Researchers at MIT have proven that the brain's cortex doesn't process specific tasks in highly specialized modules -- showing that the cortex is, in fact, quite dynamic when sharing information. Previous studies of the brain have depicted the cortex as a patchwork of function-specific regions. Parts of the visual cortex at the back of the brain, for instance, encode color and motion, while specific frontal and middle regions control more complex functions, such as decision-making. Neuroscientists have long criticized this view as too compartmentalized. In a paper published today in Science, the researchers from the Picower Institute for Learning and Memory at MIT show that, indeed, multiple cortical regions work together simultaneously to process sensorimotor information -- sensory input coupled with related actions -- despite their predetermined specialized roles.

The sequencing of the human genome laid the foundation for the study of genetic variation and its links to a wide range of diseases. But the genome itself is only part of the story, as genes can be switched on and off by a range of chemical modifications, known as "epigenetic marks." Now, a decade after the human genome was sequenced, the National Institutes of Health's Roadmap Epigenomics Consortium has created a similar map of the human epigenome. Manolis Kellis, a professor of computer science and a member of MIT's Computer Science and Artificial Intelligence Laboratory and of the Broad Institute, led the effort to integrate and analyze the datasets produced by the project, which constitute the most comprehensive view of the human epigenome to date. In a paper published today in the journal Nature, Kellis and his colleagues report 111 reference human epigenomes and study their regulatory circuitry, in a bid to understand their role in human traits and diseases.

A team from MIT Lincoln Laboratory's Bioengineering Systems and Technologies Group was named a first-place subchallenge winner at the 2014 Audio/Visual Emotion Challenge and Workshop (AVEC 2014), the fourth annual competition that invites participants to use multimedia processing and machine learning to analyze subjects' emotional states or estimate subjects' level of depression. Held at the annual Association for Computing Machinery (ACM) International Conference on Multimedia, the challenge gauges the success of entrants' approaches to automated emotion detection on a set of common benchmarks. In 2014, two subchallenges were presented: continuously distinguishing emotions and estimating the level of subjects' depression from audio and visual data. Of the 14 groups competing in the 2014 depression assessment subchallenge, Lincoln Laboratory's team was the most successful in predicting a depression score. Participants in this subchallenge estimate the severity of subjects' depression from either vocal characteristics detected in audio or facial signs identified in video recordings, or both.

In the thick, dry grass of the Paso de Cortés mountain pass in Mexico, MIT's Earth Signals and Systems (ESS) group struggled to launch a small aircraft high up into the volcanic plume rising steadily from Popocatépetl. It was a windy day, and nothing was going right. Then the volcano began to erupt. "It sounded like a big engine going off, a loud boom," says Sai Ravela, principal research scientist in the MIT Department of Earth, Atmospheres, and Planetary Science. Their escort from CENAPRED, Mexico's federal disaster prevention agency, told them to drop everything and leave.

The late professor Seth Teller created 6.811 (Principles and Practices in Assistive Technologies, or PPAT) in the fall of 2011. Through his extensive experience developing assistive technologies (AT) at MIT, his compassion for making technology available to all, and his innovative approach and drive to build this class, student interest in PPAT and AT has grown steadily since. Following Teller's untimely death on July 1 this year, a group of former PPAT and AT students including his graduate student William Li SM '12, who TA'd the inaugural PPAT offering; Grace Teo PhD '14, a former student and member of the MIT Assistive Technology Club; and a core group of students who took the class in 2013 have formed a team to continue Teller's legacy through both the coninuation of PPAT and an outgrowth known as "AT Hack," a one-day workshop launched in spring 2014. Li and Teo, who will co-instruct this year's class, and three other members of the team will work with Professor Rob Miller, MIT MacVicar Faculty Fellow, member of the Computer Science and Artificial Intelligence Lab (CSAIL), and co-education officer of the Department of Electrocal Engineering and Computer Science (EECS). Every year since the inaugural offering of PPAT, Miller had worked with Teller to help develop and teach the course.

For decades, neuroscientists have been trying to design computer networks that can mimic visual skills such as recognizing objects, which the human brain does very accurately and quickly. Until now, no computer model has been able to match the primate brain at visual object recognition during a brief glance. However, a new study from MIT neuroscientists has found that one of the latest generation of these so-called "deep neural networks" matches the primate brain. Because these networks are based on neuroscientists' current understanding of how the brain performs object recognition, the success of the latest networks suggest that neuroscientists have a fairly accurate grasp of how object recognition works, says James DiCarlo, a professor of neuroscience and head of MIT's Department of Brain and Cognitive Sciences and the senior author of a paper describing the study in the Dec. 18 issue of the journal PLoS Computational Biology. "The fact that the models predict the neural responses and the distances of objects in neural population space shows that these models encapsulate our current best understanding as to what is going on in this previously mysterious portion of the brain," says DiCarlo, who is also a member of MIT's McGovern Institute for Brain Research.

Einstein's theory of relativity envisions time as a spatial dimension, like height, width, and depth. But unlike those other dimensions, time seems to permit motion in only one direction: forward. This directional asymmetry -- the "arrow of time" -- is something of a conundrum for theoretical physics. An international group of computer scientists believes that the answer is yes. At the IEEE Conference on Computer Vision and Pattern Recognition this month, they'll present a new algorithm that can, with roughly 80 percent accuracy, determine whether a given snippet of video is playing backward or forward.

The Turing Test, developed by mathematician and legendary wartime codebreaker Alan Turing to test a machine's ability to exhibit intelligent behaviour, is the subject of an opera by Scottish composer Julian Wagstaff, which will embark on a UK tour in October 2012. The Turing Test is set in the near future and tells the fictional story of a brilliant young PhD student named Stephanie, who is trapped in a bitter battle between two rival scientists racing to build the world's first truly intelligent computer. The opera, which received critical acclaim at the Edinburgh Festival Fringe in 2007, is one hour long, and is scored for six voices and a small orchestra. It is sung in English, with one of the six singers playing the part of the computer. The tour marks the hundredth anniversary year of the birth of Alan Turing (1912–1954), who is widely considered to be the father of modern computing. The concept of a computer being able to imitate a human being was first expressed in a paper by Turing entitled "Computing Machinery and Intelligence", published in 1950 in the journal Mind.

McCarthy created the term "artificial intelligence" and was a towering figure in computer science at Stanford most of his professional life. In his career, he developed the programming language LISP, played computer chess via telegraph with opponents in Russia and invented computer time-sharing. In 1966, John McCarthy hosted a series of four simultaneous computer chess matches carried out via telegraph against rivals in Russia. John McCarthy, a professor emeritus of computer science at Stanford, the man who coined the term "artificial intelligence" and subsequently went on to define the field for more than five decades, died suddenly at his home in Stanford in the early morning Monday, Oct. 24. McCarthy was a giant in the field of computer science and a seminal figure in the field of artificial intelligence.

As he looks down into its big, dark eyes, it turns its head towards him and blinks, looking contented as it curls a bony white finger around his hand. But the "baby" is not human. And it looks more like Gollum from "The Lord Of The Rings" wearing a hemp romper suit, than a gurgly infant. Meet Heart Robot, a flexible, plastic puppet with robotic features that has been programmed to react to sound, touch and nearby movements. Heart Robot, so called because its red "heart" is visible on the left side of its body and beats at different rates, is certainly getting more attention than its menacing-looking counterpart, iC Hexapod, nearby.