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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

MIT team earns silver at ACM's global programming competition

This week the MIT Progamming Team earned silver at the World Finals of the Association for Computing Machinery's 40th annual International College Programming Contest (ICPC) in Phuket, Thailand.The world's most prestigious programming contest, ICPC involves 300,000 students from two thousand universities and 91 countries, with only the top 128 teams earning a spot in the finals. Led by CSAIL principal investigator Martin Rinard, the MIT team consisted of captain Aleksandar Zlateski, as well as Brian Chen, Steven Hao and Andrew He.

MIT launches $5 billion "Campaign for a Better World"

This past week MIT officially launched a new fundraising initiative aimed at advancing the institute's research and scholarship on some of the world's biggest challenges. Called the "MIT Campaign for a Better World", the effort aims to raise $5 billion that will go towards topics like climate change, clean energy, online education, The Campaign is guided by six priority areas that span the full breadth of MIT:

Ingestible origami robot can patch wounds inside your stomach!

In experiments involving a simulation of the human esophagus and stomach, researchers at CSAIL, the University of Sheffield, and the Tokyo Institute of Technology have demonstrated a tiny origami robot that can unfold itself from a swallowed capsule and, steered by external magnetic fields, crawl across the stomach wall to remove a swallowed button battery or patch a wound.The new work, which the researchers are presenting this week at the International Conference on Robotics and Automation, builds on a long sequence of papers on origami robots from the research group of CSAIL Director Daniela Rus, the Andrew and Erna Viterbi Professor in MIT’s Department of Electrical Engineering and Computer Science.

NASA's humanoid robot lands at CSAIL

This week MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) received an unusual package: a six-foot-tall, 300-pound humanoid robot that NASA hopes to have serve on future space missions to Mars and beyond.A team of researchers led by CSAIL principal investigator Russ Tedrake will program their new “Valkyrie” robot to autonomously perform a variety of challenging tasks that would allow it to help or even replace astronauts on missions.

Collision-free robots, guaranteed

Planning algorithms for teams of robots fall into two categories: centralized algorithms, in which a single computer makes decisions for the whole team, and decentralized algorithms, in which each robot makes its own decisions based on local observations.With centralized algorithms, if the central computer goes offline, the whole system falls apart. Decentralized algorithms handle erratic communication better, but they’re harder to design, because each robot is essentially guessing what the others will do. Most research on decentralized algorithms has focused on making collective decision-making more reliable and has deferred the problem of avoiding obstacles in the robots’ environment.

Self-driving cars, meet rubber duckies

MIT has offered courses on everything from pirate training to “street-fighting math,” but a new robotics class is truly one for the birds.This spring, a hands-on course housed at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) took students on a trip to “Duckietown.” The class’ goal was to create a fleet of 50 duckie-adorned self-driving taxis that can navigate the roads of a model city with just a single on-board camera and no pre-programmed maps.Beyond the class, Duckietown’s leaders have larger ambitions: to work with roboticists around the world to incorporate their open-source teaching materials and $100 “Duckiebot” design into other schools’ programs.

System predicts 85 percent of cyber-attacks using input from human experts

Today’s security systems usually fall into one of two categories: human or machine. So-called “analyst-driven solutions” rely on rules created by living experts and therefore miss any attacks that don’t match the rules. Meanwhile, today’s machine-learning approaches rely on “anomaly detection,” which tends to trigger false positives that both create distrust of the system and end up having to be investigated by humans, anyway.But what if there were a solution that could merge those two worlds? What would it look like?

“Flying Monkey” robot walks, grasps, and flies

A team that includes CSAIL researchers has designed a “flying monkey” robot that walks, grasps, flies, and clocks in at less than 1/10th of a pound. Modeled after the male stag beetle, the robot is part of a new class of robots capable of interacting with and modifying their surroundings, by using capabilities of legged and aerial robots.Part of the platform uses one of the world’s smallest quadrotor aircraft (“the Dragonfly”) and is powered by a single motor. Crawling, flying, and grasping allows the flying monkey to perform complex tasks such as hopping over obstacles, crawling under or through small openings, and picking up small objects.

First-ever 3-D printed robots made of both solids and liquids

One reason we don’t yet have robot personal assistants buzzing around doing our chores is because making them is hard. Assembling robots by hand is time-consuming, while automation — robots building other robots — is not yet fine-tuned enough to make robots that can do complex tasks.But if humans and robots can’t do the trick, what about 3-D printers?In a new paper, researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) present the first-ever technique for 3-D printing robots that involves printing solid and liquid materials at the same time.The new method allows the team to automatically 3-D print dynamic robots in a single step, with no assembly required, using a commercially-available 3-D printer.

This MIT PhD has his fingers on the pulse of virtual-reality

These days the buzz around virtual reality (VR) has never been bigger. Last month VCs invested $800 million in a secretive venture called Magic Leap, while just this week major platforms have finally hit the market from HTC’s Vive and Facebook’s Oculus VR. Oculus’ highly anticipated system, the Oculus Rift, features some intricate hand-tracking software courtesy of Robert Wang PhD ’11, whose start-up Nimble VR was bought by Oculus last year after raising more than $2 million in funding. Where most other hand-tracking systems require special sensors or markers, Nimble VR’s technology - with its infrared depth-sensing, 110-degree view and extremely accurate skeletal tracking of the hands - is completely glove-free.

Wireless tech means safer drones, smarter homes and password-free WiFi

We’ve all been there, impatiently twiddling our thumbs while trying to locate a WiFi signal. But what if, instead, the WiFi could locate us?According to researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), it could mean safer drones, smarter homes, and password-free WiFi.In a new paper, a research team led by Professor Dina Katabi present a system called Chronos that enables a single WiFi access point to locate users to within tens of centimeters, without any external sensors.

VIDEO: BB-8 droids deliver MIT admissions decisions!

Admissions decisions are arriving soon from a galaxy far, far away.Decisions for the Class of 2020 come out on Pi Day 3/14, at (when else?) 6:28 p.m. ET. To honor the occasion, the Admissions Office has released a new video starring Star Wars' BB-8 drones, which are apparently the means of transportation for MIT's roughly 1,500 admissions letters.More: http://mitadmissions.org/blogs/these-are-the-droids-youre-looking-for

NYT: "Smart robots make strides, but there's no need to flee just yet"

In assessing AI anxiety, the New York Times offers "reassuring views from computer scientists who sense that the end is not nigh" because "machines are not nearly as clever, or necessarily as pernicious, as the fretters believe."CSAIL researchers Daniela Rus, Russ Tedrake and Patrick Winston are part of a a new NYT documentary assessing the progress of artificial intelligence, as well as its continued challenges. From NYT:

Six steps to start-up success from serial entrepreneur Mike Stonebraker

Most entrepreneurs would consider themselves lucky to launch a single company.For MIT’s Michael Stonebraker, try nine. A researcher at MIT’s Computer Science and Artificial Intelligence Lab, Stonebraker has founded and led nine different big-data spin-offs, including VoltDB, Tamr and Vertica - the latter of which was bought by Hewlett Packard for $340 million.

Watch drones do donuts around obstacles thanks to planning algorithms

Getting drones to fly around without hitting things is no small task. Obstacle-detection and motion-planning are two of computer science’s trickiest challenges, because of the complexity involved in creating real-time flight plans that avoid obstacles and handle surprises like wind and weather.In a pair of projects announced this week, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) demonstrated software that allow drones to stop on a dime to make hairpin movements over, under, and around some 26 distinct obstacles in a simulated “forest.”

PhD takes 1 million photos of Boston skyline over 5 years

If a picture’s worth a thousand words, than Adrian Dalca is one seriously verbose researcher.Over the last five years, the CSAIL PhD student has been snapping away at the Boston skyline from his MIT apartment, taking approximately one million shots with an assortment of GoPros, phone cameras and point-and-shoots.The result: “Boston Timescape Project,” a thorough collection of skyline views taken in all of the many conditions and seasons of this fair city.

Two researchers named to Forbes' "30 Under 30" list

NBA All-Star Steph Curry. "Star Wars" actor John Boyega. Platinum-selling rapper Fetty Wap. And, of course, CSAIL researchers Abe Davis and Teasha Feldman-Fitzthum. Okay, those last two might not be household names, but they were among the select few picked to be part of Forbes "30 under 30" list, which highlights the world’s “game changers, movers and makers, and brightest minds” who are less than 30 years old.

Talks