Category Archives: Computer

Giving machine learning systems

Machine learning, which is the basis for most commercial artificial-intelligence systems, is intrinsically probabilistic. An object-recognition algorithm asked to classify a particular image, for instance, might conclude that it has a 60 percent chance of depicting a dog, but a 30 percent chance of depicting a cat.

At the Annual Conference on Neural Information Processing Systems in December, MIT researchers will present a new way of doing machine learning that enables semantically related concepts to reinforce each other. So, for instance, an object-recognition algorithm would learn to weigh the co-occurrence of the classifications “dog” and “Chihuahua” more heavily than it would the co-occurrence of “dog” and “cat.”

In experiments, the researchers found that a machine-learning algorithm that used their training strategy did a better job of predicting the tags that human users applied to images on the Flickr website than it did when it used a conventional training strategy.

“When you have a lot of possible categories, the conventional way of dealing with it is that, when you want to learn a model for each one of those categories, you use only data associated with that category,” says Chiyuan Zhang, an MIT graduate student in electrical engineering and computer science and one of the new paper’s lead authors. “It’s treating all other categories equally unfavorably. Because there are actually semantic similarities between those categories, we develop a way of making use of that semantic similarity to sort of borrow data from close categories to train the model.”

Zhang is joined on the paper by his thesis advisor, Tomaso Poggio, the Eugene McDermott Professor in the Brain Sciences and Human Behavior, and by his fellow first author Charlie Frogner, also a graduate student in Poggio’s group. Hossein Mobahi, a postdoc in the Computer Science and Artificial Intelligence Laboratory, and Mauricio Araya-Polo, a researcher with Shell Oil, round out the paper’s co-authors.

Close counts

To quantify the notion of semantic similarity, the researchers wrote an algorithm that combed through Flickr images identifying tags that tended to co-occur — for instance, “sunshine,” “water,” and “reflection.” The semantic similarity of two words was a function of how frequently they co-occurred.

Ordinarily, a machine-learning algorithm being trained to predict Flickr tags would try to identify visual features that consistently corresponded to particular tags. During training, it would be credited with every tag it got right but penalized for failed predictions.

The MIT researchers’ system essentially gives the algorithm partial credit for incorrect tags that are semantically related to the correct tags. Say, for instance, that a waterscape was tagged, among other things, “water,” “boat,” and “sunshine.” With conventional machine learning, a system that tagged that image “water,” “boat,” “summer” would get no more credit than one that tagged it “water,” “boat,” “rhinoceros.” With the researchers’ system, it would, and the credit would be a function of the likelihood that the tags “summer” and “sunshine” co-occur in the Flickr database.

Students decipher cryptography

Lincoln Laboratory recently unveiled LLCipher, a new outreach program for high school students. LLCipher is a one-week cryptography workshop that provides an introduction to modern cryptography — a math-based, theoretical approach to securing data.

Lessons in abstract algebra, number theory, and complexity theory provide students with the foundational knowledge needed to understand theoretical cryptography. Students build on that knowledge to construct provably secure encryption and digital signature schemes. On the last day, the students learn about some less mainstream developments in cryptography. Guest speakers cover various techniques that enable multiple entities to exchange data without disclosing to one another more data than necessary to perform a particular function. These include zero-knowledge proofs (proving a statement is true without revealing any information beyond the truth of the statement) and multiparty computation (computing a function over multiple parties’ inputs while keeping the inputs private).”

The course, held at Lincoln Laboratory’s Beaver Works facility near MIT campus, came about with the help of Bradley Orchard of the laboratory’s Advanced Sensor Systems and Test Beds Group and Sophia Yakoubov, Emily Shen, and David Wilson, all of the Secure Resilient Systems and Technology Group.

Orchard conceived the idea for LLCipher while teaching high-school students at the Russian School of Mathematics in Lexington, Massachusetts. He noticed a significant need for additional learning opportunities beyond what is offered in even the best high schools. Orchard noticed some very bright students were ready for material beyond calculus. “I thought it would be fun to offer a short introductory summer course for advanced high-school students,” he said. “I naturally thought of cryptography because it combines beautiful mathematics with powerful, useful, and fun techniques, and most importantly, aspects of cryptography are very accessible to advanced students.”

To design the course, Orchard asked for help from the laboratory’s management; John Wilkinson, leader of the Cyber Systems Assessments Group; and cryptography experts including Yakoubov, who, knowing how much she enjoyed teaching the CyberPatriot students, was eager to get involved.

“I cannot emphasize how difficult it is to design a short course that is effective, interesting, and fun for high-school students,” Orchard said. “Sophia did a superb job accomplishing this task, as evidenced by the enthusiasm and participation of the students. The students were engaged, asking questions, and demonstrating that they understood the material, and, most importantly, having fun.”

Chiamaka Agbasi-Porter of the Communications and Community Outreach Office handled the student applications and class logistics. Agbasi-Porter indicated that each of the 16 selected students were able to express their enjoyment of math through independent projects in their application to be a part of LLCipher. She felt the students for this pilot class were smart and enthusiastic. Yakoubov agreed: “The class was very interactive and the students were engaged in the topic; their questions and ideas made them a pleasure to teach.”

The first thing covered in class was encryption. The instructors started by explaining the one-time pad, which is a perfectly secure encryption scheme, meaning that an eavesdropping adversary cannot learn anything about the encrypted message no matter how much computing power he or she has. Perfectly secure encryption schemes have a significant drawback, however: The secret key shared by the message sender (typically called “Alice” in the industry) and message receiver (referred to as “Bob”) has to be at least as long as the message itself. Alice might want to send Bob very long messages, and might not know in advance how long these messages will be, so establishing a sufficiently long key might be problematic.

Other data helps machine learning systems

MIT researchers are developing a computer system that uses genetic, demographic, and clinical data to help predict the effects of disease on brain anatomy.

In experiments, they trained a machine-learning system on MRI data from patients with neurodegenerative diseases and found that supplementing that training with other patient information improved the system’s predictions. In the cases of patients with drastic changes in brain anatomy, the additional data cut the predictions’ error rate in half, from 20 percent to 10 percent.

“This is the first paper that we’ve ever written on this,” says Polina Golland, a professor of electrical engineering and computer science at MIT and the senior author on the new paper. “Our goal is not to prove that our model is the best model to do this kind of thing; it’s to prove that the information is actually in the data. So what we’ve done is, we take our model, and we turn off the genetic information and the demographic and clinical information, and we see that with combined information, we can predict anatomical changes better.”

First author on the paper is Adrian Dalca, an MIT graduate student in electrical engineering and computer science and a member of Golland’s group at MIT’s Computer Science and Artificial Intelligence Laboratory. They’re joined by Ramesh Sridharan, another PhD student in Golland’s group, and by Mert Sabuncu, an assistant professor of radiology at Massachusetts General Hospital, who was a postdoc in Golland’s group.

The researchers are presenting the paper at the International Conference on Medical Image Computing and Computer Assisted Intervention this week. The work is a project of the Neuroimage Analysis Center, which is based at Brigham and Women’s Hospital in Boston and funded by the National Institutes of Health.

Common denominator

In their experiments, the researchers used data from the Alzheimer’s Disease Neuroimaging Initiative, a longitudinal study on neurodegenerative disease that includes MRI scans of the same subjects taken months and years apart.

Each scan is represented as a three-dimensional model consisting of millions of tiny cubes, or “voxels,” the 3-D equivalent of image pixels.

The researchers’ first step is to produce a generic brain template by averaging the voxel values of hundreds of randomly selected MRI scans. They then characterize each scan in the training set for their machine-learning algorithm as a deformation of the template. Each subject in the training set is represented by two scans, taken between six months and seven years apart.

The researchers conducted two experiments: one in which they trained their system on scans of both healthy subjects and those displaying evidence of either Alzheimer’s disease or mild cognitive impairment, and one in which they trained it only on data from healthy subjects.

In the first experiment, they trained the system twice, once using just the MRI scans and the second time supplementing them with additional information. This included data on genetic markers known as single-nucleotide polymorphisms; demographic data, such as subject age, gender, marital status, and education level; and rudimentary clinical data, such as patients’ scores on various cognitive tests.

A secure foundation for any cryptographic system

“Indistinguishability obfuscation” is a powerful concept that would yield provably secure versions of every cryptographic system we’ve ever developed and all those we’ve been unable to develop. But nobody knows how to put it into practice.

Last week, at the IEEE Symposium on Foundations of Computer Science, MIT researchers showed that the problem of indistinguishability obfuscation is, in fact, a variation on a different cryptographic problem, called efficient functional encryption. And while computer scientists don’t know how to do efficient functional encryption, either, they believe that they’re close — much closer than they thought they were to indistinguishability obfuscation.

“This thing has really been studied for a longer time than obfuscation, and we’ve had a very nice progression of results achieving better and better functional-encryption schemes,” says Nir Bitansky, a postdoc in MIT’s Computer Science and Artificial Intelligence Laboratory who wrote the conference paper together with Vinod Vaikuntanathan, the Steven and Renee Finn Career Development Professor in the Department of Electrical Engineering and Computer Science. “People thought this is a small gap. Obfuscation — that’s another dimension. It’s much more powerful. There’s a huge gap there. What we did was really narrow this gap. Now if you want to do obfuscation and get all of crypto, everything that you can imagine, from standard assumptions, all that you have to do is solve this very specific problem, making functional encryption just a little bit more efficient.”

In computer science, “obfuscation” means disguising the operational details of a computer program so that it can’t be reverse-engineered. Many obfuscation techniques have been proposed, and many have been broken.

So computer scientists began investigating the idea theoretically. The ideal obfuscation scheme would take the source code for a program and rewrite it so that it still yields a working program, but it is impossible to determine what operations it was executing.

Theorists quickly proved that ideal obfuscation would enable almost any cryptographic scheme that they could dream up. But almost as quickly, they proved that it was impossible: There’s always a way to construct a program that can’t be perfectly obfuscated.

Fuzzy details

So they began investigating less-stringent theoretical principles, one of which was indistinguishability obfuscation. Rather than requiring that an adversary have no idea what operations the program is executing, indistinguishability obfuscation requires only that the adversary be unable to determine which of two versions of an operation it’s executing.

Most people recall from algebra, for instance, that a x (b + c) is the same thing as (a x b) + (a x c). For any given values, both expressions yield the same result, but they’d be executed differently on a computer. Indistinguishability obfuscation permits the adversary to determine that the program is performing one of those computations, but not which.

For years, the idea of indistinguishability obfuscation lay idle. But in the last few years, computer scientists have shown how to construct indistinguishability-obfuscation schemes from mathematical objects called multilinear maps. Remarkably, they also showed that even the weaker notion of indistinguishability obfuscation could yield all of cryptography.

But multilinear maps are not well understood, and it’s not clear that any of the proposed techniques for building them will offer the security guarantees that indistinguishability obfuscation requires.

Video of soccer games in real time

By exploiting the graphics-rendering software that powers sports video games, researchers at MIT and the Qatar Computing Research Institute (QCRI) have developed a system that automatically converts 2-D video of soccer games into 3-D.

The converted video can be played back over any 3-D device — a commercial 3-D TV, Google’s new Cardboard system, which turns smartphones into 3-D displays, or special-purpose displays such as Oculus Rift.

The researchers presented the new system last week at the Association for Computing Machinery’s Multimedia conference.

“Any TV these days is capable of 3-D,” says Wojciech Matusik, an associate professor of electrical engineering and computer science at MIT and one of the system’s co-developers. “There’s just no content. So we see that the production of high-quality content is the main thing that should happen. But sports is very hard. With movies, you have artists who paint the depth map. Here, there is no luxury of hiring 100 artists to do the conversion. This has to happen in real-time.”

The system is one result of a collaboration between QCRI and MIT’s Computer Science and Artificial Intelligence Laboratory. Joining Matusik on the conference paper are Kiana Calagari, a research associate at QCRI and first author; Alexandre Kaspar, an MIT graduate student in electrical engineering and computer science; Piotr Didyk, who was a postdoc in Matusik’s group and is now a researcher at the Max Planck Institute for Informatics; Mohamed Hefeeda, a principal scientist at QCRI; and Mohamed Elgharib, a QCRI postdoc. QCRI also helped fund the project.

Zeroing in

In the past, researchers have tried to develop general-purpose systems for converting 2-D video to 3-D, but they haven’t worked very well and have tended to produce odd visual artifacts that detract from the viewing experience.

“Our advantage is that we can develop it for a very specific problem domain,” Matusik says. “We are developing a conversion pipeline for a specific sport. We would like to do it at broadcast quality, and we would like to do it in real-time. What we have noticed is that we can leverage video games.”

Today’s video games generally store very detailed 3-D maps of the virtual environment that the player is navigating. When the player initiates a move, the game adjusts the map accordingly and, on the fly, generates a 2-D projection of the 3-D scene that corresponds to a particular viewing angle.

The MIT and QCRI researchers essentially ran this process in reverse. They set the very realistic Microsoft soccer game “FIFA13” to play over and over again, and used Microsoft’s video-game analysis tool PIX to continuously store screen shots of the action. For each screen shot, they also extracted the corresponding 3-D map.

Using a standard algorithm for gauging the difference between two images, they winnowed out most of the screen shots, keeping just those that best captured the range of possible viewing angles and player configurations that the game presented; the total number of screen shots still ran to the tens of thousands. Then they stored each screen shot and the associated 3-D map in a database.

Jigsaw puzzle

For every frame of 2-D video of an actual soccer game, the system looks for the 10 or so screen shots in the database that best correspond to it. Then it decomposes all those images, looking for the best matches between smaller regions of the video feed and smaller regions of the screen shots. Once it’s found those matches, it superimposes the depth information from the screen shots on the corresponding sections of the video feed. Finally, it stitches the pieces back together.

The result is a very convincing 3-D effect, with no visual artifacts. The researchers conducted a user study in which the majority of subjects gave the 3-D effect a rating of 5 (“excellent”) on a five-point (“bad” to “excellent”) scale; the average score was between 4 (“good”) and 5.

Currently, the researchers say, the system takes about a third of a second to process a frame of video. But successive frames could all be processed in parallel, so that the third-of-a-second delay needs to be incurred only once. A broadcast delay of a second or two would probably provide an adequate buffer to permit conversion on the fly. Even so, the researchers are working to bring the conversion time down still further.

Depth sensor to approximate

MIT researchers have developed a biomedical imaging system that could ultimately replace a $100,000 piece of a lab equipment with components that cost just hundreds of dollars.

The system uses a technique called fluorescence lifetime imaging, which has applications in DNA sequencing and cancer diagnosis, among other things. So the new work could have implications for both biological research and clinical practice.

“The theme of our work is to take the electronic and optical precision of this big expensive microscope and replace it with sophistication in mathematical modeling,” says Ayush Bhandari, a graduate student at the MIT Media Lab and one of the system’s developers. “We show that you can use something in consumer imaging, like the Microsoft Kinect, to do bioimaging in much the same way that the microscope is doing.”

The MIT researchers reported the new work in the Nov. 20 issue of the journal Optica. Bhandari is the first author on the paper, and he’s joined by associate professor of media arts and sciences Ramesh Raskar and Christopher Barsi, a former research scientist in Raskar’s group who now teaches physics at the Commonwealth School in Boston.

Fluorescence lifetime imaging, as its name implies, depends on fluorescence, or the tendency of materials known as fluorophores to absorb light and then re-emit it a short time later. For a given fluorophore, interactions with other chemicals will shorten the interval between the absorption and emission of light in a predictable way. Measuring that interval — the “lifetime” of the fluorescence — in a biological sample treated with a fluorescent dye can reveal information about the sample’s chemical composition.

In traditional fluorescence lifetime imaging, the imaging system emits a burst of light, much of which is absorbed by the sample, and then measures how long it takes for returning light particles, or photons, to strike an array of detectors. To make the measurement as precise as possible, the light bursts are extremely short.

The fluorescence lifetimes pertinent to biomedical imaging are in the nanosecond range. So traditional fluorescence lifetime imaging uses light bursts that last just picoseconds, or thousandths of nanoseconds.

Blunt instrument

Off-the-shelf depth sensors like the Kinect, however, use light bursts that last tens of nanoseconds. That’s fine for their intended purpose: gauging objects’ depth by measuring the time it takes light to reflect off of them and return to the sensor. But it would appear to be too coarse-grained for fluorescence lifetime imaging.

The Media Lab researchers, however, extract additional information from the light signal by subjecting it to a Fourier transform. The Fourier transform is a technique for breaking signals — optical, electrical, or acoustical — into their constituent frequencies. A given signal, no matter how irregular, can be represented as the weighted sum of signals at many different frequencies, each of them perfectly regular.

The Media Lab researchers represent the optical signal returning from the sample as the sum of 50 different frequencies. Some of those frequencies are higher than that of the signal itself, which is how they are able to recover information about fluorescence lifetimes shorter than the duration of the emitted burst of light.

For each of those 50 frequencies, the researchers measure the difference in phase between the emitted signal and the returning signal. If an electromagnetic wave can be thought of as a regular up-and-down squiggle, phase is the degree of alignment between the troughs and crests of one wave and those of another. In fluorescence imaging, phase shift also carries information about the fluorescence lifetime.

The communication connections established by the top 500 Android apps

MIT researchers have found that much of the data transferred to and from the 500 most popular free applications for Google Android cellphones make little or no difference to the user’s experience.

Of those “covert” communications, roughly half appear to be initiated by standard Android analytics packages, which report statistics on usage patterns and program performance and are intended to help developers improve applications.

“The interesting part is that the other 50 percent cannot be attributed to analytics,” says Julia Rubin, a postdoc in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), who led the new study. “There might be a very good reason for this covert communication. We are not trying to say that it has to be eliminated. We’re just saying the user needs to be informed.”

The researchers reported their findings last week at the IEEE/ACM International Conference on Automated Software Engineering. Joining Rubin on the paper are Martin Rinard, a professor of computer science and engineering at MIT; Michael Gordon, who received his PhD in electrical engineering and computer science in 2010 and remained at CSAIL as a researcher until last July; and Nguyen Nguyen of the engineering firm UWin Software.

Different operations performed by the same mobile app may require outside communication, and rather than try to coordinate shared access to a single communication channel, the app will typically open a separate communication channel for each operation.

The researchers analyzed the number of communication channels opened by the 500 most popular mobile apps and found that roughly 50 percent of them appear to have no bearing on the user experience. That doesn’t necessarily translate directly to the quantity of data exchanged over those channels, but for short sessions of application use, the portion of transmitted data irrelevant to user experience is also as much as 50 percent.

Across longer sessions, in which large files are transferred to the phone — by, say, music- or video-streaming services — the percentage of data transmitted covertly steadily diminishes. But covert communication channels remain open.

Piercing the veil

Mobile applications are usually proprietary: Their source code is not publicly available, and their developers frequently take pains to disguise the details of the programs’ execution, a technique known as obfuscation.

Algorithm speeds up complex modeling from days to hours

To work with computational models is to work in a world of unknowns: Models that simulate complex physical processes — from Earth’s changing climate to the performance of hypersonic combustion engines — are staggeringly complex, sometimes incorporating hundreds of parameters, each of which describes a piece of the larger process.

Parameters are often question marks within their models, their contributions to the whole largely unknown. To estimate the value of each unknown parameter requires plugging in hundreds, if not thousands, of values, and running the model each time to narrow in on an accurate value — a computation that can take days, and sometimes weeks.

Now MIT researchers have developed a new algorithm that vastly reduces the computation of virtually any computational model. The algorithm may be thought of as a shrinking bull’s-eye that, over several runs of a model, and in combination with some relevant data points, incrementally narrows in on its target: a probability distribution of values for each unknown parameter.

With this method, the researchers were able to arrive at the same answer as a classic computational approaches, but 200 times faster.

Youssef Marzouk, an associate professor of aeronautics and astronautics, says the algorithm is versatile enough to apply to a wide range of computationally intensive problems.

“We’re somewhat flexible about the particular application,” Marzouk says. “These models exist in a vast array of fields, from engineering and geophysics to subsurface modeling, very often with unknown parameters. We want to treat the model as a black box and say, ‘Can we accelerate this process in some way?’ That’s what our algorithm does.”

Marzouk and his colleagues — recent PhD graduate Patrick Conrad, Natesh Pillai from Harvard University, and Aaron Smith from the University of Ottawa — have published their findings this week in the Journal of the American Statistical Association.

Modeling “Monopoly”

In working with complicated models involving multiple unknown parameters, computer scientists typically employ a technique called Markov chain Monte Carlo (MCMC) analysis — a statistical sampling method that is often explained in the context of the board game “Monopoly.”

To plan out a monopoly, you want to know which properties players land on most often — essentially, an unknown parameter. Each space on the board has a probability of being landed on, determined by the rules of the game, the positions of each player, and the roll of two dice. To determine the probability distribution on the board — the range of chances each space has of being landed on — you could roll the die hundreds of times.

If you roll the die enough times, you can get a pretty good idea of where players will most likely land. This, essentially, is how an MCMC analysis works: by running a model over and over, with different inputs, to determine a probability distribution for one unknown parameter. For more complicated models involving multiple unknowns, the same method could take days to weeks to compute an answer.

Shrinking bull’s-eye

With their new algorithm, Marzouk and his colleagues aim to significantly speed up the conventional sampling process.

“What our algorithm does is short-circuits this model and puts in an approximate model,” Marzouk explains. “It may be orders of magnitude cheaper to evaluate.”

The algorithm can be applied to any complex model to quickly determine the probability distribution, or the most likely values, for an unknown parameter. Like the MCMC analysis, the algorithm runs a given model with various inputs — though sparingly, as this process can be quite time-consuming. To speed the process up, the algorithm also uses relevant data to help narrow in on approximate values for unknown parameters.

In the context of “Monopoly,” imagine that the board is essentially a three-dimensional terrain, with each space represented as a peak or valley. The higher a space’s peak, the higher the probability that space is a popular landing spot. To figure out the exact contours of the board — the probability distribution — the algorithm rolls the die at each turn and alternates between using the computationally expensive model and the approximation. With each roll of the die, the algorithm refers back to the relevant data and any previous evaluations of the model that have been collected.

Mobile image processing in the cloud

As smartphones become people’s primary computers and their primary cameras, there is growing demand for mobile versions of image-processing applications.

Image processing, however, can be computationally intensive and could quickly drain a cellphone’s battery. Some mobile applications try to solve this problem by sending image files to a central server, which processes the images and sends them back. But with large images, this introduces significant delays and could incur costs for increased data usage.

At the Siggraph Asia conference last week, researchers from MIT, Stanford University, and Adobe Systems presented a system that, in experiments, reduced the bandwidth consumed by server-based image processing by as much as 98.5 percent, and the power consumption by as much as 85 percent.

The system sends the server a highly compressed version of an image, and the server sends back an even smaller file, which contains simple instructions for modifying the original image.

Michaël Gharbi, a graduate student in electrical engineering and computer science at MIT and first author on the Siggraph paper, says that the technique could become more useful as image-processing algorithms become more sophisticated.

“We see more and more new algorithms that leverage large databases to take a decision on the pixel,” Gharbi says. “These kinds of algorithm don’t do a very complex transform if you go to a local scale on the image, but they still require a lot of computation and access to the data. So that’s the kind of operation you would need to do on the cloud.”

One example, Gharbi says, is recent work at MIT that transfers the visual styles of famous portrait photographers to cellphone snapshots. Other researchers, he says, have experimented with algorithms for changing the apparent time of day at which photos were taken.

Joining Gharbi on the new paper are his thesis advisor, Frédo Durand, a professor of computer science and engineering; YiChang Shih, who received his PhD in electrical engineering and computer science from MIT in March; Gaurav Chaurasia, a former postdoc in Durand’s group who’s now at Disney Research; Jonathan Ragan-Kelley, who has been a postdoc at Stanford since graduating from MIT in 2014; and Sylvain Paris, who was a postdoc with Durand before joining Adobe.

The MIT Media Lab and Tufts University

Based on the ScratchJr programming language co-developed by the MIT Media Lab and Tufts University, PBS has released PBS KIDS ScratchJr, a free app to help children ages 5-8 learn coding concepts as they create their own stories and games using over 150 PBS KIDS characters.

With the PBS KIDS ScratchJr app, kids can snap together colorful programming blocks to make their favorite characters move, jump, dance, and sing. In the process, they learn to solve problems, design projects, and express themselves creatively. The free app is now available from the App Store on iPad and from the Google Play store on Android tablet.

Through outreach efforts supported by the Verizon Foundation and the Ready To Learn Initiative, PBS member stations will extend the reach of PBS KIDS ScratchJr to children in underserved communities across the U.S. through programs and partnerships with Title I schools. Verizon will also be supporting the development of after-school activities and a weeklong summer camp. In addition, PBS stations will provide professional development training pilots to help teachers integrate PBS KIDS ScratchJr into classroom activities.

“We see coding as a new way for people to organize, express, and share their ideas,” said Mitchel Resnick, the LEGO Papert Professor of Learning Research at MIT, head of the Media Lab’s Lifelong Kindergarten group, and director of its Scratch team. “Coding is not just a set of technical skills, but a new type of literacy and personal expression, valuable for everyone, much like learning to write.”

To help ScratchJr learners get more out of the programming language, Media Lab alumna Professor Marina Umaschi Bers, director of the Developmental Technologies Research Group at Tufts University, and Resnick have co-authored “The Official ScratchJr Book: Help Your Kids Learn to Code,” released in November.

The app has been developed as part of the Corporation for Public Broadcasting and PBS Ready To Learn Initiative with funding from the U.S. Department of Education. Ready To Learn is a federal program that supports the development of innovative educational television and digital media targeted at preschool and early elementary school children and their families.