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3Q: D. Fox Harrell on his video game for the #MeToo era

The Imagination, Computation, and Expression Laboratory at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) has released a new video game called Grayscale, which is designed to sensitize players to problems of sexism, sexual harassment, and sexual assault in the workplace.

Goldwasser, Micali, and Rivest win BBVA Foundation Frontiers of Knowledge Awards

This week it was announced that MIT professors and CSAIL principal investigators Shafi Goldwasser, Silvio Micali, Ronald Rivest, and former MIT professor Adi Shamir won this year’s BBVA Foundation Frontiers of Knowledge Awards in the Information and Communication Technologies category for their work in cryptography.

Chasing complexity

In his junior year of high school, Ryan Williams transferred from the public school near his hometown of Somerville, Alabama — “essentially a courthouse and a couple gas stations,” as he describes it — to the Alabama School of Math and Science in Mobile.

Articles

Teleoperating robots with virtual reality

Certain industries have traditionally not had the luxury of telecommuting. Many manufacturing jobs, for example, require a physical presence to operate machinery. But what if such jobs could be done remotely? Last week researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) presented a virtual reality (VR) system that lets you teleoperate a robot using an Oculus Rift headset.

An algorithm for your blind spot

Light lets us see the things that surround us, but what if we could also use it to see things hidden around corners? It sounds like science fiction, but that’s the idea behind a new algorithm out of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) — and its discovery has implications for everything from emergency response to self-driving cars.

Celebrating the life of doctoral student and alumnus Michael B. Cohen

Michael B. Cohen ’14, SM ’16 had a deep love for mathematics and the theoretical foundations of computing — a love that was infectious, brilliant, and always shared with others. Cohen, a doctoral student in the Department of Electrical Engineering and Computer Science (EECS), died suddenly from natural causes in September. He was 25 years of age.

Bug-repair system learns from example

Anyone who’s downloaded an update to a computer program or phone app knows that most commercial software has bugs and security holes that require regular “patching.” Often, those bugs are simple oversights. For example, the program tries to read data that have already been deleted. The patches, too, are often simple — such as a single line of code that verifies that a data object still exists.

“Superhero” robot wears different outfits for different tasks

From butterflies that sprout wings to hermit crabs that switch their shells, many animals must adapt their exterior features in order to survive. While humans don’t undergo that kind of metamorphosis, we often try to create functional objects that are similarly adaptive — including our robots. Despite what you might have seen in “Transformers” movies, though, today’s robots are still pretty inflexible. Each of their parts usually has a fixed structure and a single defined purpose, making it difficult for them to perform a wide variety of actions.

Automatic code reuse

Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed a new system that allows programmers to transplant code from one program into another. The programmer can select the code from one program and an insertion point in a second program, and the system will automatically make modifications necessary — such as changing variable names — to integrate the code into its new context.

Jegelka receives DARPA Young Faculty Award

This August it was announced that CSAIL principal investigator and MIT professor Stefanie Jegelka will receive funding for her research on geometric methods in optimization.The award is part of the Defense Advanced Research Projects Agency (DARPA) Young Faculty Award (YFA), which highlights young researchers in academia covering various topics in STEM. Specifically, this YFA directs recipients to research supporting Department of Defense (DoD) and National Security capabilities, challenges, and ambitions. Jegelka’s research will explore ways to make machine learning more efficient and robust, through exploiting connections between geometric and combinatorial structure, optimization and discrete probability.

How neural networks think

Artificial-intelligence research has been transformed by machine-learning systems called neural networks, which learn how to perform tasks by analyzing huge volumes of training data. During training, a neural net continually readjusts thousands of internal parameters until it can reliably perform some task, such as identifying objects in digital images or translating text from one language to another. But on their own, the final values of those parameters say very little about how the neural net does what it does.

IBM and MIT to pursue joint research in artificial intelligence, establish new MIT–IBM Watson AI Lab

IBM and MIT today announced that IBM plans to make a 10-year, $240 million investment to create the MIT–IBM Watson AI Lab in partnership with MIT. The lab will carry out fundamental artificial intelligence (AI) research and seek to propel scientific breakthroughs that unlock the potential of AI. The collaboration aims to advance AI hardware, software, and algorithms related to deep learning and other areas; increase AI’s impact on industries, such as health care and cybersecurity; and explore the economic and ethical implications of AI on society.

Two sciences tie the knot

Economics and computer science had always been on friendly terms at MIT. With the growth of cloud computing, e-commerce, machine learning, and online social networks, their relationship grew more serious. Now that these tools and applications have become ubiquitous and gone global, economics and computer science are taking their relationship to the next level.

Indyk receives NSF funding for new Institute for Foundations of Data Science

This week it was announced that a team led by CSAIL principal investigator and MIT professor Piotr Indyk will receive funding to develop a new “Institute for Foundations of Data Science” at MIT.The project is part of the National Science Foundation’s new $17.7 million effort towards “Transdisciplinary Research in Principles of Data Science” (TRIPODS). Co-principal investigators include CSAIL’s Jonathan Kelner and Ronitt Rubinfeld, as well as Philippe Rigollet and Devavrat Shah of the MIT Institute for Data, Systems and Society.According to Indyk, the aim of the project is “to stimulate research and educational interactions between mathematics, statistics and theoretical computer science, both within MIT and in the research community at large.”

MIT App Inventor receives award from the Mass Technology Leadership Council

This week it was announced that Mass Technology Leadership Council (MassTLC) will present CSAIL principal investigator Hal Abelson’s team with the Distinguished Leadership Award for their work on the MIT App Inventor.App Inventor is a cloud-based open-source tool that lets users of all skill levels create working apps via smartphones and tablets. It serves as educational platform for teaching computing in middle school and high school, aimed at creating excitement around computer science at younger ages.

Using machine learning to improve patient care

Doctors are often deluged by signals from charts, test results, and other metrics to keep track of. It can be difficult to integrate and monitor all of these data for multiple patients while making real-time treatment decisions, especially when data is documented inconsistently across hospitals. In a new pair of papers, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) explore ways for computers to help doctors make better medical decisions.

Using chip memory more efficiently

For decades, computer chips have increased efficiency by using “caches,” small, local memory banks that store frequently used data and cut down on time- and energy-consuming communication with off-chip memory. Today’s chips generally have three or even four different levels of cache, each of which is more capacious but slower than the last. The sizes of the caches represent a compromise between the needs of different kinds of programs, but it’s rare that they’re exactly suited to any one program.

Practical parallelism

The chips in most modern desktop computers have four “cores,” or processing units, which can run different computational tasks in parallel. But the chips of the future could have dozens or even hundreds of cores, and taking advantage of all that parallelism is a stiff challenge. Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory have developed a new system that not only makes parallel programs run much more efficiently but also makes them easier to code.

Peering into neural networks

Neural networks, which learn to perform computational tasks by analyzing large sets of training data, are responsible for today’s best-performing artificial intelligence systems, from speech recognition systems, to automatic translators, to self-driving cars. But neural nets are black boxes. Once they’ve been trained, even their designers rarely have any idea what they’re doing — what data elements they’re processing and how.

Computer system predicts products of chemical reactions

When organic chemists identify a useful chemical compound — a new drug, for instance — it’s up to chemical engineers to determine how to mass-produce it. There could be 100 different sequences of reactions that yield the same end product. But some of them use cheaper reagents and lower temperatures than others, and perhaps most importantly, some are much easier to run continuously, with technicians occasionally topping up reagents in different reaction chambers.

Origami anything

In a 1999 paper, Erik Demaine — now a CSAIL principal investigator, but then an 18-year-old PhD student at the University of Waterloo, in Canada — described an algorithm that could determine how to fold a piece of paper into any conceivable 3-D shape. It was a milestone paper in the field of computational origami, but the algorithm didn’t yield very practical folding patterns. Essentially, it took a very long strip of paper and wound it into the desired shape. The resulting structures tended to have lots of seams where the strip doubled back on itself, so they weren’t very sturdy.

Videos

Custom robots in a matter of minutes

Even as robots become increasingly common, they remain incredibly difficult to make. From designing and modeling to fabricating and testing, the process is slow and costly: Even one small change can mean days or weeks of rethinking and revising important hardware. But what if there were a way to let non-experts craft different robotic designs — in one sitting?

High-quality online video with less rebuffering

We’ve all experienced two hugely frustrating things on YouTube: our video either suddenly gets pixelated, or it stops entirely to rebuffer. Both happen because of special algorithms that break videos into small chunks that load as you go. If your internet is slow, YouTube might make the next few seconds of video lower resolution to make sure you can still watch uninterrupted — hence, the pixelation. If you try to skip ahead to a part of the video that hasn’t loaded yet, your video has to stall in order to buffer that part.

Designing the microstructure of printed objects

Today’s 3-D printers have a resolution of 600 dots per inch, which means that they could pack a billion tiny cubes of different materials into a volume that measures just 1.67 cubic inches. Such precise control of printed objects’ microstructure gives designers commensurate control of the objects’ physical properties — such as their density or strength, or the way they deform when subjected to stresses. But evaluating the physical effects of every possible combination of even just two materials, for an object consisting of tens of billions of cubes, would be prohibitively time consuming.

Somersaulting simulation for jumping bots

In recent years engineers have been developing new technologies to enable robots and humans to move faster and jump higher. Soft, elastic materials store energy in these devices, which, if released carefully, enable elegant dynamic motions. Robots leap over obstacles and prosthetics empower sprinting. A fundamental challenge remains in developing these technologies. Scientists spend long hours building and testing prototypes that can reliably move in specific ways so that, for example, a robot lands right-side up upon landing a jump.

Reshaping computer-aided design

Almost every object we use is developed with computer-aided design (CAD). Ironically, while CAD programs are good for creating designs, using them is actually very difficult and time-consuming if you’re trying to improve an existing design to make the most optimal product.

Drones that drive

Being able to both walk and take flight is typical in nature — many birds, insects, and other animals can do both. If we could program robots with similar versatility, it would open up many possibilities: Imagine machines that could fly into construction areas or disaster zones that aren’t near roads and then squeeze through tight spaces on the ground to transport objects or rescue people.

Giving robots a sense of touch

Eight years ago, Ted Adelson’s research group at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) unveiled a new sensor technology, called GelSight, that uses physical contact with an object to provide a remarkably detailed 3-D map of its surface. Now, by mounting GelSight sensors on the grippers of robotic arms, two MIT teams have given robots greater sensitivity and dexterity. The researchers presented their work in two papers at the International Conference on Robotics and Automation last week.

Wearable system helps visually impaired users navigate

Computer scientists have been working for decades on automatic navigation systems to aid the visually impaired, but it’s been difficult to come up with anything as reliable and easy to use as the white cane, the type of metal-tipped cane that visually impaired people frequently use to identify clear walking paths. White canes have a few drawbacks, however. One is that the obstacles they come in contact with are sometimes other people. Another is that they can’t identify certain types of objects, such as tables or chairs, or determine whether a chair is already occupied.

Using Bitcoin to prevent identity theft

A reaction to the 2008 financial crisis, Bitcoin is a digital-currency scheme designed to wrest control of the monetary system from central banks. With Bitcoin, anyone can mint money, provided he or she can complete a complex computation quickly enough. Through a set of clever protocols, that computational hurdle prevents the system from being coopted by malicious hackers.

Eric Schmidt visits MIT to discuss computing, artificial intelligence, and 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?

Talks