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CSAIL's 16 best Tweets of 2016

The “Dragon Book,” Margaret Hamilton, and the first single-chip CPU topped our Twitter feed this past year, alongside tweets about computer science news, our research, and other topics in coding and programming. We’ve rounded up the top 16 tweets of 2016, determined by number of retweets from our audience of nearly 25,000 followers.Follow us to stay updated on the latest news from the lab! 1. How to accidently break a Skype bot - 982 RTs 


Ingestible robots, glasses-free 3-D, and computers that explain themselves

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

Data diversity

When data sets get too big, sometimes the only way to do anything useful with them is to extract much smaller subsets and analyze those instead.Those subsets have to preserve certain properties of the full sets, however, and one property that’s useful in a wide range of applications is diversity. If, for instance, you’re using your data to train a machine-learning system, you want to make sure that the subset you select represents the full range of cases that the system will have to confront.

Making big data manageable

One way to handle big data is to shrink it. If you can identify a small subset of your data set that preserves its salient mathematical relationships, you may be able to perform useful analyses on it that would be prohibitively time consuming on the full set.

Four CSAIL researchers named ACM fellows

This week the Association for Computer Machinery (ACM) announced its 2016 fellows, which include four principal investigators from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL): professors Erik Demaine, Fredo Durand, William Freeman, and Daniel Jackson. They were among the 1 percent of ACM members to receive the distinction.

Learning words from pictures

Speech recognition systems, such as those that convert speech to text on cellphones, are generally the result of machine learning. A computer pores through thousands or even millions of audio files and their transcriptions, and learns which acoustic features correspond to which typed words.But transcribing recordings is costly, time-consuming work, which has limited speech recognition to a small subset of languages spoken in wealthy nations.

Design your own custom drone

This fall’s new FAA regulations have made drone flight easier than ever for both companies and consumers. But what if the drones out on the market aren’t exactly what you want?A new system from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) is the first to allow users to design, simulate, and build their own custom drone. Users can change the size, shape, and structure of their drone based on the specific needs they have for payload, cost, flight time, battery usage, and other factors.

Face to face with "The Enemy"

When the filmmaking pioneers Auguste and Louis Lumière screened their 1895 film, "The Arrival of a Train at La Ciotat," audiences were so frightened by the real appearance of the image that they screamed and got out of the way — or so a well-known anecdote goes. Today, as one enters a virtual reality (VR) space — such as that conjured by MIT Visiting Artist Karim Ben Khelifa in his vanguard project "The Enemy" — it is not uncommon for participants to experience a similar shock at the sounds of footsteps, then sudden presence of two soldiers in the room.

Computer learns to recognize sounds by watching video

In recent years, computers have gotten remarkably good at recognizing speech and images: Think of the dictation software on most cellphones, or the algorithms that automatically identify people in photos posted to Facebook.But recognition of natural sounds — such as crowds cheering or waves crashing — has lagged behind. That’s because most automated recognition systems, whether they process audio or visual information, are the result of machine learning, in which computers search for patterns in huge compendia of training data. Usually, the training data has to be first annotated by hand, which is prohibitively expensive for all but the highest-demand applications.

Study: carpooling apps could reduce taxi traffic 75%

Traffic is not just a nuisance for drivers: it’s also a public-health hazard and bad news for the economy.Transportation studies put the annual cost of congestion at $160 billion, which includes 7 billion hours of time lost to sitting in traffic and an extra 3 billion gallons of fuel burned. One way to improve traffic is through ride-sharing - and a new MIT study suggests that using carpooling options from companies like Uber and Lyft could reduce the number of taxis on the road 75 percent without significantly impacting travel time.

How the brain recognizes faces

MIT researchers and their colleagues have developed a new computational model of the human brain’s face-recognition mechanism that seems to capture aspects of human neurology that previous models have missed.The researchers designed a machine-learning system that implemented their model, and they trained it to recognize particular faces by feeding it a battery of sample images. They found that the trained system included an intermediate processing step that represented a face’s degree of rotation — say, 45 degrees from center — but not the direction — left or right.

Creating videos of the future

Living in a dynamic physical world, it’s easy to forget how effortlessly we understand our surroundings. With minimal thought, we can figure out how scenes change and objects interact.But what’s second nature for us is still a huge problem for machines. With the limitless number of ways that objects can move, teaching computers to predict future actions can be difficult.Recently, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have moved a step closer, developing a deep-learning algorithm that, given a still image from a scene, can create a brief video that simulates the future of that scene.

Meeting of the minds for machine intelligence

Surviving breast cancer changed the course of Regina Barzilay’s research. The experience showed her, in stark relief, that oncologists and their patients lack tools for data-driven decision making. That includes what treatments to recommend, but also whether a patient’s sample even warrants a cancer diagnosis, she explained at the Nov. 10 Machine Intelligence Summit, organized by MIT and venture capital firm Pillar.“We do more machine learning when we decide on Amazon which lipstick you would buy,” said Barzilay, the Delta Electronics Professor of Electrical Engineering and Computer Science at MIT. “But not if you were deciding whether you should get treated for cancer.”

Entanglement bonanza

Quantum computers promise huge speedups on some computational problems because they harness a strange physical property called entanglement, in which the physical state of one tiny particle depends on measurements made of another. In quantum computers, entanglement is a computational resource, roughly like a chip’s clock cycles — kilohertz, megahertz, gigahertz — and memory in a conventional computer.

Teaching Hong Kong students to embrace computational thinking

CoolThink@JC, a four-year initiative of The Hong Kong Jockey Club Charities Trust, was launched today to empower the city’s primary school teachers and students with computational thinking skills, including coding.Developed through a collaboration with MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), the Education University of Hong Kong, and City University of Hong Kong, the eventual aim is to integrate computational thinking into all Hong Kong primary schools. Initially, CoolThink@JC will target over 16,500 students at 32 primary schools across the city.

Enabling wireless virtual reality

One of the limits of today’s virtual reality (VR) headsets is that they have to be tethered to computers in order to process data well enough to deliver high-resolution visuals. But wearing an HDMI cable reduces mobility and can even lead to users tripping over cords.Fortunately, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have recently unveiled a prototype system called “MoVR” that allows gamers to use any VR headset wirelessly.

Artificial-intelligence system surfs web to improve its performance

Of the vast wealth of information unlocked by the Internet, most is plain text. The data necessary to answer myriad questions — about, say, the correlations between the industrial use of certain chemicals and incidents of disease, or between patterns of news coverage and voter-poll results — may all be online. But extracting it from plain text and organizing it for quantitative analysis may be prohibitively time consuming.

Faster programs, easier programming

Dynamic programming is a technique that can yield relatively efficient solutions to computational problems in economics, genomic analysis, and other fields. But adapting it to computer chips with multiple “cores,” or processing units, requires a level of programming expertise that few economists and biologists have.

CSAIL welcomes 6 new EECS faculty

CSAIL welcomes six new faculty members to MIT's Department of Electrical Engineering and Computer Science (EECS)!The new faculty include Adam Belay, Stefanie Mueller, Max Shulakar, David Sontag, Ryan Williams and Virginia Vassilev Williams.Adam Belay will join as an assistant professor in July 2017. Belay’s research area is operating systems and networking. Much of his work has focused on restructuring computer systems so that developers can more easily reach the full performance potential of hardware. Previously he worked on storage virtualization at VMware Inc. and contributed substantial power-management code to the Linux Kernel project.

Making computers explain themselves

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.

Finding patterns in corrupted data

Data analysis — and particularly big-data analysis — is often a matter of fitting data to some sort of mathematical model. The most familiar example of this might be linear regression, which finds a line that approximates a distribution of data points. But fitting data to probability distributions, such as the familiar bell curve, is just as common.If, however, a data set has just a few corrupted entries — say, outlandishly improbable measurements — standard data-fitting techniques can break down. This problem becomes much more acute with high-dimensional data, or data with many variables, which is ubiquitous in the digital age.

Articles

CSAIL's 16 best Tweets of 2016

The “Dragon Book,” Margaret Hamilton, and the first single-chip CPU topped our Twitter feed this past year, alongside tweets about computer science news, our research, and other topics in coding and programming. We’ve rounded up the top 16 tweets of 2016, determined by number of retweets from our audience of nearly 25,000 followers.Follow us to stay updated on the latest news from the lab! 1. How to accidently break a Skype bot - 982 RTs 


Data diversity

When data sets get too big, sometimes the only way to do anything useful with them is to extract much smaller subsets and analyze those instead.Those subsets have to preserve certain properties of the full sets, however, and one property that’s useful in a wide range of applications is diversity. If, for instance, you’re using your data to train a machine-learning system, you want to make sure that the subset you select represents the full range of cases that the system will have to confront.

Making big data manageable

One way to handle big data is to shrink it. If you can identify a small subset of your data set that preserves its salient mathematical relationships, you may be able to perform useful analyses on it that would be prohibitively time consuming on the full set.

Four CSAIL researchers named ACM fellows

This week the Association for Computer Machinery (ACM) announced its 2016 fellows, which include four principal investigators from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL): professors Erik Demaine, Fredo Durand, William Freeman, and Daniel Jackson. They were among the 1 percent of ACM members to receive the distinction.

Learning words from pictures

Speech recognition systems, such as those that convert speech to text on cellphones, are generally the result of machine learning. A computer pores through thousands or even millions of audio files and their transcriptions, and learns which acoustic features correspond to which typed words.But transcribing recordings is costly, time-consuming work, which has limited speech recognition to a small subset of languages spoken in wealthy nations.

Meeting of the minds for machine intelligence

Surviving breast cancer changed the course of Regina Barzilay’s research. The experience showed her, in stark relief, that oncologists and their patients lack tools for data-driven decision making. That includes what treatments to recommend, but also whether a patient’s sample even warrants a cancer diagnosis, she explained at the Nov. 10 Machine Intelligence Summit, organized by MIT and venture capital firm Pillar.“We do more machine learning when we decide on Amazon which lipstick you would buy,” said Barzilay, the Delta Electronics Professor of Electrical Engineering and Computer Science at MIT. “But not if you were deciding whether you should get treated for cancer.”

Entanglement bonanza

Quantum computers promise huge speedups on some computational problems because they harness a strange physical property called entanglement, in which the physical state of one tiny particle depends on measurements made of another. In quantum computers, entanglement is a computational resource, roughly like a chip’s clock cycles — kilohertz, megahertz, gigahertz — and memory in a conventional computer.

Enabling wireless virtual reality

One of the limits of today’s virtual reality (VR) headsets is that they have to be tethered to computers in order to process data well enough to deliver high-resolution visuals. But wearing an HDMI cable reduces mobility and can even lead to users tripping over cords.Fortunately, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have recently unveiled a prototype system called “MoVR” that allows gamers to use any VR headset wirelessly.

Artificial-intelligence system surfs web to improve its performance

Of the vast wealth of information unlocked by the Internet, most is plain text. The data necessary to answer myriad questions — about, say, the correlations between the industrial use of certain chemicals and incidents of disease, or between patterns of news coverage and voter-poll results — may all be online. But extracting it from plain text and organizing it for quantitative analysis may be prohibitively time consuming.

Faster programs, easier programming

Dynamic programming is a technique that can yield relatively efficient solutions to computational problems in economics, genomic analysis, and other fields. But adapting it to computer chips with multiple “cores,” or processing units, requires a level of programming expertise that few economists and biologists have.

CSAIL welcomes 6 new EECS faculty

CSAIL welcomes six new faculty members to MIT's Department of Electrical Engineering and Computer Science (EECS)!The new faculty include Adam Belay, Stefanie Mueller, Max Shulakar, David Sontag, Ryan Williams and Virginia Vassilev Williams.Adam Belay will join as an assistant professor in July 2017. Belay’s research area is operating systems and networking. Much of his work has focused on restructuring computer systems so that developers can more easily reach the full performance potential of hardware. Previously he worked on storage virtualization at VMware Inc. and contributed substantial power-management code to the Linux Kernel project.

Making computers explain themselves

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.

Finding patterns in corrupted data

Data analysis — and particularly big-data analysis — is often a matter of fitting data to some sort of mathematical model. The most familiar example of this might be linear regression, which finds a line that approximates a distribution of data points. But fitting data to probability distributions, such as the familiar bell curve, is just as common.If, however, a data set has just a few corrupted entries — say, outlandishly improbable measurements — standard data-fitting techniques can break down. This problem becomes much more acute with high-dimensional data, or data with many variables, which is ubiquitous in the digital age.

Making it easier to collaborate on code

Git is an open-source system with a polarizing reputation among programmers. It’s a powerful tool to help developers track changes to code, but many view it as prohibitively difficult to use.To make it more user-friendly, a team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) has developed “Gitless,” an interface that fixes many of the system’s core problems without fundamentally changing what it does.

MRIs for fetal health

Researchers from MIT, Boston Children's Hospital, and Massachusetts General Hospital have joined forces in an ambitious new project to use magnetic resonance imaging (MRI) to evaluate the health of fetuses.

Typically, fetal development is monitored with ultrasound imaging, which is cheap and portable and can gauge blood flow through the placenta, the organ in the uterus that delivers nutrients to the fetus. But MRI could potentially measure the concentration of different chemicals in the placenta and in fetal organs, which may have more diagnostic value.

Ankur Moitra named a 2016 Packard Fellow

Ankur Moitra, the Rockwell International Career Development Associate Professor of Mathematics, was named a 2016 David and Lucile Packard Fellow. Each of this year’s 18 award recipients will receive a five-year, unrestricted research grant totaling $875,000.“The mathematics department is extremely proud and happy that Ankur has received this well-deserved honor,” said Tomasz Mrowka, head of the Department of Mathematics and the Singer Professor of Mathematics at MIT. “He is the dream colleague: He is deeply intellectually curious, makes fundamental contributions to his discipline, and is an important part our teaching mission.”

CSAIL computer vision team leads scene parsing challenge

This week a team from CSAIL’s computer vision group co-hosted the first Scene Parsing Challenge at the 2016 European Conference on Computer Vision (ECCV) in Amsterdam. The challenge was focused on scene recognition, and using data to enable algorithms to classify and segment objects in scenes. Scene recognition has important applications in robotics and even psychology. Better algorithms could determine actions happening in a given environment, spot inconsistent objects or human behaviors, and even predict future events.

Professor Emeritus Whitman Richards dies at 84

Whitman Richards '53, PhD '65, professor emeritus of cognitive sciences and of media arts and sciences and principal investigator in the Computer Science and Artificial Intelligence Laboratory, died on Sept. 16 after a long battle with myelofibrosis. One of the first four PhD graduates of the Department of Brain and Cognitive Sciences (BCS), his more than 60 years at MIT were marked by a dedication to the experimental and theoretical study of vision, perception, and cognition.Richards began his affiliation with MIT as an undergraduate, matriculating in 1950. His decision to return to MIT for graduate work was greatly inspired by a meeting with BCS founder and then department head Professor Hans-Lukas Teuber.

Cambridge Cyber Summit convenes industry, academia, and government

On Oct. 5, MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) hosted a summit that brought together cybersecurity experts from business, government, and academia to talk about better ways to combat cyber-threats directed at companies and countries.Co-organized by the Aspen Institute and CNBC, the “Cambridge Cyber Summit” featured discussions with leaders that include Admiral Michael Rogers, director of the National Security Agency (NSA); and Andrew McCabe, deputy director of the Federal Bureau of Investigation (FBI).Taking place in MIT's Kresge Auditorium, the event included a mix of interviews and demos from top government officials, technologists, and “white hat” security hackers, as well as live coverage throughout the day on CNBC.

CSAIL spin-off helps launch Mayor Walsh's "Boston's Safest Driver" contest

Boston’s roads may be getting a little safer, thanks to drivers’ mobile phones. Traditionally one of the biggest sources of driver distraction, a new competition from the city of Boston is putting mobile phones to work to measure and improve users’ driving.CSAIL spin-off Cambridge Mobile Telematics launched "Boston’s Safest Driver Competition," aimed at improving Boston’s drivers with help from an app they developed, that gives feedback on how safely you're driving.Announced by Mayor Martin J. Walsh on Monday, the competition will use a smartphone app to score drivers on behaviors associated with safer driving. Drivers who have the safest records will be eligible for prizes throughout the competition, which runs through December 3.

Automated screening for childhood communication disorders

For children with speech and language disorders, early-childhood intervention can make a great difference in their later academic and social success. But many such children — one study estimates 60 percent — go undiagnosed until kindergarten or even later.Researchers at the Computer Science and Artificial Intelligence Laboratory at MIT and Massachusetts General Hospital’s Institute of Health Professions hope to change that, with a computer system that can automatically screen young children for speech and language disorders and, potentially, even provide specific diagnoses.

Y. Bryce Kim PhD `17 wins NSF award

This month CSAIL PhD candidate Yongwook Bryce Kim ‘17 received the National Science Foundation (NSF) Award for Young Professionals Contributing to Smart and Connected Health at the 38th Annual IEEE Engineering in Medicine and Biology Conference (EMBC’16). The theme of the conference was “empowering individual health care decisions through technology.” Kim was awarded for his research on using machine learning to efficiently query physiological time series data. His work provides a scalable system to rapidly retrieve “patients like me” from massive physiological time series repositories in intensive care units. Based on the retrieved neighboring patient set, his system performs time-critical subsequent tasks such as critical event prediction and anomaly detection.

Cache management improved once again

A year ago, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory unveiled a fundamentally new way of managing memory on computer chips, one that would use circuit space much more efficiently as chips continue to comprise more and more cores, or processing units. In chips with hundreds of cores, the researchers’ scheme could free up somewhere between 15 and 25 percent of on-chip memory, enabling much more efficient computation.

NSA Director Admiral Michael Rogers to open Cambridge Cyber Summit 10/5

It was announced today that National Security Agency Director and US Cyber Command Commander Admiral Michael Rogers will open our upcoming Cambridge Cyber Summit October 5, in conversation with The Aspen Institute’s President and CEO, Walter Isaacson. Join us to hear insights from Fort Meade as the United States’ top cybersecurity official shares his view of today’s most pressing cyber threats.

Videos

Ingestible robots, glasses-free 3-D, and computers that explain themselves

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

Design your own custom drone

This fall’s new FAA regulations have made drone flight easier than ever for both companies and consumers. But what if the drones out on the market aren’t exactly what you want?A new system from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) is the first to allow users to design, simulate, and build their own custom drone. Users can change the size, shape, and structure of their drone based on the specific needs they have for payload, cost, flight time, battery usage, and other factors.

Face to face with "The Enemy"

When the filmmaking pioneers Auguste and Louis Lumière screened their 1895 film, "The Arrival of a Train at La Ciotat," audiences were so frightened by the real appearance of the image that they screamed and got out of the way — or so a well-known anecdote goes. Today, as one enters a virtual reality (VR) space — such as that conjured by MIT Visiting Artist Karim Ben Khelifa in his vanguard project "The Enemy" — it is not uncommon for participants to experience a similar shock at the sounds of footsteps, then sudden presence of two soldiers in the room.

Computer learns to recognize sounds by watching video

In recent years, computers have gotten remarkably good at recognizing speech and images: Think of the dictation software on most cellphones, or the algorithms that automatically identify people in photos posted to Facebook.But recognition of natural sounds — such as crowds cheering or waves crashing — has lagged behind. That’s because most automated recognition systems, whether they process audio or visual information, are the result of machine learning, in which computers search for patterns in huge compendia of training data. Usually, the training data has to be first annotated by hand, which is prohibitively expensive for all but the highest-demand applications.

Study: carpooling apps could reduce taxi traffic 75%

Traffic is not just a nuisance for drivers: it’s also a public-health hazard and bad news for the economy.Transportation studies put the annual cost of congestion at $160 billion, which includes 7 billion hours of time lost to sitting in traffic and an extra 3 billion gallons of fuel burned. One way to improve traffic is through ride-sharing - and a new MIT study suggests that using carpooling options from companies like Uber and Lyft could reduce the number of taxis on the road 75 percent without significantly impacting travel time.

How the brain recognizes faces

MIT researchers and their colleagues have developed a new computational model of the human brain’s face-recognition mechanism that seems to capture aspects of human neurology that previous models have missed.The researchers designed a machine-learning system that implemented their model, and they trained it to recognize particular faces by feeding it a battery of sample images. They found that the trained system included an intermediate processing step that represented a face’s degree of rotation — say, 45 degrees from center — but not the direction — left or right.

Creating videos of the future

Living in a dynamic physical world, it’s easy to forget how effortlessly we understand our surroundings. With minimal thought, we can figure out how scenes change and objects interact.But what’s second nature for us is still a huge problem for machines. With the limitless number of ways that objects can move, teaching computers to predict future actions can be difficult.Recently, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have moved a step closer, developing a deep-learning algorithm that, given a still image from a scene, can create a brief video that simulates the future of that scene.

Teaching Hong Kong students to embrace computational thinking

CoolThink@JC, a four-year initiative of The Hong Kong Jockey Club Charities Trust, was launched today to empower the city’s primary school teachers and students with computational thinking skills, including coding.Developed through a collaboration with MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), the Education University of Hong Kong, and City University of Hong Kong, the eventual aim is to integrate computational thinking into all Hong Kong primary schools. Initially, CoolThink@JC will target over 16,500 students at 32 primary schools across the city.

Prepping a robot for its journey to Mars

Sarah Hensley is preparing an astronaut named Valkyrie for a mission to Mars. It is 6 feet tall, weighs 300 pounds, and is equipped with an extended chest cavity that makes it look distinctly female. Hensley spends much of her time this semester analyzing the movements of one of Valkyrie's arms.As a fourth-year electrical engineering student at MIT, Hensley is working with a team of CSAIL researchers to prepare Valkyrie, a humanoid robot also known as R5, for future space missions. As a teenager in New Jersey, Hensley loved to read in her downtime, particularly Isaac Asimov’s classic robot series. “I’m a huge science fiction nerd — and now I’m actually getting to work with a robot that’s real and not just in books. That’s like, wow.”

Designing for 3-D printing

3-D printing has progressed over the last decade to include multi-material fabrication, enabling production of powerful, functional objects. While many advances have been made, it still has been difficult for non-programmers to create objects made of many materials (or mixtures of materials) without a more user-friendly interface.But this week, a team from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) will present “Foundry,” a system for custom-designing a variety of 3-D printed objects with multiple materials.

3-D-printed robots with shock-absorbing skins

Anyone who’s watched drone videos or an episode of “BattleBots” knows that robots can break — and often it’s because they don’t have the proper padding to protect themselves.But this week researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) will present a new method for 3-D printing soft materials that make robots safer and more precise in their movements — and that could be used to improve the durability of drones, phones, shoes, helmets, and more.The team’s “programmable viscoelastic material” (PVM) technique allows users to program every single part of a 3D-printed object to the exact levels of stiffness and elasticity they want, depending on the task they need for it.

Detecting emotions with wireless signals

As many a relationship book can tell you, understanding someone else’s emotions can be a difficult task. Facial expressions aren’t always reliable: a smile can conceal frustration, while a poker face might mask a winning hand.But what if technology could tell us how someone is really feeling?Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed “EQ-Radio,” a device that can detect a person’s emotions using wireless signals.

Solving network congestion

There are few things more frustrating than trying to use your phone on a crowded network.

With phone usage growing faster than wireless spectrum, we’re all now fighting over smaller and smaller bits of bandwidth.

Spectrum crunch is such a big problem that the White House is getting involved, recently announcing both a $400 million research initiative and a $4 million global competition devoted to the issue.

Simit programming language can speed up simulations 200x, reduce code 90 percent

Computer simulations of physical systems are common in science, engineering, and entertainment, but they use several different types of tools.If, say, you want to explore how a crack forms in an airplane wing, you need a very precise physical model of the crack’s immediate vicinity. But if you want to simulate the flexion of an airplane wing under different flight conditions, it’s more practical to use a simpler, higher-level description of the wing.If, however, you want to model the effects of wing flexion on the crack’s propagation, or vice versa, you need to switch back and forth between these two levels of description, which is difficult not only for computer programmers but for computers, too.

New movie screen allows for glasses-free 3-D at a larger scale

3-D movies immerse us in new worlds and allow us to see places and things in ways that we otherwise couldn’t. But behind every 3-D experience is something that is uniformly despised: those goofy glasses.Fortunately, there may be hope. In a new paper, a team from MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) and Israel’s Weizmann Institute of Science have demonstrated a display that lets audiences watch 3-D films in a movie theater without extra eyewear.Dubbed “Cinema 3D,” the prototype uses a special array of lenses and mirrors to enable viewers to watch a 3-D movie from any seat in a theater.

Acoustic-filtering system could spur better earmuffs, mufflers & even musical instruments

A team from CSAIL has helped develop a simulation method called "Acoustic Voxels" that allows them to develop acoustic filters that can reduce certain sounds and amplify others. With researchers at Disney Research and Columbia University, the team has discovered a way to predict acoustic qualities 70,000 times faster than current algorithms. They have demonstrated their approach with 3D-printed designs that include mufflers, new wind instruments that can produce specific desired sounds, and “earmuffs” for motorcyclists that amplify car-horns and filter out wind-noise.

Robot helps nurses schedule tasks on labor floor

Today’s robots are awkward co-workers because they are often unable to predict what humans need. In hospitals, robots are employed to perform simple tasks such as delivering supplies and medications, but they have to be explicitly told what to do. A team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) thinks that this will soon change, and that robots might be most effective by helping humans perform one of the most complex tasks of all: scheduling. In a pair of new papers, CSAIL researchers demonstrate a robot that, by learning from human workers, can help assign and schedule tasks in fields ranging from medicine to the military.

Teaching machines to predict the future

When we see two people meet, we can often predict what happens next: a handshake, a hug, or maybe even a kiss. Our ability to anticipate actions is thanks to intuitions born out of a lifetime of experiences.Machines, on the other hand, have trouble making use of complex knowledge like that. Computer systems that predict actions would open up new possibilities ranging from robots that can better navigate human environments, to emergency response systems that predict falls, to Google Glass-style headsets that feed you suggestions for what to do in different situations.

Artificial intelligence produces realistic sounds that fool humans

For robots to navigate the world, they need to be able to make reasonable assumptions about their surroundings and what might happen during a sequence of events.One way that humans come to learn these things is through sound. For infants, poking and prodding objects is not just fun; some studies suggest that it’s actually how they develop an intuitive theory of physics. Could it be that we can get machines to learn the same way?Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have demonstrated an algorithm that has effectively learned how to predict sound: When shown a silent video clip of an object being hit, the algorithm can produce a sound for the hit that is realistic enough to fool human viewers.

Talks