Category Archives: Internet

The clutter in online conversations

From Reddit to Quora, discussion forums can be equal parts informative and daunting. We’ve all fallen down rabbit holes of lengthy threads that are impossible to sift through. Comments can be redundant, off-topic or even inaccurate, but all that content is ultimately still there for us to try and untangle.

Sick of the clutter, a team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) has developed “Wikum,” a system that helps users construct concise, expandable summaries that make it easier to navigate unruly discussions.

“Right now, every forum member has to go through the same mental labor of squeezing out key points from long threads,” says MIT Professor David Karger, who was senior author on a new paper about Wikum. “If every reader could contribute that mental labor back into the discussion, it would save that time and energy for every future reader, making the conversation more useful for everyone.”

The team tested Wikum against a Google document with tracked changes that aimed to mimic the collaborative editing structure of a wiki. They found that Wikum users completed reading much faster and recalled discussion points more accurately, and that editors made edits 40 percent faster.

Karger wrote the new paper with PhD students Lea Verou and Amy Zhang, who was lead author. The team presented the work last week at ACM’s Conference on Computer-Supported Cooperative Work and Social Computing in Portland, Oregon.

How it works

While wikis can be a good way for people to summarize discussions, they aren’t ideal because users can’t see what’s already been summarized. This makes it difficult to break summarizing down into small steps that can be completed by individual users, because it requires that they spend a lot of energy figuring out what needs to happen next. Meanwhile, forums like Reddit let users “upvote” the best answers or comments, but lack contextual summaries that help readers get detailed overviews of discussions.

Wikum bridges the gap between forums and wikis by letting users work in small doses to refine a discussion’s main points, and giving readers an overall “map” of the conversation.

Readers can import discussions from places such as Disqus, a commenting platform used for publishers like The Atlantic. Then, once users create a summary, readers can examine the text and decide if they want to expand the topic to read more. The system uses color-coded “summary trees” that show topics at different levels of depth and lets readers jump between original comments and summaries.

Creative approaches to connectivity

Daniel Zuo came to MIT with a plan: He wanted to study algorithms and one day to become a research professor.

The senior has more than accomplished the former goal, conducting innovative research on algorithms to reduce network congestion, in the Networks and Mobile Systems group at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). And, as he graduates this spring with a bachelor’s degree in computer science and electrical engineering and a master’s in engineering, he is well on his way to achieving the latter one.

But Zuo has also taken some productive detours from that roadmap, including minoring in creative writing and helping to launch MakeMIT, the nation’s largest “hardware hackathon.”

The next step in his journey will take him to Cambridge University, where he will continue his computer science research as a Marshall Scholar.

“The Marshall affords me the opportunity to keep exploring for a couple more years on an academic level, and to grow on a personal level, too,” Zuo says. While studying in the Advanced Computer Science program at the university’s Computer Laboratory, “I’ll be able to work with networks and systems to deepen my understanding and take more time to explore this field,” he says.

Algorithms to connect the world

Zuo fell in love with algorithms his first year at MIT. “It was exactly what I was looking for,” he says with a smile. “I took every algorithms course there was on offer.”

His first research experience, the summer after his freshman year, was in the lab of Professor Manolis Kellis, head of the Computational Biology group at CSAIL. Zuo worked with a postdoc in Kellis’ group to use algorithms to identify related clusters of genes in a single cell type within a specific tissue. “We ended up coming up with a pretty cool algorithm,” he says.

As a research assistant for TIBCO Career Development Assistant Professor Mohammad Alizadeh, Zuo is now working on cutting-edge algorithms for congestion control in networks, with a focus on “lossless” data networks.

Modern computer network applications need to be able to transmit large amounts of data quickly, without losing information. Zuo likens the situation to a congested traffic light. When there are too many messages queuing at the light, some information just gets dropped.

“When the traffic light starts to get too full, I can send a packet back upstream that says ‘Wait, if you’re going to send me something, don’t,’” he explains. But sending that signal can create a new problem: a “back-propagation” of even more pauses, and more congestion upstream. Zuo’s algorithms aim to solve both of these problems, ensuring that sent data are never lost and that “traffic lights” don’t become too crowded.

Communication networks from malicious hackers

Distributed planning, communication, and control algorithms for autonomous robots make up a major area of research in computer science. But in the literature on multirobot systems, security has gotten relatively short shrift.

In the latest issue of the journal Autonomous Robots, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory and their colleagues present a new technique for preventing malicious hackers from commandeering robot teams’ communication networks. The technique could provide an added layer of security in systems that encrypt communications, or an alternative in circumstances in which encryption is impractical.

“The robotics community has focused on making multirobot systems autonomous and increasingly more capable by developing the science of autonomy. In some sense we have not done enough about systems-level issues like cybersecurity and privacy,” says Daniela Rus, an Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science at MIT and senior author on the new paper.

“But when we deploy multirobot systems in real applications, we expose them to all the issues that current computer systems are exposed to,” she adds. “If you take over a computer system, you can make it release private data — and you can do a lot of other bad things. A cybersecurity attack on a robot has all the perils of attacks on computer systems, plus the robot could be controlled to take potentially damaging action in the physical world. So in some sense there is even more urgency that we think about this problem.”

Identity theft

Most planning algorithms in multirobot systems rely on some kind of voting procedure to determine a course of action. Each robot makes a recommendation based on its own limited, local observations, and the recommendations are aggregated to yield a final decision.

A natural way for a hacker to infiltrate a multirobot system would be to impersonate a large number of robots on the network and cast enough spurious votes to tip the collective decision, a technique called “spoofing.” The researchers’ new system analyzes the distinctive ways in which robots’ wireless transmissions interact with the environment, to assign each of them its own radio “fingerprint.” If the system identifies multiple votes as coming from the same transmitter, it can discount them as probably fraudulent.

“There are two ways to think of it,” says Stephanie Gil, a research scientist in Rus’ Distributed Robotics Lab and a co-author on the new paper. “In some cases cryptography is too difficult to implement in a decentralized form. Perhaps you just don’t have that central key authority that you can secure, and you have agents continually entering or exiting the network, so that a key-passing scheme becomes much more challenging to implement. In that case, we can still provide protection.

“And in case you can implement a cryptographic scheme, then if one of the agents with the key gets compromised, we can still provide  protection by mitigating and even quantifying the maximum amount of damage that can be done by the adversary.”

Hold your ground

In their paper, the researchers consider a problem known as “coverage,” in which robots position themselves to distribute some service across a geographic area — communication links, monitoring, or the like. In this case, each robot’s “vote” is simply its report of its position, which the other robots use to determine their own.

The paper includes a theoretical analysis that compares the results of a common coverage algorithm under normal circumstances and the results produced when the new system is actively thwarting a spoofing attack. Even when 75 percent of the robots in the system have been infiltrated by such an attack, the robots’ positions are within 3 centimeters of what they should be. To verify the theoretical predictions, the researchers also implemented their system using a battery of distributed Wi-Fi transmitters and an autonomous helicopter.

Prevent customer profiling and price gouging

Most website visits these days entail a database query — to look up airline flights, for example, or to find the fastest driving route between two addresses.

But online database queries can reveal a surprising amount of information about the people making them. And some travel sites have been known to jack up the prices on flights whose routes are drawing an unusually high volume of queries.

At the USENIX Symposium on Networked Systems Design and Implementation next week, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory and Stanford University will present a new encryption system that disguises users’ database queries so that they reveal no private information.

The system is called Splinter because it splits a query up and distributes it across copies of the same database on multiple servers. The servers return results that make sense only when recombined according to a procedure that the user alone knows. As long as at least one of the servers can be trusted, it’s impossible for anyone other than the user to determine what query the servers executed.

“The canonical example behind this line of work was public patent databases,” says Frank Wang, an MIT graduate student in electrical engineering and computer science and first author on the conference paper. “When people were searching for certain kinds of patents, they gave away the research they were working on. Stock prices is another example: A lot of the time, when you search for stock quotes, it gives away information about what stocks you’re going to buy. Another example is maps: When you’re searching for where you are and where you’re going to go, it reveals a wealth of information about you.”

Honest broker

Of course, if the site that hosts the database is itself collecting users’ data without their consent, the requirement of at least one trusted server is difficult to enforce.

Wang, however, points to the increasing popularity of services such as DuckDuckGo, a search engine that uses search results from other sites, such as Bing and Yahoo, but vows not to profile its customers.

“We see a shift toward people wanting private queries,” Wang says. “We can imagine a model in which other services scrape a travel site, and maybe they volunteer to host the information for you, or maybe you subscribe to them. Or maybe in the future, travel sites realize that these services are becoming more popular and they volunteer the data. But right now, we’re trusting that third-party sites have adequate protections, and with Splinter we try to make that more of a guarantee.”

The number of exposures necessary

Compressed sensing is an exciting new computational technique for extracting large amounts of information from a signal. In one high-profile demonstration, for instance, researchers at Rice University built a camera that could produce 2-D images using only a single light sensor rather than the millions of light sensors found in a commodity camera.

But using compressed sensing for image acquisition is inefficient: That “single-pixel camera” needed thousands of exposures to produce a reasonably clear image. Reporting their results in the journal IEEE Transactions on Computational Imaging, researchers from the MIT Media Lab now describe a new technique that makes image acquisition using compressed sensing 50 times as efficient. In the case of the single-pixel camera, it could get the number of exposures down from thousands to dozens.

One intriguing aspect of compressed-sensing imaging systems is that, unlike conventional cameras, they don’t require lenses. That could make them useful in harsh environments or in applications that use wavelengths of light outside the visible spectrum. Getting rid of the lens opens new prospects for the design of imaging systems.

“Formerly, imaging required a lens, and the lens would map pixels in space to sensors in an array, with everything precisely structured and engineered,” says Guy Satat, a graduate student at the Media Lab and first author on the new paper.  “With computational imaging, we began to ask: Is a lens necessary?  Does the sensor have to be a structured array? How many pixels should the sensor have? Is a single pixel sufficient? These questions essentially break down the fundamental idea of what a camera is.  The fact that only a single pixel is required and a lens is no longer necessary relaxes major design constraints, and enables the development of novel imaging systems. Using ultrafast sensing makes the measurement significantly more efficient.”

Recursive applications

One of Satat’s coauthors on the new paper is his thesis advisor, associate professor of media arts and sciences Ramesh Raskar. Like many projects from Raskar’s group, the new compressed-sensing technique depends on time-of-flight imaging, in which a short burst of light is projected into a scene, and ultrafast sensors measure how long the light takes to reflect back.

The technique uses time-of-flight imaging, but somewhat circularly, one of its potential applications is improving the performance of time-of-flight cameras. It could thus have implications for a number of other projects from Raskar’s group, such as a camera that can see around corners and visible-light imaging systems for medical diagnosis and vehicular navigation.

Many prototype systems from Raskar’s Camera Culture group at the Media Lab have used time-of-flight cameras called streak cameras, which are expensive and difficult to use: They capture only one row of image pixels at a time. But the past few years have seen the advent of commercial time-of-flight cameras called SPADs, for single-photon avalanche diodes.

Though not nearly as fast as streak cameras, SPADs are still fast enough for many time-of-flight applications, and they can capture a full 2-D image in a single exposure. Furthermore, their sensors are built using manufacturing techniques common in the computer chip industry, so they should be cost-effective to mass produce.

Help make a ubiquitous model of decision processes more accurate

Markov decision processes are mathematical models used to determine the best courses of action when both current circumstances and future consequences are uncertain. They’ve had a huge range of applications — in natural-resource management, manufacturing, operations management, robot control, finance, epidemiology, scientific-experiment design, and tennis strategy, just to name a few.

But analyses involving Markov decision processes (MDPs) usually make some simplifying assumptions. In an MDP, a given decision doesn’t always yield a predictable result; it could yield a range of possible results. And each of those results has a different “value,” meaning the chance that it will lead, ultimately, to a desirable outcome.

Characterizing the value of given decision requires collection of empirical data, which can be prohibitively time consuming, so analysts usually just make educated guesses. That means, however, that the MDP analysis doesn’t guarantee the best decision in all cases.

In the Proceedings of the Conference on Neural Information Processing Systems, published last month, researchers from MIT and Duke University took a step toward putting MDP analysis on more secure footing. They show that, by adopting a simple trick long known in statistics but little applied in machine learning, it’s possible to accurately characterize the value of a given decision while collecting much less empirical data than had previously seemed necessary.

In their paper, the researchers described a simple example in which the standard approach to characterizing probabilities would require the same decision to be performed almost 4 million times in order to yield a reliable value estimate.

With the researchers’ approach, it would need to be run 167,000 times. That’s still a big number — except, perhaps, in the context of a server farm processing millions of web clicks per second, where MDP analysis could help allocate computational resources. In other contexts, the work at least represents a big step in the right direction.

“People are not going to start using something that is so sample-intensive right now,” says Jason Pazis, a postdoc at the MIT Laboratory for Information and Decision Systems and first author on the new paper. “We’ve shown one way to bring the sample complexity down. And hopefully, it’s orthogonal to many other ways, so we can combine them.”

Unpredictable outcomes

In their paper, the researchers also report running simulations of a robot exploring its environment, in which their approach yielded consistently better results than the existing approach, even with more reasonable sample sizes — nine and 105. Pazis emphasizes, however, that the paper’s theoretical results bear only on the number of samples required to estimate values; they don’t prove anything about the relative performance of different algorithms at low sample sizes.

Pazis is joined on the paper by Jonathan How, the Richard Cockburn Maclaurin Professor of Aeronautics and Astronautics at MIT, and by Ronald Parr, a professor of computer science at Duke.

Although the possible outcomes of a decision may be described according to a probability distribution, the expected value of the decision is just the mean, or average, value of all outcomes. In the familiar bell curve of the so-called normal distribution, the mean defines the highest point of the bell.

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.”