Monthly Archives: April 2017

Helping to solve problems in areas

“Julia is a great tool.” That’s what New York University professor of economics and Nobel laureate Thomas J. Sargent told 250 engineers, computer scientists, programmers, and data scientists at the third annual JuliaCon held at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL).

If you have not yet heard of Julia, it is not a “who,” but a “what.” Developed at CSAIL, the MIT Department of Mathematics, and throughout the Julia community, it is a fast-maturing programming language developed to be simple to learn, highly dynamic, operational at the speed of C, and ranging in use from general programming to highly quantitative uses such as scientific computing, machine learning, data mining, large-scale linear algebra, and distributed and parallel computing. The language was launched open-source in 2012 and has begun to amass a large following of users and contributors.

This year’s JuliaCon, held June 21-25, was the biggest yet, and featured presentations describing how Julia is being used to solve complex problems in areas as diverse as economic modeling, spaceflight, bioinformatics, and many others.

“We are very excited about Julia because our models are complicated,” said Sargent, who is also a senior fellow at the Hoover Institution. “It’s easy to write the problem down, but it’s hard to solve it — especially if our model is high dimensional. That’s why we need Julia. Figuring out how to solve these problems requires some creativity. The guys who deserve a lot of the credit are the ones who figured out how to put this into a computer. This is a walking advertisement for Julia.” Sargent added that the reason Julia is important is because the next generation of macroeconomic models is very computationally intensive, using high-dimensional models and fitting them over extremely large data sets.

Sargent was awarded the Nobel Memorial Prize in Economic Sciences in 2011 for his work on macroeconomics. Together with John Stachurski he founded quantecon.net, a Julia- and Python-based learning platform for quantitative economics focusing on algorithms and numerical methods for studying economic problems as well as coding skills.

The Julia programming language was created and open-sourced thanks, in part, to a 2012 innovation grant awarded by the MIT Deshapnde Center for Technological Innovation. Julia combines the functionality of quantitative environments such as Matlab, R, SPSS, Stata, SAS, and Python with the speed of production programming languages like Java and C++ to solve big data and analytics problems. It delivers dramatic improvements in simplicity, speed, capacity, and productivity for data scientists, algorithmic traders, quants, scientists, and engineers who need to solve massive computation problems quickly and accurately. The number of Julia users has grown dramatically during the last five years, doubling every nine months. It is taught at MIT, Stanford University, and dozens of universities worldwide. Julia 0.5 will launch this month and Julia 1.0 in 2017.

The basis for machine learning systems decisions

In recent years, the best-performing systems in artificial-intelligence research have come courtesy of neural networks, which look for patterns in training data that yield useful predictions or classifications. A neural net might, for instance, be trained to recognize certain objects in digital images or to infer the topics of texts.

But neural nets are black boxes. After training, a network may be very good at classifying data, but even its creators will have no idea why. With visual data, it’s sometimes possible to automate experiments that determine which visual features a neural net is responding to. But text-processing systems tend to be more opaque.

At the Association for Computational Linguistics’ Conference on Empirical Methods in Natural Language Processing, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) will present a new way to train neural networks so that they provide not only predictions and classifications but rationales for their decisions.

“In real-world applications, sometimes people really want to know why the model makes the predictions it does,” says Tao Lei, an MIT graduate student in electrical engineering and computer science and first author on the new paper. “One major reason that doctors don’t trust machine-learning methods is that there’s no evidence.”

“It’s not only the medical domain,” adds Regina Barzilay, the Delta Electronics Professor of Electrical Engineering and Computer Science and Lei’s thesis advisor. “It’s in any domain where the cost of making the wrong prediction is very high. You need to justify why you did it.”

“There’s a broader aspect to this work, as well,” says Tommi Jaakkola, an MIT professor of electrical engineering and computer science and the third coauthor on the paper. “You may not want to just verify that the model is making the prediction in the right way; you might also want to exert some influence in terms of the types of predictions that it should make. How does a layperson communicate with a complex model that’s trained with algorithms that they know nothing about? They might be able to tell you about the rationale for a particular prediction. In that sense it opens up a different way of communicating with the model.”

The deflection of light particles passing through animal tissue

MIT researchers have developed a technique for recovering visual information from light that has scattered because of interactions with the environment — such as passing through human tissue.

The technique could lead to medical-imaging systems that use visible light, which carries much more information than X-rays or ultrasound waves, or to computer vision systems that work in fog or drizzle. The development of such vision systems has been a major obstacle to self-driving cars.

In experiments, the researchers fired a laser beam through a “mask” — a thick sheet of plastic with slits cut through it in a certain configuration, such as the letter A  — and then through a 1.5-centimeter “tissue phantom,” a slab of material designed to mimic the optical properties of human tissue for purposes of calibrating imaging systems. Light scattered by the tissue phantom was then collected by a high-speed camera, which could measure the light’s time of arrival.

From that information, the researchers’ algorithms were able to reconstruct an accurate image of the pattern cut into the mask.

“The reason our eyes are sensitive only in this narrow part of the spectrum is because this is where light and matter interact most,” says Guy Satat, a graduate student at the MIT Media Lab and first author on the new paper. “This is why X-ray is able to go inside the body, because there is very little interaction. That’s why it can’t distinguish between different types of tissue, or see bleeding, or see oxygenated or deoxygenated blood.”

The imaging technique’s potential applications in automotive sensing may be even more compelling than those in medical imaging, however. Many experimental algorithms for guiding autonomous vehicles are highly reliable under good illumination, but they fall apart completely in fog or drizzle; computer vision systems misinterpret the scattered light as having reflected off of objects that don’t exist. The new technique could address that problem.

Satat’s coauthors on the new paper, published today in Scientific Reports, are three other members of the Media Lab’s Camera Culture group: Ramesh Raskar, the group’s leader, Satat’s thesis advisor, and an associate professor of media arts and sciences; Barmak Heshmat, a research scientist; and Dan Raviv, a postdoc.

Expanding circles

Like many of the Camera Culture group’s projects, the new system relies on a pulsed laser that emits ultrashort bursts of light, and a high-speed camera that can distinguish the arrival times of different groups of photons, or light particles. When a light burst reaches a scattering medium, such as a tissue phantom, some photons pass through unmolested; some are only slightly deflected from a straight path; and some bounce around inside the medium for a comparatively long time. The first photons to arrive at the sensor have thus undergone the least scattering; the last to arrive have undergone the most.

Information theory and developed early time sharing computers

Robert “Bob” Fano, a professor emeritus in the Department of Electrical Engineering and Computer Science (EECS) whose work helped usher in the personal computing age, died in Naples, Florida on July 13. He was 98.

During his time on the faculty at MIT, Fano conducted research across multiple disciplines, including information theory, networks, electrical engineering and radar technologies. His work on “time-sharing” — systems that allow multiple people to use a computer at the same time — helped pave the way for the more widespread use of computers in society.

Much of his early work in information theory has directly impacted modern technologies. His research with Claude Shannon, for example, spurred data-compression techniques like Huffman coding that are used in today’s high-definition TVs and computer networks.

In 1961, Fano and Fernando Corbató, professor emeritus in EECS, developed the Compatible Time-Sharing System (CTSS), one of the earliest time-sharing systems. The success of CTSS helped convince MIT to launch Project MAC, a pivotal early center for computing research for which Fano served as its founding director. Project MAC has since dramatically expanded to become MIT’s largest interdepartmental research lab, the Computer Science and Artificial Intelligence Laboratory (CSAIL).

“Bob did pioneering work in computer science at a time when many people viewed the field as a curiosity rather than a rigorous academic discipline,” CSAIL Director Daniela Rus says. “None of our work here would have been possible without his passion, insight, and drive.”

Fano was the Ford Professor of Engineering in EECS and a dedicated teacher who would often labor into the late hours of the morning, working on new lectures. He was also a member of multiple research labs at MIT, including the Laboratory for Computer Science, the Research Laboratory for Electronics, the MIT Radiation Laboratory, and the MIT Lincoln Laboratory. He helped create MIT’s first official curriculum for computer science, which is now the most popular major at the Institute.

In many respects, Fano was one of the world’s first open-source advocates. He frequently described computing as a public utility that, like water or electricity, should be accessible to all. His writings in the 1960s often discussed computing’s place in society, and predated today’s debates about the ethical implications of technology.

“One must consider the security of a system that may hold in its mass memory detailed information on individuals and organizations,” he wrote in a 1966 paper he co-authored with Corbató. “How will access to the utility be controlled? Who will regulate its use?”

A native of Italy, Fano studied at the School of Engineering of Torino before moving to the United States in 1939. He earned both his bachelor’s degree (1941) and his doctorate (1947) from MIT in electrical engineering, and was a member of the MIT faculty from 1947 until 1984.

During World War II, Fano worked on microwave components at the MIT Radiation Laboratory and on radar technologies at the Lincoln Lab. He also served as associate head of EECS from 1971 to 1974.