In complex crisis situations teams of experts must often make difficult decisions within a narrow time frame. However, voluminous amounts of information and the complexity of distributed cognition can hamper the quality and timeliness of decision-making by human teams and lead to catastrophic consequences. A Penn State Univ. team has devised a system that merges human and computer intelligence to support decision-making.
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. Massachusetts Institute of Technology 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.
Now that the Internet’s basic protocols are more than 30 years old, network scientists are increasingly turning their attention to ad hoc networks where unsolved problems still abound. Most theoretical analyses of ad hoc networks have assumed that the communications links within the network are stable. But that often isn’t the case with real-world wireless devices.
Debi Green is trying to book a vacation, but she's having a hard time getting the words out. Even though it's been nearly nine years since she suffered a stroke, language sometimes fails her. Luckily, the computerized travel agent has all the time in the world. It's an avatar being tested at Temple Univ. in Philadelphia, where researchers are working to develop a virtual speech therapist.
In a pair of recent papers, researchers at Massachusetts Institute of Technology have demonstrated that, for a few specific tasks, it’s possible to write computer programs using ordinary language rather than special-purpose programming languages. The work may be of some help to programmers, and it could let non-programmers manipulate common types of files in ways that previously required familiarity with programming languages.
Honda's robotics technology, although among the most advanced for mobility, has come under fire as lacking practical applications and being little more than an expensive toy. The latest example is its walking, talking interactive Asimo robot, which is now acting as a museum guide in Tokyo. In addition to glitches that have interrupted its operation, it lacks voice recognition.
Each summer, power grids are pushed to their limits. A single failure in the system can cause power outages throughout a neighborhood or across towns. To help prevent smaller incidents from snowballing into massive power failures, researchers devised an algorithm that identifies the most dangerous pairs of failures among the millions possible in a power grid.
The world's first space conversation experiment between a robot and humans is ready to be launched. Developers from the Kirobo project, named after "kibo" or hope in Japanese and "robot," gathered in Tokyo Wednesday to demonstrate the humanoid robot's ability to talk.
Researchers at Massachusetts Institute of Technology have developed a new algorithm that can accurately measure the heart rates of people depicted in ordinary digital video by analyzing imperceptibly small head movements that accompany the rush of blood caused by the heart’s contractions.
A team of researchers has developed a new encryption scheme, known as a functional-encryption scheme, that solves a major problem with homomorphic encryption. The scheme would let the cloud server to run a single, specified computation on the homomorphically encrypted result, without being able to extract any other information about it.
Germany's defense minister on Wednesday admitted mistakes were made in the handling of a program to develop unmanned surveillance drones and announced tougher oversight procedures for all armament projects. Opposition parties say Thomas de Maiziere wasted public funds by canceling the botched 600 million euro ($800 million) program too late, but he rejected calls for his resignation.
Reinforcement learning is a technique in which a computer system learns how best to solve some problem through trial-and-error. Classic applications of reinforcement learning involve problems like robot navigation and automated surveillance. Now, researchers have developed a new reinforcement-learning algorithm that, for many problems, allows computer systems to find solutions much more efficiently than previous algorithms did.
Researchers from North Carolina State University have developed a software algorithm that detects and isolates cyberattacks on networked control systems—which are used to coordinate transportation, power, and other infrastructure across the United States.
You get into your car and ask it to get you home in time for the start of the big game, stopping off at your favorite Chinese restaurant on the way for takeout. But the car informs you that the road past the Chinese restaurant is closed for repairs, and you will have to choose a different place. You select a nearby Korean restaurant from the options the car suggests. Autonomous devices could soon collaborate with humans in this way.
Whether reaching for a book out of a cluttered cabinet or pruning a bush in the backyard, a person’s arm frequently makes contact with objects during everyday tasks. Animals do it too, when foraging for food, for example. Much in the same way, robots are now able to intelligently maneuver within clutter, gently making contact with objects while accomplishing a task. This new control method has wide applications.
Surgical robots could make some types of surgery safer and more effective, but proving that the software controlling these machines works as intended is problematic. Researchers at Carnegie Mellon University and Johns Hopkins University have demonstrated that methods for reliably detecting software bugs and ultimately verifying software safety can be applied successfully to this breed of robot.
Researchers in the U.K. have been working to program a group of 40 robots to carry out simple fetching and carrying tasks, by grouping around an object and working together to push it across a surface. Even after being scattered, the robots can group again and organize themselves by order of priority. The team says the ability to control robot swarms could prove hugely beneficial in a range of contexts, from military to medical.
Robot butlers that tidy your house or cook you a meal have long been the dream of science-fiction writers and artificial intelligence researchers alike. But if robots are ever going to move effectively around our constantly changing homes or workspaces performing such complex tasks, they will need to be more aware of their own limitations, according to researchers at Massachusetts Institute of Technology.
When a robot is moving one of its limbs through free space, its behavior is well described by a few simple equations. But as soon as it strikes something solid, those equations break down. Roboticists typically use ad hoc control strategies to negotiate collisions and then revert to their rigorous mathematical models when the robot begins to move again. Researchers at Massachusetts Institute of Technology are hoping to change that, with a new mathematical framework that unifies the analysis of both collisions and movement through free space.
When Georgia Tech opens the doors to the Georgia Dome next month as the host institution for the 2013 Final Four, expect third-seeded Florida to walk out as the national champion. That's the prediction from Georgia Tech's Logistic Regression/Markov Chain (LRMC) college basketball ranking system, a computerized model that has chosen the men's basketball national champ in three of the last five years.
Memristors are made of fine nanolayers and can be used to connect electric circuits and for several years have been considered to be the electronic equivalent of the synapse. A researcher in Germany, physicist Andy Thomas, is now using his memristors as key components for his blueprint for an artificial brain.
Many commercial robotic arms perform what roboticists call "pick-and-place" tasks: The arm picks up an object in one location and places it in another. Usually, the objects are positioned so that the arm can easily grasp them; the appendage that does the grasping may even be tailored to the objects' shape. General-purpose household robots, however, would have to be able to manipulate objects of any shape, left in any location. And today, commercially available robots don't have anything like the dexterity of the human hand. Until now.
Ancient languages hold a treasure trove of information about the culture, politics and commerce of millennia past. Yet, reconstructing them to reveal clues into human history can require decades of painstaking work. Now, scientists at the University of California, Berkeley, have created an automated “time machine,” of sorts, that will greatly accelerate and improve the process of reconstructing hundreds of ancestral languages.
Research at the University of Gothenburg and Chalmers University of Technology has resulted in a new type of machine that sorts used batteries by means of artificial intelligence (AI). One machine is now being used in the U.K., sorting one-third of the country's recycled batteries.
Stanford University researchers have designed the fastest, most accurate algorithm yet for brain-implantable prosthetic systems that can help disabled people maneuver computer cursors with their thoughts. The algorithm's speed, accuracy, and natural movement approach those of a real arm, doubling performance of existing algorithms.