Supercomputing system

The new TX-Green computing system at the MIT Lincoln Laboratory Supercomputing Center (LLSC) has been named the most powerful supercomputer in New England, 43rd most powerful in the U.S., and 106th most powerful in the world. A team of experts at TOP500 ranks the world’s 500 most powerful supercomputers biannually. The systems are ranked based on a LINPACK Benchmark, which is a measure of a system’s floating-point computing power, i.e., how fast a computer solves a dense system of linear equations.

Established in early 2016, the LLSC was developed to enhance computing power and accessibility for more than 1,000 researchers across the laboratory. The LLSC uses interactive supercomputing to augment the processing power of desktop systems to process large sets of sensor data, create high-fidelity simulations, and develop new algorithms. Located in Holyoke, Massachusetts, the new system is the only zero-carbon supercomputer on the TOP500 list; it uses energy from a mixture of hydroelectric, wind, solar, and nuclear sources.

In November, Dell EMC installed a new petaflop-scale system, which consists of 41,472 Intel processor cores and can compute 1015 operations per second. Compared to

Plain text into data for statistical analysis

The vast wealth of information unlocked by the Internet, most is plain text. The data necessary to answer myriad questions — about, say, the correlations between the industrial use of certain chemicals and incidents of disease, or between patterns of news coverage and voter-poll results — may all be online. But extracting it from plain text and organizing it for quantitative analysis may be prohibitively time consuming.

Information extraction — or automatically classifying data items stored as plain text — is thus a major topic of artificial-intelligence research. Last week, at the Association for Computational Linguistics’ Conference on Empirical Methods on Natural Language Processing, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory won a best-paper award for a new approach to information extraction that turns conventional machine learning on its head.

Most machine-learning systems work by combing through training examples and looking for patterns that correspond to classifications provided by human annotators. For instance, humans might label parts of speech in a set of texts, and the machine-learning system will try to identify patterns that resolve ambiguities — for instance, when “her” is a

Machine learning system

In recent years, computers have gotten remarkably good at recognizing speech and images: Think of the dictation software on most cellphones, or the algorithms that automatically identify people in photos posted to Facebook.

But recognition of natural sounds — such as crowds cheering or waves crashing — has lagged behind. That’s because most automated recognition systems, whether they process audio or visual information, are the result of machine learning, in which computers search for patterns in huge compendia of training data. Usually, the training data has to be first annotated by hand, which is prohibitively expensive for all but the highest-demand applications.

Sound recognition may be catching up, however, thanks to researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). At the Neural Information Processing Systems conference next week, they will present a sound-recognition system that outperforms its predecessors but didn’t require hand-annotated data during training.

Instead, the researchers trained the system on video. First, existing computer vision systems that recognize scenes and objects categorized the images in the video. The new system then found correlations between those visual categories and natural sounds.

“Computer vision has

Deep learning system

Living in a dynamic physical world, it’s easy to forget how effortlessly we understand our surroundings. With minimal thought, we can figure out how scenes change and objects interact.

But what’s second nature for us is still a huge problem for machines. With the limitless number of ways that objects can move, teaching computers to predict future actions can be difficult.

Recently, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have moved a step closer, developing a deep-learning algorithm that, given a still image from a scene, can create a brief video that simulates the future of that scene.

Trained on 2 million unlabeled videos that include a year’s worth of footage, the algorithm generated videos that human subjects deemed to be realistic 20 percent more often than a baseline model.

The team says that future versions could be used for everything from improved security tactics and safer self-driving cars. According to CSAIL PhD student and first author Carl Vondrick, the algorithm can also help machines recognize people’s activities without expensive human annotations.

“These videos show us what computers think can happen in a scene,” says Vondrick. “If you can predict the future, you must have understood something about the present.”

Vondrick wrote the

Outsized influence on organizational

Jay W. Forrester SM ’45, professor emeritus in the MIT Sloan School of Management, founder of the field of system dynamics, and a pioneer of digital computing, died Nov. 16. He was 98.

Forrester’s time at MIT was rife with invention. He was a key figure in the development of digital computing, the national air defense system, and MIT’s Lincoln Laboratory. He developed servomechanisms (feedback-based controls for mechanical devices), radar controls, and flight-training computers for the U.S. Navy. He led Project Whirlwind, an early MIT digital computing project. It was his work on Whirlwind that led him to invent magnetic core memory, an early form of RAM for which he holds the patent, in 1949.

MIT Sloan Professor John Sterman, a student, friend, and colleague of Forrester’s since the 1970s, points to a 2003 photo of Forrester on a Segway as an illustration of his work’s lasting impact.

“He really is standing on top of the fruits of his many careers,” Sterman said. “He’s standing on a device that integrates servomechanisms, digital controllers, and a sophisticated feedback control system.”

“From the air traffic control system to 3-D printers, from the software companies use to manage their supply chains to the simulations nations use to understand climate change,

Venture capitalists gather to discuss

Surviving breast cancer changed the course of Regina Barzilay’s research. The experience showed her, in stark relief, that oncologists and their patients lack tools for data-driven decision making. That includes what treatments to recommend, but also whether a patient’s sample even warrants a cancer diagnosis, she explained at the Nov. 10 Machine Intelligence Summit, organized by MIT and venture capital firm Pillar.

“We do more machine learning when we decide on Amazon which lipstick you would buy,” said Barzilay, the Delta Electronics Professor of Electrical Engineering and Computer Science at MIT. “But not if you were deciding whether you should get treated for cancer.”

Barzilay now studies how smarter computing can help patients. She wields the powerful predictive approach called machine learning, a technique that allows computers, given enough data and training, to pick out patterns on their own — sometimes even beyond what humans are capable of pinpointing.

Machine learning has long been vaunted in consumer contexts — Apple’s Siri can talk with us because machine learning enables her to understand natural human speech — yet the summit gave a glimpse of the approach’s much broader potential. Its reach could offer not only better Siris (e.g., Amazon’s “Alexa”), but improved health care

Powerful than previously realized

Quantum computers promise huge speedups on some computational problems because they harness a strange physical property called entanglement, in which the physical state of one tiny particle depends on measurements made of another. In quantum computers, entanglement is a computational resource, roughly like a chip’s clock cycles — kilohertz, megahertz, gigahertz — and memory in a conventional computer.

In a recent paper in the journal Proceedings of the National Academy of Sciences, researchers at MIT and IBM’s Thomas J. Watson Research Center show that simple systems of quantum particles exhibit exponentially more entanglement than was previously believed. That means that quantum computers — or other quantum information devices — powerful enough to be of practical use could be closer than we thought.

Where ordinary computers deal in bits of information, quantum computers deal in quantum bits, or qubits. Previously, researchers believed that in a certain class of simple quantum systems, the degree of entanglement was, at best, proportional to the logarithm of the number of qubits.

“For models that satisfy certain physical-reasonability criteria — i.e., they’re not too contrived; they’re something that you could in principle realize in the lab — people thought that a factor of the log of the system size

Reproduces aspects of human neurology

MIT researchers and their colleagues have developed a new computational model of the human brain’s face-recognition mechanism that seems to capture aspects of human neurology that previous models have missed.

The researchers designed a machine-learning system that implemented their model, and they trained it to recognize particular faces by feeding it a battery of sample images. They found that the trained system included an intermediate processing step that represented a face’s degree of rotation — say, 45 degrees from center — but not the direction — left or right.

This property wasn’t built into the system; it emerged spontaneously from the training process. But it duplicates an experimentally observed feature of the primate face-processing mechanism. The researchers consider this an indication that their system and the brain are doing something similar.

“This is not a proof that we understand what’s going on,” says Tomaso Poggio, a professor of brain and cognitive sciences at MIT and director of the Center for Brains, Minds, and Machines (CBMM), a multi-institution research consortium funded by the National Science Foundation and headquartered at MIT. “Models are kind of cartoons of reality, especially in biology. So I would be surprised if things turn out to be this simple. But

Fabricate drones with a wide range

This fall’s new Federal Aviation Administration regulations have made drone flight easier than ever for both companies and consumers. But what if the drones out on the market aren’t exactly what you want?

A new system from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) is the first to allow users to design, simulate, and build their own custom drone. Users can change the size, shape, and structure of their drone based on the specific needs they have for payload, cost, flight time, battery usage, and other factors.

To demonstrate, researchers created a range of unusual-looking drones, including a five-rotor “pentacopter” and a rabbit-shaped “bunnycopter” with propellers of different sizes and rotors of different heights.

“This system opens up new possibilities for how drones look and function,” says MIT Professor Wojciech Matusik, who oversaw the project in CSAIL’s Computational Fabrication Group. “It’s no longer a one-size-fits-all approach for people who want to make and use drones for particular purposes.”

The interface lets users design drones with different propellers, rotors, and rods. It also provides guarantees that the drones it fabricates can take off, hover and land — which is no simple task considering the intricate technical trade-offs associated with drone weight, shape, and control.

“For example, adding more

Fully automated speech recognition

Speech recognition systems, such as those that convert speech to text on cellphones, are generally the result of machine learning. A computer pores through thousands or even millions of audio files and their transcriptions, and learns which acoustic features correspond to which typed words.

But transcribing recordings is costly, time-consuming work, which has limited speech recognition to a small subset of languages spoken in wealthy nations.

At the Neural Information Processing Systems conference this week, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) are presenting a new approach to training speech-recognition systems that doesn’t depend on transcription. Instead, their system analyzes correspondences between images and spoken descriptions of those images, as captured in a large collection of audio recordings. The system then learns which acoustic features of the recordings correlate with which image characteristics.

“The goal of this work is to try to get the machine to learn language more like the way humans do,” says Jim Glass, a senior research scientist at CSAIL and a co-author on the paper describing the new system. “The current methods that people use to train up speech recognizers are very supervised. You get an utterance, and you’re told what’s said. And you do this

Largest publicly traded corporation

Apple CEO Tim Cook will deliver the address at MIT’s 2017 Commencement exercises on Friday, June 9.

Cook joined Apple in 1998 and was named its CEO in 2011. As chief executive, he has overseen the introduction of some of Apple’s innovative and popular products, including iPhone 7 and Apple Watch. An advocate for equality and champion of the environment, Cook reminds audiences that Apple’s mission is to change the world for the better, both through its products and its policies.

“Mr. Cook’s brilliance as a business leader, his genuineness as a human being, and his passion for issues that matter to our community make his voice one that I know will resonate deeply with our graduates,” MIT President L. Rafael Reif says. “I am delighted that he will join us for Commencement and eagerly await his charge to the Class of 2017.”

Before becoming CEO, Cook was Apple’s chief operating officer, responsible for the company’s worldwide sales and operations, including management of Apple’s global supply chain, sales activities, and service and support. He also headed the Macintosh division and played a key role in the development of strategic reseller and supplier relationships, ensuring the company’s flexibility in a demanding marketplace.

“Apple stands at the intersection of

Provided key knowledge

This week the Association for Computer Machinery (ACM) announced its 2016 fellows, which include four principal investigators from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL): professors Erik Demaine, Fredo Durand, William Freeman, and Daniel Jackson. They were among the 1 percent of ACM members to receive the distinction.

“Erik, Fredo, Bill, and Daniel are wonderful colleagues and extraordinary computer scientists, and I am so happy to see their contributions recognized with the most prestigious member grade of the ACM,” says CSAIL Director Daniela Rus, who herself was named a fellow last year. “All of us at CSAIL are very proud of these researchers for receiving these esteemed honors.”

ACM’s 53 fellows for 2016 were named for their distinctive contributions spanning such computer science disciplines as computer vision, computer graphics, software design, machine learning, algorithms, and theoretical computer science.

“As nearly 100,000 computing professionals are members of our association, to be selected to join the top 1 percent is truly an honor,” says ACM President Vicki L. Hanson. “Fellows are chosen by their peers and hail from leading universities, corporations and research labs throughout the world. Their inspiration, insights and dedication bring immeasurable benefits that improve lives and help drive the global economy.

Target technique combats information

When it comes to protecting data from cyberattacks, information technology (IT) specialists who defend computer networks face attackers armed with some advantages. For one, while attackers need only find one vulnerability in a system to gain network access and disrupt, corrupt, or steal data, the IT personnel must constantly guard against and work to mitigate varied and myriad network intrusion attempts.

The homogeneity and uniformity of software applications have traditionally created another advantage for cyber attackers. “Attackers can develop a single exploit against a software application and use it to compromise millions of instances of that application because all instances look alike internally,” says Hamed Okhravi, a senior staff member in the Cyber Security and Information Sciences Division at MIT Lincoln Laboratory. To counter this problem, cybersecurity practitioners have implemented randomization techniques in operating systems. These techniques, notably address space layout randomization (ASLR), diversify the memory locations used by each instance of the application at the point at which the application is loaded into memory.

In response to randomization approaches like ASLR, attackers developed information leakage attacks, also called memory disclosure attacks. Through these software assaults, attackers can make the application disclose how its internals have been randomized while the application is

Strengthen the intersection

“When you’re part of a community, you want to leave it better than you found it,” says Keertan Kini, an MEng student in the Department of Electrical Engineering, or Course 6. That philosophy has guided Kini throughout his years at MIT, as he works to improve policy both inside and out of MIT.

As a member of the Undergraduate Student Advisory Group, former chair of the Course 6 Underground Guide Committee, member of the Internet Policy Research Initiative (IPRI), and of the Advanced Network Architecture group, Kini’s research focus has been in finding ways that technology and policy can work together. As Kini puts it, “there can be unintended consequences when you don’t have technology makers who are talking to policymakers and you don’t have policymakers talking to technologists.” His goal is to allow them to talk to each other.

At 14, Kini first started to get interested in politics. He volunteered for President Obama’s 2008 campaign, making calls and putting up posters. “That was the point I became civically engaged,” says Kini. After that, he was campaigning for a ballot initiative to raise more funding for his high school, and he hasn’t stopped being interested in public policy since.

High school was

Preserving their fundamental mathematical

One way to handle big data is to shrink it. If you can identify a small subset of your data set that preserves its salient mathematical relationships, you may be able to perform useful analyses on it that would be prohibitively time consuming on the full set.

The methods for creating such “coresets” vary according to application, however. Last week, at the Annual Conference on Neural Information Processing Systems, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory and the University of Haifa in Israel presented a new coreset-generation technique that’s tailored to a whole family of data analysis tools with applications in natural-language processing, computer vision, signal processing, recommendation systems, weather prediction, finance, and neuroscience, among many others.

“These are all very general algorithms that are used in so many applications,” says Daniela Rus, the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science at MIT and senior author on the new paper. “They’re fundamental to so many problems. By figuring out the coreset for a huge matrix for one of these tools, you can enable computations that at the moment are simply not possible.”

As an example, in their paper the researchers apply their technique to a matrix —

Science and engineering

During January of her junior year at MIT, Caroline Colbert chose to do a winter externship at Massachusetts General Hospital (MGH). Her job was to shadow the radiation oncology staff, including the doctors that care for patients and medical physicists that design radiation treatment plans.

Colbert, now a senior in the Department of Nuclear Science and Engineering (NSE), had expected to pursue a career in nuclear power. But after working in a medical environment, she changed her plans.

She stayed at MGH to work on building a model to automate the generation of treatment plans for patients who will undergo a form of radiation therapy called volumetric-modulated arc therapy (VMAT). The work was so interesting that she is still involved with it and has now decided to pursue a doctoral degree in medical physics, a field that allows her to blend her training in nuclear science and engineering with her interest in medical technologies.

She’s even zoomed in on schools with programs that have accreditation from the Commission on Accreditation of Medical Physics Graduate Programs so she’ll have the option of having a more direct impact on patients. “I don’t know yet if I’ll be more interested in clinical work, research, or both,”

Computational role

Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory have developed a new computational model of a neural circuit in the brain, which could shed light on the biological role of inhibitory neurons — neurons that keep other neurons from firing.

The model describes a neural circuit consisting of an array of input neurons and an equivalent number of output neurons. The circuit performs what neuroscientists call a “winner-take-all” operation, in which signals from multiple input neurons induce a signal in just one output neuron.

Using the tools of theoretical computer science, the researchers prove that, within the context of their model, a certain configuration of inhibitory neurons provides the most efficient means of enacting a winner-take-all operation. Because the model makes empirical predictions about the behavior of inhibitory neurons in the brain, it offers a good example of the way in which computational analysis could aid neuroscience.

The researchers will present their results this week at the conference on Innovations in Theoretical Computer Science. Nancy Lynch, the NEC Professor of Software Science and Engineering at MIT, is the senior author on the paper. She’s joined by Merav Parter, a postdoc in her group, and Cameron Musco, an MIT graduate student in electrical

Platform queries and maps

People generally associate graphic processing units (GPUs) with imaging processing. Developed for video games in the 1990s, modern GPUs are specialized circuits with thousands of small, efficient processing units, or “cores,” that work simultaneously to rapidly render graphics on screen.

But for the better part of a decade, GPUs have also found general computing applications. Because of their incredible parallel-computing speeds and high-performance memory, GPUs are today used for advanced lab simulations and deep-learning programming, among other things.

Now, Todd Mostak, a former researcher at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), is using GPUs to develop an analytic database and visualization platform called MapD, which is the fastest of its kind in the world, according to Mostak.

MapD is essentially a form of a commonly used database-management system that’s modified to run on GPUs instead of the central processing units (CPUs) that power most traditional database-management systems. By doing so, MapD can process billions of data points in milliseconds, making it 100 times faster than traditional systems. Moreover, MapD visualizes all processed data points nearly instantaneously — such as, say, plotting tweets on a world map — and parameters can be modified on the fly to adjust the visualized display.

With its first

Combines art and technology

Garrett Parrish grew up singing and dancing as a theater kid, influenced by his older siblings, one of whom is an actor and the other a stage manager. But by the time he reached high school, Parrish had branched out significantly, drumming in his school’s jazz ensemble and helping to build a state-championship-winning robot.

MIT was the first place Parrish felt he was able to work meaningfully at the nexus of art and technology. “Being a part of the MIT culture, and having the resources that are available here, are what really what opened my mind to that intersection,” the MIT senior says. “That’s always been my goal from the beginning: to be as emotionally educated as I am technically educated.”

Parrish, who is majoring in mechanical engineering, has collaborated on a dizzying array of projects ranging from app-building, to assistant directing, to collaborating on a robotic opera. Driving his work is an interest in shaping technology to serve others.

“The whole goal of my life is to fix all the people problems. I sincerely think that the biggest problems we have are how we deal with each other, and how we treat each other. [We need to be] promoting empathy and understanding,

The Computer Science and Artificial Intelligence

Machines that predict the future, robots that patch wounds, and wireless emotion-detectors are just a few of the exciting projects that came out of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) this year. Here’s a sampling of 16 highlights from 2016 that span the many computer science disciplines that make up CSAIL.

Robots for exploring Mars — and your stomach

  • A team led by CSAIL director Daniela Rus developed an ingestible origami robot that unfolds in the stomach to patch wounds and remove swallowed batteries.
  • Researchers are working on NASA’s humanoid robot, “Valkyrie,” who will be programmed for trips into outer space and to autonomously perform tasks.
  • A 3-D printed robot was made of both solids and liquids and printed in one single step, with no assembly required.

Keeping data safe and secure

  • CSAIL hosted a cyber summit that convened members of academia, industry, and government, including featured speakers Admiral Michael Rogers, director of the National Security Agency; and Andrew McCabe, deputy director of the Federal Bureau of Investigation.
  • Researchers came up with a system for staying anonymous online that uses less bandwidth to transfer large files between anonymous users.
  • A deep-learning system called AI2 was shown to be able to predict 85 percent of cyberattacks with