For more than 50 years, language scientists have assumed that sentence structure is fundamentally hierarchical, made up of small parts in turn made of smaller parts, like Russian nesting dolls. A new Cornell study suggests language use is simpler than they had thought. Co-author Morten Christiansen, Cornell professor of psychology and co-director of the Cornell Cognitive Science Program, and his colleagues say that language is actually based on simpler sequential structures, like clusters of beads on a string. "What we're suggesting is that the language system deals with words by grouping them into little clumps that are then associated with meaning," he said. Sentences are made up of such word clumps, or "constructions," that are understood when arranged in a particular order. For example, the word sequence "bread and butter" might be represented as a construction, whereas the reverse sequence of words ("butter and bread") would likely not.

The idea of Artificial Intelligence has captured our collective imagination for decades. Can behaviour that we think of as intelligent be replicated in a machine? If so, what consequences could this have for society? And what does it tell us about ourselves as human beings? Besides being a topic in science fiction and popular philosophy, AI is also a well-established area of scientific research.

A software program tracks your emotions as you watch a movie, creating an emotion catalog. Plenty of human-gadget interfaces can let you control a robot or a computer with your mind. But these communications are command-based -- your PR2 still can't tell whether you're asking it for a beer to celebrate, or to drink away your sorrows. An EEG-based affective computing system allows you to communicate your emotions, adding a new layer to human-computer interactions. In the video below, Robert Oschler of Android Review demonstrates EmoRate, a software program that catalogs his emotions.

Denton initially shod the Mantis in modified go-kart tires. "They worked out really well," he notes, "but they weren't very grippy." So he fabricated custom rubber feet, modeling the hexagonal pattern after off-road tires. Now he alternates shoes based on the terrain. In 2007, Matt Denton stopped on the side of the road near his home in Hampshire, England, to watch an excavator dig.

Deep in the bowels of Google's Google Brain, the company is asking the question: can neural networks create music? Google's Magenta project has an answer: yes. Whether that music is good, however... Google may have annihilated its human competition in the ancient game of Go, but human artists don't have to worry--yet--about losing their livelihoods. Still, it appears that Magenta could at least drop a riff or two that human artists could remix. Want to hear it for yourself?

Intel is in transition right now: An executive shakeup this month laid the path for new boss Venkata Renduchintala to put his imprint on the company's PC, Internet of Things and software operations. So no wonder the vibe at this week's Intel Developer Forum in Shenzhen was mellow. Intel kept the show a low-key affair, choosing not to bring it to the attention of a worldwide audience, unlike previous years. But IDF Shenzhen remains an important event on Intel's calendar. China is a huge market, and it's also a place where the chip maker encourages small hardware shops in the alleys of Shenzhen to experiment with PC, mobile and now, IoT ideas.

WASHINGTON–Superintelligent computers could outsmart humans, but scientists largely dismiss any parallels to Terminator and a dystopian "rise of the machines" (much like the hapless scientists in the movies, it must be noted). The struggle between the thirst for research and the anxiety over the consequences was clear from "Are Super Intelligent Computers Really A Threat to Humanity?" a panel discussion held at the Information Technology and Innovation Foundation Tuesday morning. The risks of rogue machinery are not far off from the cautionary tales played out in movies including Metropolis, 2001: A Space Odyssey, Terminator of course, and most recently, Ex Machina. According to Stuart Russell of U.C. Berkeley, "if the system is better than you at taking into account more information and looking further ahead into the future, and it doesn't have the exactly the same goals as you…then you have a problem." A superintelligent computer could avoid being shut down by its creators, and that's when people might lose control of the machine, Russell warned.

The goal of this project is to use Artificial Intelligence methods to study the phenomenon of expressive music performance. The focus of the project is on developing and using machine learning and data mining methods for the analysis of expressive performance data. The goal is to gain a deeper understanding of this complex domain of human competence and to contribute new methods to the (relatively new) branch of musicology that tries to develop quantitative models and theories of musical expression. By musical expression, we mean the variations in tempo, timing, dynamics, articulation, etc. that performers apply when playing and interpreting'' a piece. Our goal is to study real expressive performances with machine learning methods, in order to discover some fundamental patterns or principles that characterize sensible'' musical performances, and to elucidate the relation between structural aspects of the music and typical or musically sensible'' performance patterns.

Robots are composed of several systems working together: the controller is the robot's brain, which controls its movements; the body is the robot's physical appearance related to the job it performs; mobility, or how the robot moves, depends on the job it performs (for example, a robot uses propellers and rudders in the water, and legs or wheels on land); power is used to fuel the robot (electric solar cells are one example, such as the solar-powered robots described here); sensors provide signals to give robots a perceptual understanding of their environment so they can alter their behavior based on that information; and tools are unique to the task the robot performs. Just as robots are made of several systems, the field of AI requires a collaboration of many different disciplines to be successful. Engineering is clearly useful, but I know people who have a background in biology, psychology, physics, and computer science. What's most important is a willingness to learn a lot of new things from a variety of disciplines.

Welcome to the robotic age. Long-term federal investments in fundamental science and engineering research, and the researchers who pursue them, have led to novel machines that safely partner with people in nearly every environment. Soon, helping hands are as likely to be made of metal and plastic as flesh and bone.