Monthly Archives: October 2016

The coolest things that happened at the Computer Science

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 the help of some human input.

Advancements in computer vision

  • A new imaging technique called Interactive Dynamic Video lets you reach in and “touch” objects in videos using a normal camera.
  • Researchers from CSAIL and Israel’s Weizmann Institute of Science produced a movie display called Cinema 3D that uses special lenses and mirrors to allow viewers to watch 3-D movies in a theater without having to wear those clunky 3-D glasses.
  • A new deep-learning algorithm can predict human interactions more accurately than ever before, by training itself on footage from TV shows like “Desperate Housewives” and “The Office.”
  • A group from MIT and Harvard University developed an algorithm that may help astronomers produce the first image of a black hole, stitching together telescope data to essentially turn the planet into one large telescope dish.

Fighting bias in machine learning

When Joy Buolamwini, an MIT master’s candidate in media arts and sciences, sits in front a mirror, she sees a black woman in her 20s. But when her photo is run through recognition software, it does not recognize her face. A seemingly neutral machine programmed with algorithms-codified processes simply fails to detect her features. Buolamwini is, she says, “on the wrong side of computational decisions” that can lead to exclusionary and discriminatory practices and behaviors in society.

That phenomenon, which Buolamwini calls the “coded gaze,”  is what motivated her late last year to launch the Algorithmic Justice League (AJL) to highlight such bias through provocative media and interactive exhibitions; to provide space for people to voice concerns and experiences with coded discrimination; and to develop practices for accountability during the design, development, and deployment phases of coded systems.

That work is what contributed to the Media Lab student earning the grand prize in the professional category of The Search for Hidden Figures. The nationwide contest, created by PepsiCo and 21st Century Fox in partnership with the New York Academy of Sciences, is named for a recently released film that tells the real-life story of three African-American women at NASA whose math brilliance helped launch the United States into the space race in the early 1960s.

“I’m honored to receive this recognition, and I’ll use the prize to continue my mission to show compassion through computation,” says Buolamwini, who was born in Canada, then lived in Ghana and, at the age of four, moved to Oxford, Mississippi. She’s a two-time recipient of an Astronaut Scholarship in a program established by NASA’s Mercury 7 crew members, including late astronaut John Glenn, who are depicted in the film “Hidden Figures.”

The film had a big impact on Buolamwini when she saw a special MIT sneak preview in early December: “I witnessed the power of storytelling to change cultural perceptions by highlighting hidden truths. After the screening where I met Margot Lee Shetterly, who wrote the book on which the film is based, I left inspired to tell my story, and applied for the contest. Being selected as a grand prize winner provides affirmation that pursuing STEM is worth celebrating. And it’s an important reminder to share the stories of discriminatory experiences that necessitate the Algorithmic Justice League as well as the uplifting stories of people who come together to create a world where technology can work for all of us and drive social change.”

The Search for Hidden Figures contest attracted 7,300 submissions from students across the United States. As one of two grand prize winners, Buolamwini receives a $50,000 scholarship, a trip to the Kennedy Space Center in Florida, plus access to New York Academy of Sciences training materials and programs in STEM. She plans to use the prize resources to develop what she calls “bias busting” tools to help defeat bias in machine learning.

That is the focus of her current research at the MIT Media Lab, where Buolamwini is in the Civic Media group pursuing a master’s degree with an eye toward a PhD. “The Media Lab serves as a unifying thread in my journey in STEM. Until I saw the lab on TV, I didn’t realize there was a place dedicated to exploring the future of humanity and technology by allowing us to indulge our imaginations by continuously asking, ‘What if?'”

Before coming to the Media Lab, Buolamwini earned a BS in computer science as a Stamps President’s Scholar at Georgia Tech and a master’s in learning and technology as a Rhodes Scholar at Oxford University. As part of her Rhodes Scholar Service Year, Buolamwini launched Code4Rights to guide young people in partnering with local organizations to develop meaningful technology for their communities. In that year, she also built upon a computer science learning initiative she’d created during her Fulbright fellowship in Lusaka, Zambia, to empower young people to become creators of technology. And, as an entrepreneur, she co-founded a startup hair care technology company and now advises an MIT-connected “smart” clothing startup aimed at transforming women’s health. She’s also an experienced public speaker, most recently at TEDx Beacon Street, the White House, the Vatican, and the Museum of Fine Arts, Boston.

From an early age, Buolamwini felt encouraged to aim high in STEM: “I went after my dreams, and now I continue to push myself beyond present barriers to create a more inclusive future. Inclusive participation matters. And, by being visible in STEM, I hope to inspire the next generation of stargazers.”

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, and technology is an enormous power to influence that in a good way,” he says.

Technology for doing good

Parrish began his academic career at Harvard University and transferred to MIT after his first year. Frustrated at how little power individuals often have in society, Parrish joined DoneGood co-founders Scott Jacobsen and Cullen Schwartz, and became the startup’s chief technology officer his sophomore year. “We kind of distilled our frustrations about the way things are into, ‘How do you actionably use people’s existing power to create real change?’” Parrish says.

The DoneGood app and Chrome extension help consumers find businesses that share their priorities and values, such as paying a living wage, or using organic ingredients. The extension monitors a user’s online shopping and recommends alternatives. The mobile app offers a directory of local options and national brands that users can filter according to their values. “The two things that everyday people have at their disposal to create change is how they spend their time and how they spend their money. We direct money away from brands that aren’t sustainable, therefore creating an actionable incentive for them to become more sustainable,” Parrish says.

DoneGood has raised its first round of funding, and became a finalist in the MIT $100K Entrepreneurship Competition last May. The company now has five full-time employees, and Parrish continues to work as CTO part-time. “It’s been a really amazing experience to be in such an important leadership role. And to take something from the ground up, and really figure out what is the best way to actually create the change you want,” Parrish says. “Where technology meets cultural influence is very interesting, and it’s a space that requires a lot of responsibility and perspective.”

Robotic spectaculars

Parrish also loves building physical objects, and his mechanical engineering major has provided a path to many of his creative projects. “Part of my enjoyment comes from building things with [my] hands and being able to actually work in the physical world, and by studying mechanical engineering you get an invaluable understanding of how the physical world works,” he says. “I also believe strongly in the powers of computers to do things, so combining the two of [these areas] — basically programming mechanical things — is where I think I can get the most enjoyment.”

Easy querying and filtering data

The age of big data has seen a host of new techniques for analyzing large data sets. But before any of those techniques can be applied, the target data has to be aggregated, organized, and cleaned up.

That turns out to be a shockingly time-consuming task. In a 2016 survey, 80 data scientists told the company CrowdFlower that, on average, they spent 80 percent of their time collecting and organizing data and only 20 percent analyzing it.

An international team of computer scientists hopes to change that, with a new system called Data Civilizer, which automatically finds connections among many different data tables and allows users to perform database-style queries across all of them. The results of the queries can then be saved as new, orderly data sets that may draw information from dozens or even thousands of different tables.

“Modern organizations have many thousands of data sets spread across files, spreadsheets, databases, data lakes, and other software systems,” says Sam Madden, an MIT professor of electrical engineering and computer science and faculty director of MIT’s bigdata@CSAIL initiative. “Civilizer helps analysts in these organizations quickly find data sets that contain information that is relevant to them and, more importantly, combine related data sets together to create new, unified data sets that consolidate data of interest for some analysis.”

The researchers presented their system last week at the Conference on Innovative Data Systems Research. The lead authors on the paper are Dong Deng and Raul Castro Fernandez, both postdocs at MIT’s Computer Science and Artificial Intelligence Laboratory; Madden is one of the senior authors. They’re joined by six other researchers from Technical University of Berlin, Nanyang Technological University, the University of Waterloo, and the Qatar Computing Research Institute. Although he’s not a co-author, MIT adjunct professor of electrical engineering and computer science Michael Stonebraker, who in 2014 won the Turing Award — the highest honor in computer science — contributed to the work as well.

Pairs and permutations

Data Civilizer assumes that the data it’s consolidating is arranged in tables. As Madden explains, in the database community, there’s a sizable literature on automatically converting data to tabular form, so that wasn’t the focus of the new research. Similarly, while the prototype of the system can extract tabular data from several different types of files, getting it to work with every conceivable spreadsheet or database program was not the researchers’ immediate priority. “That part is engineering,” Madden says.

The system begins by analyzing every column of every table at its disposal. First, it produces a statistical summary of the data in each column. For numerical data, that might include a distribution of the frequency with which different values occur; the range of values; and the “cardinality” of the values, or the number of different values the column contains. For textual data, a summary would include a list of the most frequently occurring words in the column and the number of different words. Data Civilizer also keeps a master index of every word occurring in every table and the tables that contain it.

Then the system compares all of the column summaries against each other, identifying pairs of columns that appear to have commonalities — similar data ranges, similar sets of words, and the like. It assigns every pair of columns a similarity score and, on that basis, produces a map, rather like a network diagram, that traces out the connections between individual columns and between the tables that contain them.