Published by The AAAI Press, Menlo Park, California. This proceedings is available in book format. Please Note: Abstracts are linked to individual titles, and will appear in a separate browser window. Full-text versions of the papers are linked to the abstract text. Access to full text may be restricted to AAAI members.

ACCESS: a computer vision art project (ACCESS) - This project uses computer vision to track users and control a robotic spotlight. Users online can view two webcams and the tracking information. Behavioral model of active visual perception and invariant recognition (BMV) (Rostov State U) Content-Based Image Retrieval: Interactive Learning and Search - This demo has a supervised learning capability to fine tune search queries. Corner Detection in Curves - Five algorithms for corner detection in planar curves are described and presented for online comparison. Test images are provided, including defects in textiles.

Welcome to the home page of the computer Hex research group. We --- Kenny Young, Kelly Li, Broderick, Phil, Ryan, Jakub (and previously Aja, David, Jack, Mike, Morgan, Nathan Po, Maryia, Martha, Leah, Yngvi, Geoff Ryan, and Robert Budac) --- build Hex players and solvers. The group informally dates from 1999, when Jack, who wrote Queenbee, started an MSc with Jonathan. Current projects include MoHex, and Solver. Previous projects include Wolve, Mongoose and Queenbee.

Imagine seeing a dozen pictures flash by in a fraction of a second. You might think it would be impossible to identify any images you see for such a short time. However, a team of neuroscientists from MIT has found that the human brain can process entire images that the eye sees for as little as 13 milliseconds -- the first evidence of such rapid processing speed. That speed is far faster than the 100 milliseconds suggested by previous studies. In the new study, which appears in the journal Attention, Perception, and Psychophysics, researchers asked subjects to look for a particular type of image, such as "picnic" or "smiling couple," as they viewed a series of six or 12 images, each presented for between 13 and 80 milliseconds.

Every time you open your eyes, visual information flows into your brain, which interprets what you're seeing. Now, for the first time, MIT neuroscientists have noninvasively mapped this flow of information in the human brain with unique accuracy, using a novel brain-scanning technique. This technique, which combines two existing technologies, allows researchers to identify precisely both the location and timing of human brain activity. Using this new approach, the MIT researchers scanned individuals' brains as they looked at different images and were able to pinpoint, to the millisecond, when the brain recognizes and categorizes an object, and where these processes occur. "This method gives you a visualization of'when' and'where' at the same time. It's a window into processes happening at the millisecond and millimeter scale," says Aude Oliva, a principal research scientist in MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL).

The human brain has 100 billion neurons, connected to each other in networks that allow us to interpret the world around us, plan for the future, and control our actions and movements. MIT neuroscientist Sebastian Seung wants to map those networks, creating a wiring diagram of the brain that could help scientists learn how we each become our unique selves. In a paper appearing in the Aug. 7 online edition of Nature, Seung and collaborators at MIT and the Max Planck Institute for Medical Research in Germany have reported their first step toward this goal: Using a combination of human and artificial intelligence, they have mapped all the wiring among 950 neurons within a tiny patch of the mouse retina. Composed of neurons that process visual information, the retina is technically part of the brain and is a more approachable starting point, Seung says. They also identified a new type of retinal cell that had not been seen before.

"Imagine a 6-year-old kid about to start school. The kid has only known his local village, the local fields," Italian tenor Andrea Bocelli said at MIT on Friday. "This is a place that kid would have imagined was close to the stars. That kid, of course, was me." Bocelli, who became blind after a childhood accident, visited MIT in support of the Andrea Bocelli Foundation's (ABF) funding of research on assistive technologies for the blind and for reducing global poverty.

Spending a day in someone else's shoes can help us to learn what makes them tick. Now the same approach is being used to develop a better understanding between humans and robots, to enable them to work together as a team. Robots are increasingly being used in the manufacturing industry to perform tasks that bring them into closer contact with humans. But while a great deal of work is being done to ensure robots and humans can operate safely side-by-side, more effort is needed to make robots smart enough to work effectively with people, says Julie Shah, an assistant professor of aeronautics and astronautics at MIT and head of the Interactive Robotics Group in the Computer Science and Artificial Intelligence Laboratory (CSAIL). "People aren't robots, they don't do things the same way every single time," Shah says.

John Mikhael sees three fields as key to understanding the brain: math, neuroscience, and medicine. "If you want to understand how the brain works, combining those three is a great way to get there," he says. Mikhael, who graduated from MIT in June with a bachelor's degree in mathematics, plans to pursue his study of neuroscience next fall when he enters an MD/PhD program at Oxford University with a Rhodes Scholarship. "Neuroscience is a very exciting field," he says. "In many ways, the brain is the most sophisticated computer out there. Our brains can do things effortlessly that we couldn't even dream of teaching computers how to do, like producing language, understanding social cues, or recognizing faces with our level of proficiency."

The birth of artificial-intelligence research as an autonomous discipline is generally thought to have been the monthlong Dartmouth Summer Research Project on Artificial Intelligence in 1956, which convened 10 leading electrical engineers -- including MIT's Marvin Minsky and Claude Shannon -- to discuss "how to make machines use language" and "form abstractions and concepts." A decade later, impressed by rapid advances in the design of digital computers, Minsky was emboldened to declare that "within a generation ... the problem of creating'artificial intelligence' will substantially be solved." The problem, of course, turned out to be much more difficult than AI's pioneers had imagined. In recent years, by exploiting machine learning -- in which computers learn to perform tasks from sets of training examples -- artificial-intelligence researchers have built special-purpose systems that can do things like interpret spoken language or play Jeopardy with great success. But according to Tomaso Poggio, the Eugene McDermott Professor of Brain Sciences and Human Behavior at MIT, "These recent achievements have, ironically, underscored the limitations of computer science and artificial intelligence. We do not yet understand how the brain gives rise to intelligence, nor do we know how to build machines that are as broadly intelligent as we are."