Topics covered include: representation and inference in first-order logic, modern deterministic and decision-theoretic planning techniques, basic supervised learning methods, and Bayesian network inference and learning. This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5504 (Techniques in Artificial Intelligence).

It's not a vulture or crow but a Falcon--a new brand of unmanned aerial vehicle, or drone, and Johnson is flying it. The sheriff's office here in Mesa County, a plateau of farms and ranches corralled by bone-hued mountains, is weighing the Falcon's potential for spotting lost hikers and criminals on the lam. A laptop on a table in front of Johnson shows the drone's flickering images of a nearby highway. Standing behind Johnson, watching him watch the Falcon, is its designer, Chris Miser. Rock-jawed, arms crossed, sunglasses pushed atop his shaved head, Miser is a former Air Force captain who worked on military drones before quitting in 2007 to found his own company in Aurora, Colorado. The Falcon has an eight-foot wingspan but weighs just 9.5 pounds.

As more than 90 students in MIT's SuperUROP Advanced Undergraduate Research Opportunites Program flowed into a year-end certificates presentation event on May 7 at the Media Lab Skyline Room, they looked quite ready to celebrate a year of work on intense research collaborations with faculty advisors, supported as by the generous backing of industry and private sponsors. Senior Ava Soleimany says she has gained a "much-nuanced understanding of what exactly research involves." With an interest in the computational aspects of synthetic biology and its medical applications, Soleimany applied to be a SuperUROP in spring 2014 in Professor Timothy Lu's Synthetic Biology Group. She was able to extend her existing Undergraduate Research Opportunities Program (UROP) project into a SuperUROP. "It allowed me to hit the ground running in the fall while many of my peers were still sorting out the details of their projects," she notes.

Projects that target aid toward villages and rural areas in the developing world often face time-consuming challenges, even at the most basic level of figuring out where the most appropriate sites are for pilot programs or deployment of new systems such as solar-power for regions that have no access to electricity. Often, even the sizes and locations of villages are poorly mapped, so time-consuming field studies are needed to locate suitable sites. Now, a team of graduate students at MIT and a social-service group of data scientists have come up with a way of automating parts of that evaluation process, by developing software that can identify houses and even types of houses from readily-available satellite imagery -- potentially saving considerable time that would otherwise be spent sending teams from village to village. Their findings have now been published in the journal Big Data. The multidisciplinary team came together in the course of discussions at MIT's Sidney Pacific graduate dormitory, explains team member Brian Spatocco: "We started talking about this problem, and we realized we all had skills that were relevant."

The average person receives upwards of 150 emails a day, and it often seems like no amount of tagging or filtering can close the floodgates. One major source of stress is the never-ending conversation threads made possible by group emails. Mailing lists can be a fantastic medium for substantive discussions, but often they deliver too much of what we don't want and not enough of what we do. Believe it or not, such tools have barely changed since the pre-Internet days of Arpanet 40 years ago: You either opt in or opt out, you get dozens of irrelevant emails, and the views of a few loudmouths usually end up drowning out the rest. In an age of Facebook and Reddit, users expect a sense of control over how they consume their content, and yet that control and personalization often doesn't extend to their own inboxes.

For the last decade, scientists have deployed increasingly capable underwater robots to map and monitor pockets of the ocean to track the health of fisheries, and survey marine habitats and species. In general, such robots are effective at carrying out low-level tasks, specifically assigned to them by human engineers -- a tedious and time-consuming process for the engineers. When deploying autonomous underwater vehicles (AUVs), much of an engineer's time is spent writing scripts, or low-level commands, in order to direct a robot to carry out a mission plan. Now a new programming approach developed by MIT engineers gives robots more "cognitive" capabilities, enabling humans to specify high-level goals, while a robot performs high-level decision-making to figure out how to achieve these goals. For example, an engineer may give a robot a list of goal locations to explore, along with any time constraints, as well as physical directions, such as staying a certain distance above the seafloor.

For several years now, the research groups of MIT professors of computer science and engineering William Freeman and Frédo Durand have been investigating techniques for amplifying movements captured by video but indiscernible to the human eye. Versions of their algorithms can make the human pulse visible and even recover intelligible speech from the vibrations of objects filmed through soundproof glass. Earlier this month, at the Computer Vision and Pattern Recognition conference, Freeman, Durand, and colleagues at the Qatar Computing Research Institute (QCRI) presented a new version of the algorithm that can amplify small motions even when they're contained within objects executing large motions. So, for instance, it could make visible the precise sequence of muscle contractions in the arms of a baseball player swinging the bat, or in the legs of a soccer player taking a corner kick. "The previous version of the algorithm assumed everything was small in the video," Durand says.

Today, the National Institutes of Health (NIH) announced the first round of Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative award recipients, including several MIT interdisciplinary teams. The BRAIN Initiative, spearheaded by President Obama in April 2013, challenges the nation's leading scientists to advance our sophisticated understanding of the human mind and discover new ways to treat, prevent, and cure neurological disorders like Alzheimer's, schizophrenia, autism, and traumatic brain injury. "The human brain is one of the most complicated structures in the known universe," said NIH Director Francis Collins. "We have an unprecedented opportunity to develop new technologies that will allow us to map the circuits of the brain, measure activity within those circuits, and understand how their interactions maintain health and modulate human behavior." With the NIH Brain Initiative, the scientific community is charged with accelerating the invention of cutting-edge technologies that can produce dynamic images of complex neural circuits and illuminate the interaction of lightning-fast brain cells.

Seth Teller, a professor of computer science and engineering at MIT who was well known for his efforts to advance human-robot interactions, died yesterday. He was 50, and he had been a member of the MIT faculty since 1994. President L. Rafael Reif announced the news in an email to the MIT community. "I knew Seth as a person of great human warmth and intellectual intensity," Reif wrote in his letter. "He was a brilliant engineer and a gifted advisor with a passion for new challenges. His loss is difficult to grasp."

Suppose you're trying to navigate an unfamiliar section of a big city, and you're using a particular cluster of skyscrapers as a reference point. Traffic and one-way streets force you to take some odd turns, and for a while you lose sight of your landmarks. When they reappear, in order to use them for navigation, you have to be able to identify them as the same buildings you were tracking before -- as well as your orientation relative to them. That type of re-identification is second nature for humans, but it's difficult for computers. At the IEEE Conference on Computer Vision and Pattern Recognition in June, MIT researchers will present a new algorithm that could make it much easier, by identifying the major orientations in 3-D scenes.