Human cues used to improve computer user-friendliness Lijun Yin wants computers to understand inputs from humans that go beyond the traditional keyboard and mouse. "Our research in computer graphics and computer vision tries to make using computers easier," says the Binghamton University computer scientist. "Can we find a more comfortable, intuitive and intelligent way to use the computer? It should feel like you're talking to a friend. This could also help disabled people use computers the way everyone else does." Yin's team has developed ways to provide information to the computer based on where a user is looking as well as through gestures or speech. To some extent, that's already possible. Yin says the next step would be enabling the computer to recognize a user's emotional state. "Computers only understand zeroes and ones," Yin says. He's partnering with Binghamton University psychologist Peter Gerhardstein to explore ways this work could benefit children with autism. Imagine if a computer could understand when people are in pain.
NCS — Neuromorphic Cognitive Systems Computational humor It is a relatively new area, with the first dedicated conference organized in 1996.[1] Joke generators[edit] Pun generation[edit] An approach to analysis of humor is classification of jokes. A further step is an attempt to generate jokes basing on the rules that underlie classification. Q: What is the difference between leaves and a car? A: One you bake and brush and rake, the other you rush and brake and bake. Q: What do you call a strange market? A: A bizarre bazaar. Since then the approach has been improved, and the latest report, dated 2007, describes the STANDUP joke generator, implemented in the Java programming language.[4][5] The STANDUP generator was tested on children within the framework of analyzing its usability for language skills development for children with communication disabilities, e.g., because of cerebral palsy. Other[edit] Stock and Strapparava described a program to generate funny acronyms.[9] Joke recognition[edit] Applications[edit] Related research[edit] See also[edit]
Artificial intelligence creeps nearer via bee algorithms and crowdsourcing Yet crowdsourcing can be extremely effective, as MIT's Riley Crane showed in answering DARPA's challenge to find 10 weather balloons moored around the US. The MIT team used social networks and a pyramid of financial incentives to recruit volunteers, their friends and their friends of friends to report sightings - and won by finding all 10 within nine hours. "Not all hard problems can be solved by aggregation," he said. "This is a toy problem," he said, "but it's still starting to show some of the possibilities of what we're going to be able to do in future." Other interesting approaches included the MIT Media Lab's Alexander Wissner-Gross, who argues that if a planet-scale superhuman intelligence emerges it will most likely be from either the quantitative finance or advertising industries. Exactly how much AI should resemble humans is a long-running debate. Yet for centuries, explained Sharon Bertsch McGrayne, author of The Theory That Wouldn't Die, mentioning Bayes was career suicide.
Memristor Processor Solves Mazes Memristors are the fourth fundamental building block of electronic circuits, after resistors, capacitors and inductors. They were famously predicted in the early 1970s but only discovered 30 years later at HP Labs in Palo Alto, California. Memristors are resistors that “remember” the state they were in, which changes according to the current passing through them. They are expected to revolutionise the design and capabilities of electronic circuits and may even make possible brain-like architectures in silicon, since neurons behave like memristors. Today, we see one of the first revolutionary circuits thanks to Yuriy Pershin at the University of South Carolina and Massimiliano Di Ventra at the University of California, San Diego, two pioneers in this field. Mazes are a class of graphical puzzles in which, given an entrance point, one has to find the exit via an intricate succession of paths, with the majority leading to a dead end, and only one, or few, correctly “solving” the puzzle.
Common Sense Computing Initiative | at the MIT Media Lab ePlayer - Progressive VOD Programmed DNA Robot Goes Where Scientists Tell It | Nanotechnology | LiveScience A tiny robot made from strands of DNA could pave the way for mini-machines that can dive into the human body to perform surgeries, among other futuristic applications. While DNA-based robots have been made before, this latest real-life micromachine is the first one that researchers have successfully programmed to follow instructions on where to move. Once assembled, the robot can take multiple steps without any outside help, according to lead researcher Andrew Turberfield, a professor at the University of Oxford. "Turberfield's group has figured out a beautiful way to automate the movement of a strand of DNA along a track," said William Sherman, an associate scientist at Brookhaven National Laboratory, who was not involved in the study. DNA bots When thinking about robots, many of us picture humanlike machines with metal moving parts, like Rosie from "The Jetsons." Enter the DNA molecule. Takes instruction well
Association for the Advancement of Artificial Intelligence Researchers Give Robots the Capability for Deceptive Behavior Georgia Tech Regents professor Ronald Arkin (left) and research engineer Alan Wagner look on as the black robot deceives the red robot into thinking it is hiding down the left corridor. (Click image for high-resolution version. Credit: Gary Meek) A robot deceives an enemy soldier by creating a false trail and hiding so that it will not be caught. While this sounds like a scene from one of the Terminator movies, it’s actually the scenario of an experiment conducted by researchers at the Georgia Institute of Technology as part of what is believed to be the first detailed examination of robot deception. We have developed algorithms that allow a robot to determine whether it should deceive a human or other intelligent machine and we have designed techniques that help the robot select the best deceptive strategy to reduce its chance of being discovered,” said Ronald Arkin, a Regents professor in the Georgia Tech School of Interactive Computing.
International Society of Artificial Life iCub RobotCub ~ Official Site