An Introduction to Fuzzy Logic Applications in Intelligent Systems consists of a collection of chapters written by leading experts in the field of fuzzy sets. Each chapter addresses an area where fuzzy sets have been applied to situations broadly related to intelligent systems. The volume provides an introduction to and an overview of recent applications of fuzzy sets to various areas of intelligent systems. Its purpose is to provide information and easy access for people new to the field. The book also serves as an excellent reference for researchers in the field and those working in the specifics of systems development.

We recently ran across Alelo (www.alelo.com), The company's very engaging, interactive 3D role playing games teach languages like Arabic and Pashto to troops being deployed to the Middle East. Using speech recognition and other technology, the titles teach foreign languages to players as they go through the game in simulated environments like Iraq. We spoke with Dr. W. Lewis Johnson, CEO of Alelo, about the firm's technology and plans. Ben Kuo: Tell us a little bit about Alelo, and what the company and product does?

But the robotic rush in Japan is also being driven by unique societal needs. Confronting a major depopulation problem due to a record low birthrate and its status as the nation with the longest lifespan on Earth, Japanese are fretting about who will staff the factory floors of the world's second-largest economy in the years ahead. Toyota, Japan's biggest car maker, has come up with one answer in moving to create a line of worker robots with human-like hands able to perform multiple sophisticated tasks.

The Massachusetts Institute of Technology brain sciences department and, separately, a group of some 200 neuroscientists from around the world have written letters to The New York Times claiming that a book excerpt in the newspaper's Sunday magazine this week contains important errors, misinterpretations of scientific disputes, and unfair characterizations of an MIT neuroscientist who did groundbreaking research on human memory. In particular, the excerpt contains a 36-volley verbatim exchange between author Luke Dittrich and MIT's Suzanne Corkin in which she says that key documents from historic experiments were "shredded." "Most of it has gone, is in the trash, was shredded," Corkin is quoted as telling Dittrich before she died in May, explaining, "there's no place to preserve it." Destroying files related to historic scientific research would raise eyebrows, but Corkin's colleagues say it never happened. "We believe that no records were destroyed and, to the contrary, that professor Corkin worked in her final days to organize and preserve all records," said the letter that Dr. James DiCarlo, head of the MIT Department of Brain and Cognitive Sciences, sent to the Times late Tuesday.

During the DARPA Robotics Challenge Finals, which took place here June 5 and 6, the winning team's DRC-HUBO robot finished all eight tasks in less than 45 minutes. The winning bot had a humanoid design that could transform itself into a wheeled kneeling position for faster, more stable movement. The Running Man robot from Florida's Institute for Human and Machine Cognition claimed second, finishing all the tasks in just over 50 minutes. Team Tartan Rescue's CHIMP robot came in third, completing all the tasks in just over 55 minutes. The second and third place teams took home $1 million and $500,000, respectively.

A self-driving car carrying a family of four on a rural two-lane highway spots a bouncing ball ahead. As the vehicle approaches a child runs out to retrieve the ball. Should the car risk its passengers' lives by swerving to the side--where the edge of the road meets a steep cliff? Or should the car continue on its path, ensuring its passengers' safety at the child's expense? This scenario and many others pose moral and ethical dilemmas that carmakers, car buyers and regulators must address before vehicles should be given full autonomy, according to a study published Thursday in Science.

Earlier this month, the nation watched as Watson, a computer system designed by IBM, drubbed the two all time champions of Jeopardy. It was a much more difficult challenge than, say, beating a grandmaster at chess. To win, Watson had to navigate the vagaries of human speech, the idioms, the puns, the cultural references -- all the things, in short, that make language delightful and deeply machine unfriendly. Journalist Stephen Baker spent a year behind the scenes, as the team of IBM engineers struggled to design and build Watson in time for the show. He tells the story of project Watson, and what it means for the future, in his new book, "Final Jeopardy: Man vs. Machine and the Quest to Know Everything."

A couple of years ago the directors of a women's clothing company asked me to help them develop better fashion recommendations for their clients. No one in their right mind would seek my personal advice in an area I know so little about--I am, after all, a male computer scientist--but they were not asking for my personal advice. They were asking for my machine-learning advice, and I obliged. Based purely on sales figures and client surveys, I was able to recommend to women whom I have never met fashion items I have never seen. My recommendations beat the performance of professional stylists.

Quantum information science is a young field, its underpinnings still being laid by a large number of researchers [see "Rules for a Complex Quantum World," by Michael A. Nielsen; Scientific American, November 2002]. Classical information science, by contrast, sprang forth about 50 years ago, from the work of one remarkable man: Claude E. Shannon. In a landmark paper written at Bell Labs in 1948, Shannon defined in mathematical terms what information is and how it can be transmitted in the face of noise. What had been viewed as quite distinct modes of communication--the telegraph, telephone, radio and television--were unified in a single framework. Shannon was born in 1916 in Petoskey, Michigan, the son of a judge and a teacher.

The brain performs its canonical task -- learning -- by tweaking its myriad connections according to a secret set of rules. To unlock these secrets, scientists 30 years ago began developing computer models that try to replicate the learning process. Now, a growing number of experiments are revealing that these models behave strikingly similar to actual brains when performing certain tasks. Researchers say the similarities suggest a basic correspondence between the brains' and computers' underlying learning algorithms. The algorithm used by a computer model called the Boltzmann machine, invented by Geoffrey Hinton and Terry Sejnowski in 1983, appears particularly promising as a simple theoretical explanation of a number of brain processes, including development, memory formation, object and sound recognition, and the sleep-wake cycle.