Monthly Archives: September 2017

The years most significant innovations address

R&D 100 Awards have been presented to six technologies that were developed either solely by technical staff from MIT Lincoln Laboratory or through their collaborations with researchers from other organizations. These awards, given annually by R&D Magazine, recognize the 100 most significant inventions introduced in the past year.

A panel composed of R&D Magazine editors and independent reviewers selects the recipients from hundreds of nominees from industry, government laboratories, and university research institutes worldwide. The awards were announced during a banquet at the 2017 R&D 100 Conference last month in Orlando, Florida. The six winning technologies from this year bring to 38 the total number of R&D 100 Awards that Lincoln Laboratory has received since 2010.

Measuring metabolism to guide health decisions

Lincoln Laboratory, in collaboration with the U.S. Army Research Institute of Environmental Medicine and the Marine Expeditionary Rifle Squad, developed a low-cost personal sensor that allows individuals to make on-demand metabolic measurements simply by breathing into the apparatus. The Carbon dioxide/Oxygen Breath and Respiration Analyzer (COBRA) measures the respiratory exchange ratio in exhaled breath (i.e., the ratio of carbon dioxide produced to oxygen consumed) and the volume rate of oxygen consumption. These measurements enable the calculation of both energy expenditure and the levels of carbohydrates and fats burned by the body to meet energy-expenditure demands.

This information about the rate of energy expenditure and the dietary sources of metabolic energy can help military commanders, doctors, physical trainers, and coaches set reasonable standards for physical exertion. For example, limits on the distance and speed of foot marches can be established by quantifying metabolic workloads of soldiers. Clinicians could also use COBRA metabolic data to tailor dietary and exercise regimens for the one-third of Americans who are struggling to manage obesity and avoid the high blood glucose levels that precede the onset of Type 2 diabetes.

“The COBRA system promises to cost-effectively provide RER and energy-expenditure measurements comparable to those provided by clinical sensors costing as much as $40,000, and with ease of use that makes personal ownership feasible,” says Gary Shaw, principal investigator on the Laboratory’s COBRA team.

Enabling remotely piloted aircraft to avoid mid-air collisions

Lincoln Laboratory worked with the U.S. Army, SRC Inc., and Kutta Technologies to develop a system that enables remotely piloted aircraft to meet Federal Aviation Administration (FAA) and international standards for seeing and avoiding other aircraft.

The Ground-Based Sense-and-Avoid (GBSAA) System for Unmanned Aircraft Systems (UAS) uses both existing FAA radars to locate transponder-equipped airplanes that communicate with air traffic control personnel and 3-D radars with special processing algorithms to locate general-aviation airplanes that do not carry transponders. The GBSAA system’s purpose-built algorithms process the surveillance data from the various radars to track aircraft and to estimate the risk the remotely piloted systems pose to nearby aircraft; the system then issues warnings and provides maneuver guidance for UAS pilots.

The next generation of artificial intelligence

On a recent Monday morning, Vivienne Sze, an associate professor of electrical engineering and computer science at MIT, spoke with enthusiasm about network architecture design. Her students nodded slowly, as if on the verge of comprehension. When the material clicked, the nods grew in speed and confidence. “Everything crystal clear?” she asked with a brief pause and a return nod before diving back in.

This new course, 6.S082/6.888 (Hardware Architecture for Deep Learning), is modest in size — capped at 25 for now — compared to the bursting lecture halls characteristic of other MIT classes focused on machine learning and artificial intelligence. But this course is a little different. With a long list of prerequisites and a heavy base of assumed knowledge, students are jumping into deep water quickly. They blaze through algorithmic design in a few weeks, cover the terrain of computer hardware design in a similar period, then get down to the real work: how to think about making these two fields work together.

The goal of the class is to teach students the interplay between two traditionally separate disciplines, Sze says. “How can you write algorithms that map well onto hardware so they can run faster? And how can you design hardware to better support the algorithm?” she asks rhetorically. “It’s one thing to design algorithms, but to deploy them in the real world you have to consider speed and energy consumption.”

“We are beginning to see tremendous student interest in the hardware side of deep learning,” says Joel Emer, who co-teaches the course with Sze. A professor of the practice in MIT’s Department of Electrical Engineering and Computer Science, and a senior distinguished research scientist at the chip manufacturer NVidia, Emer has partnered with Sze before. Together they wrote a journal article that provides a comprehensive tutorial and survey coverage of recent advances toward enabling efficient processing of deep neural networks. It is used as the main reference for the course.

In 2016, their group unveiled a new, energy-efficient computer chip optimized for neural networks, which could enable powerful artificial-intelligence systems to run locally on mobile devices. The groundbreaking chip, called “Eyeriss,” could also help usher in the internet of things.

Association for Computing Machinery Fellows

Today four MIT faculty were named among the Association for Computing Machinery’s 2017 Fellows for making “landmark contributions to computing.”

Honorees included School of Science Dean Michael Sipser and three researchers affiliated with MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL): Shafi Goldwasser, Tomás Lozano-Pérez, and Silvio Micali.

The professors were among fewer than 1 percent of Association for Computing Machinery (ACM) members to receive the distinction. Fellows are named for contributions spanning such disciplines as graphics, vision, software design, algorithms, and theory.

“Shafi, Tomás, Silvio, and Michael are very esteemed colleagues and friends, and I’m so happy to see that their contributions have recognized with ACM’s most prestigious member grade,” said CSAIL Director Daniela Rus, who herself was named an ACM Fellow in 2014. “All of us at MIT are very proud of them for receiving this distinguished honor.”

Goldwasser was selected for “transformative work that laid the complexity-theoretic foundations for the science of cryptography.” This work has helped spur entire subfields of computer science, including zero-knowledge proofs, cryptographic theory, and probabilistically checkable proofs. In 2012 she received ACM’s Turing Award, often referred to as “the Nobel Prize of computing.”

Lozano-Pérez was recognized for “contributions to robotics, and motion planning, geometric algorithms, and their applications.” His current work focuses on integrating task, motion, and decision planning for robotic manipulation. He was a recipient of the 2011 IEEE Robotics Pioneer Award, and is also a 2014 MacVicar Fellow and a fellow of the Association for the Advancement of Artificial Intelligence (AAAI) and of the IEEE.

Like Goldwasser, Micali was also honored for his work in cryptography and complexity theory, including his pioneering of new methods for the efficient verification of mathematical proofs. His work has had a major impact on how computer scientists understand concepts like randomness and privacy. Current interests include zero-knowledge proofs, secure protocols, and pseudorandom generation. He has also received the Turing Award, the Goedel prize in theoretical computer science, and the RSA prize in cryptography.