A growing body of research has demonstrated that algorithms and other types of software can be discriminatory, yet the vague nature of these tools makes it difficult to implement specific regulations. Determining the existing legal, ethical and philosophical implications of these powerful decision-making aides, while still obtaining answers and information, is a complex challenge. Harini Suresh, a PhD student at MITs Computer Science and Artificial Intelligence Laboratory (CSAIL), is investigating this multilayered puzzle: how to create fair and accurate machine learning algorithms that let users obtain the data they need. Suresh studies the societal implications of automated systems in MIT Professor John Guttag's Data-Driven Inference Group, which uses machine learning and computer vision to improve outcomes in medicine, finance, and sports. Here, she discusses her research motivations, how a food allergy led her to MIT, and teaching students about deep learning.
Nancy Kanwisher, the Walter A. Rosenblith Professor of Cognitive Neuroscience at MIT, has been named a recipient of the 2018 Heineken Prize -- the Netherlands' most prestigious scientific prize -- for her work on the functional organization of the human brain. Kanwisher, who is a professor of brain and cognitive sciences and a member of MIT's McGovern Institute for Brain Research, uses neuroimaging to study the functional organization of the human brain. Over the last 20 years her lab has played a central role in the identification of regions of the human brain that are engaged in particular components of perception and cognition. Many of these regions are very specifically engaged in a single mental function such as perceiving faces, places, bodies, or words, or understanding the meanings of sentences or the mental states of others. These regions form a "neural portrait of the human mind," according to Kanwisher, who has assembled dozens of videos for the general public on her website, NancysBrainTalks.
The MIT Statistics and Data Science Center (SDSC), a part of the Institute for Data, Systems, and Society (IDSS), announced two new academic programs today: the MicroMasters program in Statistics and Data Science, and the Interdisciplinary Doctoral Program in Statistics, both beginning in the fall. The MicroMasters program, currently under development by MIT faculty, will be offered online through edX. "Digital technologies are enabling us to bring MIT's data science curriculum to learners around the world regardless of their location or socioeconomic status," says Vice President for Open Learning Sanjay Sarma. The curriculum includes foundational knowledge of data science methods and tools, a deep dive into probability and statistics, and opportunities to learn, implement, and experiment with data analysis techniques and machine learning algorithms. "The demand for data scientists is growing rapidly," says Dean for Digital Learning Krishna Rajagopal.
In past negotiations aimed at reducing the arsenals of the world's nuclear superpowers, chiefly the U.S. and Russia, a major sticking point has been the verification process: How do you prove that real bombs and nuclear devices -- not just replicas -- have been destroyed, without revealing closely held secrets about the design of those weapons? Now, researchers at MIT have come up with a clever solution, which in effect serves as a physics-based version of the cryptographic keys used in computer encryption systems. In fact, they've come up with two entirely different versions of such a system, to show that there may be a variety of options to choose from if any one is found to have drawbacks. Their findings are reported in two papers, one in Nature Communications and the other in the Proceedings of the National Academy of Sciences, with MIT assistant professor of nuclear science and engineering Areg Danagoulian as senior author of both. Because of the difficulties in proving that a nuclear warhead is real and contains actual nuclear fuel (typically highly enriched plutonium), past treaties have instead focused on the much larger and harder-to-fake delivery systems: intercontinental ballistic missiles, cruise missiles, and bombers.
Sitan Chen, Lillian Chin '17, and Suchita Nety -- are among the 30 recipients of the 2018 Paul and Daisy Soros Fellowships for New Americans. Sylvia Biscoveanu, a recent graduate of Penn State University who will be pursuing a PhD at the MIT Kavli Institute for Astrophysics and Space Research next fall, was also named a Soros Fellow. The Soros Fellowships provide up to $90,000 funding for graduate studies for immigrants and the children of immigrants. Award winners are selected for their potential to make significant contributions to United States society, culture, or their academic fields. This year, over 1,700 candidates applied to the prestigious fellowship program.
Map apps may have changed our world, but they still haven't mapped all of it yet. Specifically, mapping roads can be difficult and tedious: Even after taking aerial images, companies still have to spend many hours manually tracing out roads. As a result, even companies like Google haven't yet gotten around to mapping the vast majority of the more than 20 million miles of roads across the globe. Gaps in maps are a problem, particularly for systems being developed for self-driving cars. To address the issue, researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have created RoadTracer, an automated method to build road maps that's 45 percent more accurate than existing approaches.
During the past several years, many strains of bacteria have become resistant to existing antibiotics, and very few new drugs have been added to the antibiotic arsenal. To help combat this growing public health problem, some scientists are exploring antimicrobial peptides -- naturally occurring peptides found in most organisms. Most of these are not powerful enough to fight off infections in humans, so researchers are trying to come up with new, more potent versions. Researchers at MIT and the Catholic University of Brasilia have now developed a streamlined approach to developing such drugs. Their new strategy, which relies on a computer algorithm that mimics the natural process of evolution, has already yielded one potential drug candidate that successfully killed bacteria in mice.
MIT professors and MacArthur Fellows Regina Barzilay and Dina Katabi recently gathered leaders in technology, biotech, and regulatory agencies for a summit to inspire widespread adoption of artificial intelligence and digital technologies in health care. MIT is surrounded by pharmaceutical companies, but until now there has been sparse connection between AI research at MIT and research on drug discovery. The fields have in essence spoken different languages and existed worlds apart. Barzilay and Katabi are set to change that. Less than a year ago, they started a collaboration with pharmaceutical companies and quickly recognized a wealth of new research questions and an opportunity to transform the process of drug design and manufacturing.
Timothy Manning Swager, the John D. MacArthur Professor of Chemistry, has been named a 2018 Vannevar Bush Faculty Fellow by the U.S. Department of Defense (DoD). The Vannevar Bush Faculty Fellowship program is sponsored by the Basic Research Office in the Office of the Under Secretary of Defense for Research and Engineering. It is administered by the Office of Naval Research. The program seeks outstanding researchers to conduct transformative basic research in topic areas of interest to the DoD. Through the program, select university researchers and students learn about DoD's current and future challenges, and are introduced to some of the ongoing critical research.
Everyday experience makes it obvious -- sometimes frustratingly so -- that our working memory capacity is limited. We can only keep so many things consciously in mind at once. The results of a new study may explain why: They suggest that the "coupling," or synchrony, of brain waves among three key regions breaks down in specific ways when visual working memory load becomes too much to handle. "When you reach capacity there is a loss of feedback coupling," says senior author Earl Miller, the Picower Professor of Neuroscience at MIT's Picower Institute for Learning and Memory. That loss of synchrony means the regions can no longer communicate with each other to sustain working memory.