Donald Michie was born on 11 November 1923, and was educated at Rugby School and Balliol College, Oxford. He obtained the MA, DPhil, and DSc degrees from Oxford University for studies in biological sciences. For contributions to artificial intelligence he was elected a founding Fellow of the American Association of Artificial Intelligence. He has received honorary degrees from the UK's National Council of Academic Awards, from Salford University, Aberdeen University, the University of York and the University of Stirling.

AIAI is part of the Centre for Intellligent Systems and their Applications (CISA) within the School of Informatics at the University of Edinburgh. We offer a wide range of undergraduate and postgraduate degrees in Artificial Intelligence, Cognitive Science, Computational Linguistics, Computer Science and Software Engineering.

JILR is the official journal of the the Association for the Advancement of Computing in Education (AACE). Members have free, online access to all back issues via LearnTechLib–The Learning & Technology Library. Indexed in leading indices including: Educational Research Abstracts, ERIC, LearnTechLib-The Learning and Technology Library, Index Copernicus, GetCited, Google Scholar, Journal Seek, Microsoft Academic Search, Bacon's Media Directory, Cabell's, Ulrich, and several others.

The National Information Infrastructure (NII) will have profound effects on the lives of every citizen. It promises to deliver to people in their homes and offices a vast array of information in many forms, changing the ways in which business is conducted, offering new educational opportunities, bringing geographically dispersed library resources and entertainment materials to everyone's doorstep. It will connect people to people, and help them with their jobs and tasks. For the NII to be useful, however, people will need easy and efficient access to its resources. Today's computers are complex and difficult to use, even for experts. The NII will be orders of magnitude more complex than current systems; it could easily become a labyrinth of databases and services that is inconvenient for experts and inaccessible to many Americans. The field of artificial intelligence (AI) can play a pivotal role in meeting major challenges of the NII. AI uses the theoretical and experimental tools of ...

Founded in 1979, the Association for the Advancement of Artificial Intelligence (AAAI) (formerly the American Association for Artificial Intelligence) is a nonprofit scientific society devoted to advancing the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines. AAAI aims to promote research in, and responsible use of, artificial intelligence. AAAI also aims to increase public understanding of artificial intelligence, improve the teaching and training of AI practitioners, and provide guidance for research planners and funders concerning the importance and potential of current AI developments and future directions. Major AAAI activities include organizing and sponsoring conferences, symposia, and workshops, publishing a quarterly magazine for all members, publishing books, proceedings, and reports, and awarding grants, scholarships, and other honors. We are delighted to announce that the AAAI-17 conference will be held February 4–9, 2017 in San Francisco, California.

They comb through databases of previously labeled images and look for combinations of visual features that seem to correlate with particular objects. Then, when presented with a new image, they try to determine whether it contains one of the previously identified combinations of features. Even the best object-recognition systems, however, succeed only around 30 or 40 percent of the time -- and their failures can be totally mystifying. Researchers are divided in their explanations: Are the learning algorithms themselves to blame? Or are they being applied to the wrong types of features?

Reinforcement learning is a technique, common in computer science, in which a computer system learns how best to solve some problem through trial-and-error. Classic applications of reinforcement learning involve problems as diverse as robot navigation, network administration and automated surveillance. At the Association for Uncertainty in Artificial Intelligence's annual conference this summer, researchers from MIT's Laboratory for Information and Decision Systems (LIDS) and Computer Science and Artificial Intelligence Laboratory will present a new reinforcement-learning algorithm that, for a wide range of problems, allows computer systems to find solutions much more efficiently than previous algorithms did. The paper also represents the first application of a new programming framework that the researchers developed, which makes it much easier to set up and run reinforcement-learning experiments. Alborz Geramifard, a LIDS postdoc and first author of the new paper, hopes that the software, dubbed RLPy (for reinforcement learning and Python, the programming language it uses), will allow researchers to more efficiently test new algorithms and compare algorithms' performance on different tasks.

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."

About 10 percent of the U.S. population suffers from dyslexia, a condition that makes learning to read difficult. Dyslexia is usually diagnosed around second grade, but the results of a new study from MIT could help identify those children before they even begin reading, so they can be given extra help earlier. The study, done with researchers at Boston Children's Hospital, found a correlation between poor pre-reading skills in kindergartners and the size of a brain structure that connects two language-processing areas. Previous studies have shown that in adults with poor reading skills, this structure, known as the arcuate fasciculus, is smaller and less organized than in adults who read normally. However, it was unknown if these differences cause reading difficulties or result from lack of reading experience.

Tutor research group focuses on various issues related to Intelligent Tutoring Systems (ITS). Our current projects include Programming By Demonstration for Tutoring Systems, Authoring Tools for Intelligent Tutoring Systems, Using Association Rules to Mine Data from Tutorial Log Files, Improving an Intelligent Tutoring System by Data Mining Tutorial Log Files, Integrating Tutoring Systems into Distributed Simulations Environments: Tutoring Collaborative Learning and many more. Subscribe to the mailing list to receive TRG announcements.