4, 2001. (more)

The animation is a 3D simulation of how the Seaglider maneuvers. Cut-away views of the internal mechanics show how the Seaglider pitches, rolls, dives, ascends, and surfaces. These Seaglider animations demonstrate how the Seaglider moves through the ocean and communicate with satellites to transmit data and determine its global position. As the Seaglider dives and ascends its wings cause it to glide, allowing horizontal movement. Internal sensors monitor the depth, heading and attitude of the vehicle. External sensors are constantly scanning the ocean to determine water properties. 2008. (more)