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.

Guy Bresler joined the MIT faculty in September 2015 as the Bonnie and Marty (1964) Tenenbaum Career Development Professor in the Department of Electrical Engineering and Computer Science (EECS). He also joined the Institute for Data, Systems, and Society (IDSS) -- which addresses complex societal challenges by advancing education and research at the intersection of statistics, data science, information and decision systems, and social sciences -- as a member of the Laboratory for Information and Decision Systems (LIDS). Bresler's research investigates the relationship between combinatorial structure and computational tractability of high-dimensional inference in graphical models and other statistical models. His current work focuses on learning graphical models from data, and explores how both data and computation requirements can be reduced if the model is subsequently used for a specific inference task. Bresler is also interested in applications of these methods, especially to recommendation systems and computational biology.

Edward S. Boyden, a professor of media arts and sciences, biological engineering, and brain and cognitive sciences at MIT, has won the BBVA Foundation Frontiers of Knowledge Award in Biomedicine for his role in the development of optogenetics, a technique for controlling brain activity with light. Gero Miesenböck of the Oxford University and Karl Deisseroth of Stanford University were also honored with the prize for their role in developing and refining the technique. The BBVA Foundation Frontiers of Knowledge Awards are given annually for "outstanding contributions and radical advances in a broad range of scientific, technological, and artistic areas." The 400,000-Euro prize in the category of biomedicine will be shared among the three neuroscientists. "If we imagine the brain as a computer, optogenetics is a keyboard that allows us to send extremely precise commands," says Boyden, a faculty member at the MIT Media Lab with a joint appointment at MIT's McGovern Institute for Brain Research.

A new study from MIT reveals that a brain region dedicated to reading has connections for that skill even before children learn to read. By scanning the brains of children before and after they learned to read, the researchers found that they could predict the precise location where each child's visual word form area (VWFA) would develop, based on the connections of that region to other parts of the brain. Neuroscientists have long wondered why the brain has a region exclusively dedicated to reading -- a skill that is unique to humans and only developed about 5,400 years ago, which is not enough time for evolution to have reshaped the brain for that specific task. The new study suggests that the VWFA, located in an area that receives visual input, has pre-existing connections to brain regions associated with language processing, making it ideally suited to become devoted to reading. "Long-range connections that allow this region to talk to other areas of the brain seem to drive function," says Zeynep Saygin, a postdoc at MIT's McGovern Institute for Brain Research.

MIT President L. Rafael Reif announced today a significant expansion of the Institute's programs in learning research and online and digital education -- from pre-kindergarten through residential higher education and lifelong learning -- that fulfills a number of recommendations made in 2014 by the Institute-Wide Task Force on the Future of MIT Education. Most notably, Reif announced the creation of the MIT Integrated Learning Initiative (MITili), to be led by Professor John Gabrieli, and a new effort to increase MIT's ability to improve science, technology, engineering, and mathematics (STEM) learning by students from pre-kindergarten through high school (pK-12), to be led by Professor Angela Belcher. The announcement also included a program to support faculty innovations in MIT residential education and new work to enhance MIT's continuing education programs. In keeping with the high priority of these new efforts and of the entire field of digital learning, Professor Sanjay Sarma, now dean of digital learning, will oversee them in the newly created position of vice president for open learning, reporting directly to Reif. Chancellor Cynthia Barnhart, who will share responsibility with Sarma for several aspects of this work, predicts that the programs announced today will have "far-reaching and tremendous implications for education -- for MIT students as well as for students not at MIT."

MIT researchers are developing a computer system that uses genetic, demographic, and clinical data to help predict the effects of disease on brain anatomy. In experiments, they trained a machine-learning system on MRI data from patients with neurodegenerative diseases and found that supplementing that training with other patient information improved the system's predictions. "This is the first paper that we've ever written on this," says Polina Golland, a professor of electrical engineering and computer science at MIT and the senior author on the new paper. "Our goal is not to prove that our model is the best model to do this kind of thing; it's to prove that the information is actually in the data. So what we've done is, we take our model, and we turn off the genetic information and the demographic and clinical information, and we see that with combined information, we can predict anatomical changes better."

NASA announced today that MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) is one of two university research groups nationwide that will receive a 6-foot, 290-pound humanoid robot to test and develop for future space missions to Mars and beyond. A group led by CSAIL principal investigator Russ Tedrake will develop algorithms for the robot, known as "Valkyrie" or "R5," as part of NASA's upcoming Space Robotics Challenge, which aims to create more dexterous autonomous robots that can help or even take the place of humans "extreme space" missions. Tedrake's team, which was selected from groups that were entered in this year's Defense Advanced Research Projects Agency (DARPA) Robotics Challenge, will receive as much as $250,000 a year for two years from NASA's Space Technology Mission Directive. NASA says it is interested in humanoid robots because they can help or even replace astronauts working in extreme space environments. Robots like R5 could be used in future missions either as precursor robots performing mission tasks before humans arrive or as human-assistive robots collaborating with the human crew.

When the eyes are open, visual information flows from the retina through the optic nerve and into the brain, which assembles this raw information into objects and scenes. Scientists have previously hypothesized that objects are distinguished in the inferior temporal (IT) cortex, which is near the end of this flow of information, also called the ventral stream. A new study from MIT neuroscientists offers evidence that this is indeed the case. Using data from both humans and nonhuman primates, the researchers found that neuron firing patterns in the IT cortex correlate strongly with success in object-recognition tasks. "While we knew from prior work that neuronal population activity in inferior temporal cortex was likely to underlie visual object recognition, we did not have a predictive map that could accurately link that neural activity to object perception and behavior. The results from this study demonstrate that a particular map from particular aspects of IT population activity to behavior is highly accurate over all types of objects that were tested," says James DiCarlo, head of MIT's Department of Brain and Cognitive Sciences, a member of the McGovern Institute for Brain Research, and senior author of the study, which appears in the Journal of Neuroscience.

With the Modular Prosthetic Limb, researchers from Johns Hopkins University Applied Physics Lab have successfully demonstrated the possibilities of controlling artificial limbs simply by thought. A 26-year-old man who was paralyzed in both legs has regained the ability to walk using a system controlled by his brain waves, along with a harness to help support his body weight, a new study says. In order to walk, the patient wore a cap with electrodes that detected his brain signals. These electrical signals -- the same as those a doctor looks at when running an electroencephalogram (EEG) test -- were sent to a computer, which "decoded" the brain waves. It then used them to send instructions to another device that stimulated the nerves in the man's legs, causing the muscles to move.

Three of the contenders, from left to right: Virginia Tech's THOR, DARPA's test platform robot made by Boston Dynamics and Raytheon's Guardian. Scientists have invented a machine that imitates the way the human brain learns new information, a step forward for artificial intelligence, researchers reported. The system described in the journal Science is a computer model "that captures humans' unique ability to learn new concepts from a single example," the study said. "Though the model is only capable of learning handwritten characters from alphabets, the approach underlying it could be broadened to have applications for other symbol-based systems, like gestures, dance moves, and the words of spoken and signed languages." Joshua Tenenbaum, a professor at the Massachusetts Institute for Technology (MIT), said he wanted to build a machine that could mimic the mental abilities of young children.