An era of rapid evolution of structures and devices driven by new capabilities in machine learning, nanoscale experiments, and economic modeling is unfolding, MIT materials researchers revealed during the annual Industrial Liaison Program (ILP) Research and Development Conference. Pointing to progress in areas as diverse as biomedical devices, computing, and energy, Carl Thompson, director of the MIT Materials Research Laboratory and the Stavros Salapatas Professor of Materials Science and Engineering, noted the convergence of advances in nanoscale imaging and computerized prediction of materials structure and behavior with analysis of the likelihood of success in the marketplace. A longstanding problem with green energy sources, including solar and wind, is their power production varies widely and is often mismatched to demand. Thompson noted the work of Jessika Trancik, an associate professor of energy studies who has identified the economic value of various energy storage methods based on their relative costs. These methods include compressed air, pumped water, and vanadium-based flow batteries, in addition to traditional cell-type batteries such as nickel-cadmium, lithium ion and sodium-sulfur combinations.
Machine learning algorithms now underlie much of the software we use, helping to personalize our news feeds and finish our thoughts before we're done typing. But as artificial intelligence becomes further embedded in daily life, expectations have risen. Before autonomous systems fully gain our confidence, we need to know they are reliable in most situations and can withstand outside interference; in engineering terms, that they are robust. We also need to understand the reasoning behind their decisions; that they are interpretable. Aleksander Madry, an associate professor of computer science at MIT and a lead faculty member of the Computer Science and Artificial Intelligence Lab (CSAIL)'s Trustworthy AI initiative, compares AI to a sharp knife, a useful but potentially-hazardous tool that society must learn to weild properly.
MIT researchers have invented a way to fabricate nanoscale 3-D objects of nearly any shape. "It's a way of putting nearly any kind of material into a 3-D pattern with nanoscale precision," says Edward Boyden, the Y. Eva Tan Professor in Neurotechnology and an associate professor of biological engineering and of brain and cognitive sciences at MIT. Using the new technique, the researchers can create any shape and structure they want by patterning a polymer scaffold with a laser. After attaching other useful materials to the scaffold, they shrink it, generating structures one thousandth the volume of the original. These tiny structures could have applications in many fields, from optics to medicine to robotics, the researchers say.
Whether it's the pleasant experience of returning one's childhood home over the holidays or the unease of revisiting a site that proved unpleasant, we often find that when we return to a context where an episode first happened, specific and vivid memories can come flooding back. In a new study in the journal Neuron, scientists from the Picower Institute for Learning and Memory at MIT are reporting the discovery of a mechanism the brain may be employing to make that phenomenon occur. "Suppose you are driving home in the evening and encounter a beautiful orange twilight in the sky, which reminds you of the great vacation you had a few summers ago at a Caribbean island," says study senior author Susumu Tonegawa, the Picower Professor of Biology and Neuroscience at MIT. "This initial recall could be a general recall of the vacation. But moments later, you may get reminded of details of some specific events or situations that took place during the vacation which you had not been thinking about." At the heart of that second stage of recall, where specific details are suddenly vividly available, is a change in the electrical excitability of what researchers call "engram cells" -- the ensemble of neurons that together encode a memory through the specific pattern of their connection.
Small imperfections in a wine glass or tiny creases in a contact lens can be tricky to make out, even in good light. In almost total darkness, images of such transparent features or objects are nearly impossible to decipher. But now, engineers at MIT have developed a technique that can reveal these "invisible" objects, in the dark. In a study published today in Physical Review Letters, the researchers reconstructed transparent objects from images of those objects, taken in almost pitch-black conditions. They did this using a "deep neural network," a machine-learning technique that involves training a computer to associate certain inputs with specific outputs -- in this case, dark, grainy images of transparent objects and the objects themselves.
The following news article is adapted from a press release issued by Caltech, in partnership with the MIT School of Science, the Naval Postgraduate School, and the Jet Propulsion Laboratory. Facing the certainty of a changing climate coupled with the uncertainty that remains in predictions of how it will change, scientists and engineers from across the country are teaming up to build a new type of climate model that is designed to provide more precise and actionable predictions. Leveraging recent advances in the computational and data sciences, the comprehensive effort capitalizes on vast amounts of data that are now available and on increasingly powerful computing capabilities both for processing data and for simulating the Earth system. The new model will be built by a consortium of researchers led by Caltech, in partnership with MIT; the Naval Postgraduate School (NPS); and the Jet Propulsion Laboratory (JPL), which Caltech manages for NASA. The consortium, dubbed the Climate Modeling Alliance (CliMA), plans to fuse Earth observations and high-resolution simulations into a model that represents important small-scale features, such as clouds and turbulence, more reliably than existing climate models.
This fall, a team of four students in MIT's course 6.811 (Principles and Practices of Assistive Technology, or PPAT) designed a device that will help Pauline Dowell, a legally blind MIT employee, sail more independently. PPAT is a collaboration between the Harvard-MIT Program in Health Sciences and Technology and the departments of Mechanical Engineering and Electrical Engineering and Computer Science. An upper-level class, it draws students from those majors and others, and each semester its students are partnered with clients with disabilities. The clients, like Dowell, have reached out to the instructors with a problem that they've encountered that could be solved with assistive technology. The students are charged with designing and creating a technology for their client over the course of a semester.
"The Laughing Room," an interactive art installation by author, illustrator, and MIT graduate student Jonathan "Jonny" Sun, looks like a typical living room: couches, armchairs, coffee table, soft lighting. This cozy scene, however, sits in a glass-enclosed space, flanked by bright lights and a microphone, with a bank of laptops and a video camera positioned across the room. People wander in, take a seat, begin chatting. "I wanted the installation to resemble a sitcom set from the 1980s–a private, familial space," said Sun. "I wanted to explore how AI is changing our conception of private space, with things like the Amazon Echo or Google Home, where you're aware of this third party listening." "The Control Room," a companion installation located in Hayden Library at MIT, displayed a live stream of the action in "The Laughing Room," while another monitor showed the algorithm evaluating people's speech in real time.
Ellen Roche is used to bridging two worlds. Originally from Galway, she has spent the past 14 years moving back and forth between the United States and her native Ireland. She has also spent time in both industry and academia. As an assistant professor at MIT, she holds a joint appointment in the Department of Mechanical Engineering and the Institute for Medical Engineering and Science. Then there is Roche's work, which lies at the intersection of biomedicine and mechanical engineering.
An MIT doctoral student and a recent alumnus -- Pablo Ducru and Michael Shum '17, MEng '18 -- have been selected for the Schwarzman Scholars' class of 2020. They will join other young leaders from around the world next August to study and reside at Schwarzman College at Beijing's prestigious Tshingua University. Schwarzman Scholars earn a fully funded one-year master's degree in global affairs with concentrations in public policy, economics and business, or international relations. They also have access to internships; mentoring; engagement with international leaders in academia, politics, and industry; and opportunities to travel throughout China. Now in its fourth year, the Schwarzman Scholars graduate fellowship aims to train the next generation of global leaders and deepen their understanding of China's place in global affairs.