Monthly Archives: February 2017

Trump administration to take immediate action on cybersecurity

In a world where hackers can sabotage power plants and impact elections, there has never been a more crucial time to examine cybersecurity for critical infrastructure, most of which is privately owned.

According to MIT experts, over the last 25 years presidents from both parties have paid lip service to the topic while doing little about it, leading to a series of short-term fixes they liken to a losing game of “Whac-a-Mole.” This scattershot approach, they say, endangers national security.

In a new report based on a year of workshops with leaders from industry and government, the MIT team has made a series of recommendations for the Trump administration to develop a coherent cybersecurity plan that coordinates efforts across departments, encourages investment, and removes parts of key infrastructure like the electric grid from the internet.

Coming on the heels of a leak of the new administration’s proposed executive order on cybersecurity, the report also recommends changes in tax law and regulations to incentivize private companies to improve the security of their critical infrastructure. While the administration is focused on federal systems, the MIT team aimed to address what’s left out of that effort: privately-owned critical infrastructure.

“The nation will require a coordinated, multi-year effort to address deep strategic weaknesses in the architecture of critical systems, in how those systems are operated, and in the devices that connect to them,” the authors write. “But we must begin now. Our goal is action, both immediate and long-term.”

Entitled “Making America Safer: Toward a More Secure Network Environment for Critical Sectors,” the 50-page report outlines seven strategic challenges that would greatly reduce the risks from cyber attacks in the sectors of electricity, finance, communications and oil/natural gas. The workshops included representatives from major companies from each sector, and focused on recommendations related to immediate incentives, long-term research and streamlined regulation.

The report was published by MIT’s Internet Policy Research Initiative (IPRI) at the Computer Science and Artificial Intelligence Laboratory (CSAIL), in conjunction with MIT’s Center for International Studies (CIS). Principal author Joel Brenner was formerly inspector general of the National Security Agency and head of U.S. counterintelligence in the Office of the Director of National Intelligence. Other contributors include Hal Abelson, David Clark, Shirley Hung, Kenneth Oye, Richard Samuels, John Tirman and Daniel Weitzner.

To determine what a better security environment would look like, the researchers convened a series of workshops aimed at going beyond the day-to-day tactical challenges to look at deep cyber vulnerabilities.

The workshops highlighted the difficulty of quantifying the level of risk across different sectors and the return on investment for specific cybersecurity measures. In light of facility-directed attacks like the Stuxnet virus and the sabotage of a Saudi oil refinery, attendees expressed deep concern about the security of infrastructure like the electric grid, which depends on public networks.

The future of technology

When Alphabet executive chairman Eric Schmidt started programming in 1969 at the age of 14, there was no explicit title for what he was doing. “I was just a nerd,” he says.

But now computer science has fundamentally transformed fields like transportation, health care and education, and also provoked many new questions. What will artificial intelligence (AI) be like in 10 years? How will it impact tomorrow’s jobs? What’s next for autonomous cars?

These topics were all on the table on May 3, when the Computer Science and Artificial Intelligence Laboratory (CSAIL) hosted Schmidt for a conversation with CSAIL Director Daniela Rus at the Kirsch Auditorium in the Stata Center.

Schmidt discussed his early days as a computer science PhD at the University of California at Berkeley, where he looked up to MIT researchers like Michael Dertouzos. At Bell Labs he coded UNIX’s lexical-analysis program Lex before moving on to executive roles at Sun Microsystems, Novell, and finally Google, where he served as CEO from 2001 to 2011. In his current role as executive chairman of Google’s parent company, Schmidt focuses on Alphabet’s external matters, advising Google CEO Sundar Pichai and other senior leadership on business and policy.

Speaking with Rus on the topic of health care, Schmidt said that doing a better job of leveraging data will enable doctors to improve how they make decisions.

“Hospitals have enormous amounts of data, which is inaccessible to anyone except for themselves,” he said. “These [machine learning] techniques allow you to take all of that information, sum it all together, and actually produce outcomes.”

Schmidt also cited Google’s ongoing work in self-driving vehicles, including last week’s launch of 500 cars in Arizona, and addressed the issue of how technology will impact jobs in different fields.

“The economic folks would say that you can see the job that’s lost, but you very seldom can see the job that’s created,” said Schmidt. “While there will be a tremendous dislocation of jobs — and I’m not denying that — I think that, in aggregate, there will be more jobs.”

Rus also asked Schmidt about his opposition to the Trump administration’s efforts to limit the number of H1B visas that U.S. tech companies can offer to high-skilled foreign workers.

“At Google we want the best people in the world, regardless of sex, race, country, or what-have-you,” said Schmidt. “Stupid government policies that restrict us from giving us a fair chance of getting those people are antithetical to our mission [and] the things we serve.”

Schmidt ended the conversation by imploring students to take the skills they’ve learned and use them to work on the world’s toughest problems.

“There’s nothing more exciting than that feeling of inventing something new,” he said. “You as scientists should identify those areas and run at them as hard as you can.”

In his introduction of Schmidt, MIT President L. Rafael Reif applauded him for his leadership on issues like innovation and sustainability, including his support of MIT’s Inclusive Innovation Competition, which awards prizes to organizations that focus on improving economic opportunity for low-income communities.

Walking speed with wireless signals

We’ve long known that blood pressure, breathing, body temperature and pulse provide an important window into the complexities of human health. But a growing body of research suggests that another vital sign – how fast you walk – could be a better predictor of health issues like cognitive decline, falls, and even certain cardiac or pulmonary diseases.

Unfortunately, it’s hard to accurately monitor walking speed in a way that’s both continuous and unobtrusive. Professor Dina Katabi’s group at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) has been working on the problem, and believes that the answer is to go wireless.

In a new paper, the team presents “WiGait,” a device that can measure the walking speed of multiple people with 95 to 99 percent accuracy using wireless signals.

The size of a small painting, the device can be placed on the wall of a person’s house and its signals emit roughly one-hundredth the amount of radiation of a standard cellphone. It builds on Katabi’s previous work on WiTrack, which analyzes wireless signals reflected off people’s bodies to measure a range of behaviors from breathing and falling to specific emotions.

“By using in-home sensors, we can see trends in how walking speed changes over longer periods of time,” says lead author and PhD student Chen-Yu Hsu. “This can provide insight into whether someone should adjust their health regimen, whether that’s doing physical therapy or altering their medications.”

WiGait is also 85 to 99 percent accurate at measuring a person’s stride length, which could allow researchers to better understand conditions like Parkinson’s disease that are characterized by reduced step size.

Hsu and Katabi developed WiGait with CSAIL PhD student Zachary Kabelac and master’s student Rumen Hristov, alongside undergraduate Yuchen Liu from the Hong Kong University of Science and Technology, and Assistant Professor Christine Liu from the Boston University School of Medicine. The team will present their paper in May at ACM’s CHI Conference on Human Factors in Computing Systems in Colorado.

How it works

Today, walking speed is measured by physical therapists or clinicians using a stopwatch. Wearables like FitBit can only roughly estimate speed based on step count, and GPS-enabled smartphones are similarly inaccurate and can’t work indoors. Cameras are intrusive and can only monitor one room. VICON motion tracking is the only method that’s comparably accurate to WiGate, but it is not widely available enough to be practical for monitoring day-to-day health changes.

Meanwhile, WiGait measures walking speed with a high level of granularity, without requiring that the person wear or carry a sensor. It does so by analyzing the surrounding wireless signals and their reflections off a person’s body. The CSAIL team’s algorithms can also distinguish walking from other movements, such as cleaning the kitchen or brushing one’s teeth.

Katabi says the device could help reveal a wealth of important health information, particularly for the elderly. A change in walking speed, for example, could mean that the person has suffered an injury or is at an increased risk of falling. The system’s feedback could even help the person determine if they should move to a different environment such as an assisted-living home.

Combat media stereotypes of Muslim women

Layla Shaikley SM ’13 began her master’s in architecture at MIT with a hunger to redevelop nations recovering from conflict. When she decided that data and logistics contributed more immediately to development than architecture did, ­Shaikley switched to the Media Lab to work with Professor Sandy ­Pentland, and became a cofounder of Wise Systems, which develops routing software that helps companies deliver goods and services.

“There’s nothing more creative than building a company,” Shaikley says. “We plan the most effective routes and optimize them in real time using driver feedback. Better logistics can dramatically reduce the number of late deliveries, increase efficiency, and save fuel.”

But Shaikley is perhaps better known for a viral video, “Muslim Hipsters: #mipsterz,” that she and friends created to combat the media stereotypes of Muslim women. It reached hundreds of thousands of viewers and received vigorous positive and negative feedback.

The video “is a really refreshing, jovial view of an underrepresented identity: young American Muslim women with alternative interests in the arts and culture,” Shaikley says. “The narrow media image is so far from the real fabric of Muslim-­American life that we all need to add our pieces to the quilt to create a more accurate image.”

Shaikley’s parents moved from Iraq to California in the 1970s, and she and her five siblings enjoyed a “quintessentially all-­American childhood,” she says. “I grew up on a skateboard, and I love to surf and snowboard.” She feels deeply grateful to her parents, who “always put our needs first,” she adds. “When we visited relatives in Iraq, we observed what life is like when people don’t have the privilege of a free society. Those experiences really shaped my understanding of the world and also my sense of responsibility to give back.”

Shaikley says the sum of her diverse life experiences has helped her as a professional with Wise Systems and as a voice for underrepresented Muslim women.

“My work at MIT under [professors] Reinhard Goethert and Sandy ­Pentland was critical to my career and understanding of data as it relates to developing urban areas,” she says. “And every piece of my disparate experiences, which included the coolest internship of my life with NASA working on robotics for Mars, has played a huge role.”

The hottest subjects on campus

On an afternoon in early April, Tommi Jaakkola is pacing at the front of the vast auditorium that is 26-100. The chalkboards behind him are covered with equations. Jaakkola looks relaxed in a short-sleeved black shirt and jeans, and gestures to the board. “What is the answer here?” he asks the 500 MIT students before him. “If you answer, you get a chocolate. If nobody answers, I get one — because I knew the answer and you didn’t.” The room erupts in laugher.

With similar flair but a tighter focus on the first few rows of seats, Regina Barzilay had held the room the week prior. She paused often to ask: “Does this make sense?” If silence ensued, she warmly met the eyes of the students and reassured them: “It’s okay. It will come.” Barzilay acts as though she is teaching a small seminar rather than a stadium-sized class requiring four instructors, 15 teaching assistants, and, on occasion, an overflow room.

Welcome to “Introduction to Machine Learning,” a course in understanding how to give computers the ability to learn things without being explicitly programmed to do so. The popularity of 6.036, as it is also known, grew steadily after it was first offered, from 138 in 2013 to 302 students in 2016. This year 700 students registered for the course — so many that professors had to find ways to winnow the class down to about 500, a size that could fit in one of MIT’s largest lecture halls.

Jaakkola, the Thomas Siebel Professor in the Department of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society, and Barzilay, the Delta Electronics Professor of Electrical Engineering and Computer Science, have led 6.036 since its inception. They provide students from varied departments with the necessary tools to apply machine learning in the real world — and they do so, according to students, in a manner that is remarkably engaging.

Greg Young, an MIT senior and electrical engineering and computer science major, says the orchestration of the class, which is co-taught by Wojciech Matusik and Pablo Parrilo from the Department of Electrical Engineering and Computer Science (EECS), is impressive. This is all the more so because the trendiness of machine learning (and, consequently, the class enrollment), in his opinion, is nearly out of hand.

“I think people are going where they think the next big thing is,” Young says. Waving an arm to indicate the hundreds of students lined up in desks below him, he says: “The professors certainly do a good job keeping us engaged, considering the size of this class.”

Indeed, the popularity of 6.036 is such that a version for graduate students — 6.862 (Applied Machine Learning) — was folded into it last spring. These students take 6.036 and do an additional semester-long project that involves applying machine learning methods to a problem in their own research.

“Nowadays machine learning is used almost everywhere to make sense of data,” says faculty lead, Stefanie Jegelka, the X-Window Consortium Career Development Assistant Professor in EECS. She says her students come from MIT’s schools of engineering, architecture, science, management, and elsewhere. Only one-third of graduate students seeking to take the spinoff secured seats this semester.

How they learn

The success of 6.036, according to its faculty designers, has to do with its balanced delivery of theoretical content and programming experience — all in enough depth to prove challenging but graspable, and, above all, useful. “Our students want to learn to think like an applied machine-learning person,” says Jaakkola, who launched the pilot course with Barzilay. “We try to expose the material in a way that enables students with very minimal background to sort of get the gist of how things work and why they work.”

Once the domain of science fiction and movies, machine learning has become an integral part of our lived experience. From our expectations as consumers (think of those Netflix and Amazon recommendations), to how we interact with social media (those ads on Facebook are no accident), to how we acquire any kind of information (“Alexa, what is the Laplace transform?”), machine learning algorithms operate, in the simplest sense, by converting large collections of knowledge and information into predictions that are relevant to individual needs.

As a discipline, then, machine learning is the attempt to design and build computer programs that learn from experience for the purpose of prediction or control. In 6.036, students study principles and algorithms for turning training data into effective automated predictions. “The course provides an excellent survey of techniques,” says EECS graduate student Helen Zhou, a 6.036 teaching assistant. “It helps build a foundation for understanding what all those buzzwords in the tech industry mean.”