Every person of the world who wants to learn something

Raul Boquin, now an MIT senior, remembers the assignment from his freshman year as if it were yesterday. During a leadership workshop, he was asked to write a headline for a newspaper in his imagined future. The words that came to mind resonated so strongly that they now hang on the walls of his dorm room: “Equal opportunities in education for all.”

“I realized that I didn’t come to MIT because it was the best engineering school, but because it was the best place to discover what I was truly passionate about,” he says. “MIT pushed me to my limits and made me able to say ‘I don’t have to be the number one math person, or the number one computer science person, to make a difference’ with the passion I ended up having, which is education.”

Boquin, who is majoring in mathematics with computer science, predicts his life’s work will be to “find a way to adapt education to every person of the world who wants to learn something.”

More to education than teaching

Boquin’s first forays into education followed a relatively traditional path. As part of the undergraduate coursework he needed for his education concentration, he spent time observing teachers in local middle and high schools.

“But at the end of sophomore year, I realized that there was a lot more to education than just teaching.

The summer before his junior year, Boquin worked as a counselor and teaching assistant at Bridge to Enter Advanced Mathematics (BEAM). “It originally started as just a math camp for students in the summer, teaching them things like topology and number theory,” Boquin says. “These were seventh grade Hispanic and black children, and they loved it. And they were amazing at it.”

On a campus in upstate New York, Boquin taught classes by day and talked to students about his own work in mathematics by night. He also designed parts of the BEAM curriculum and came up with fun ways of teaching the lessons. “It was inspiring because it was like I wasn’t only a teacher, but I was a mentor and a friend,” he says.

Back at MIT, with the guidance of Eric Klopfer, professor and director of the Scheller Teacher Education Program and the Education Arcade, Boquin joined lead developer Paul Medlock-Walton to work on Gameblox, through MIT’s Undergraduate Research Opportunities Program (UROP).

Boquin describes Gameblox as a blocks programming language, in which users put blocks together to make something happen in the program. He worked on the user interface of the program, wrote tutorials for features, and built a framework for other researchers to test new code and features. His favorite part, though, was working on a Gameblox curriculum.

“I researched ways of finding out how teachers could use Gameblox to teach not only math and science, but also English, and history, and geography, and how to incorporate programming concepts in different levels of education,” Boquin says. “The features that I got to add to Gameblox as an engineer, I got to test, live, right afterward with teachers from Boston, or with students.”

Security protocols to thwart hijacked servers

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

At the IEEE Symposium on Security and Privacy this week, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory are presenting a new system that uses Bitcoin’s security machinery to defend against online identity theft.

“Our paper is about using Bitcoin to prevent online services from getting away with lying,” says Alin Tomescu, a graduate student in electrical engineering and computer science and first author on the paper. “When you build systems that are distributed and send each other digital signatures, for instance, those systems can be compromised, and they can lie. They can say one thing to one person and one thing to another. And we want to prevent that.”

An attacker who hacked a public-key encryption system, for instance, might “certify” — or cryptographically assert the validity of — a false encryption key, to trick users into revealing secret information. But it couldn’t also decertify the true key without setting off alarms, so there would be two keys in circulation bearing certification from the same authority. The new system, which Tomescu developed together with his thesis advisor, Srini Devadas, the Edwin Sibley Webster Professor of Electrical Engineering and Computer Science at MIT, defends against such “equivocation.”

Because Bitcoin is completely decentralized, the only thing ensuring its reliability is a massive public log — referred to as the blockchain — of every Bitcoin transaction conducted since the system was first introduced in 2009. Earlier systems have used the Bitcoin machinery to guard against equivocation, but for verification, they required the download of the entire blockchain, which is 110 gigabytes and growing hourly. Tomescu and Devadas’ system, by contrast, requires the download of only about 40 megabytes of data, so it could run on a smartphone.

Striking paydirt

Extending the blockchain is integral to the process of minting — or in Bitcoin terminology, “mining” — new bitcoins. The mining process is built around a mathematical function, called a one-way hash function, that takes three inputs: the last log entry in the blockchain; a new blockchain entry, in which the miner awards him- or herself a fixed number of new bitcoins (currently 12.5); and an integer. The output of the function is a string of 1s and 0s.

The inner workings of neural networks

Neural networks, which learn to perform computational tasks by analyzing large sets of training data, are responsible for today’s best-performing artificial intelligence systems, from speech recognition systems, to automatic translators, to self-driving cars.

But neural nets are black boxes. Once they’ve been trained, even their designers rarely have any idea what they’re doing — what data elements they’re processing and how.

Two years ago, a team of computer-vision researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) described a method for peering into the black box of a neural net trained to identify visual scenes. The method provided some interesting insights, but it required data to be sent to human reviewers recruited through Amazon’s Mechanical Turk crowdsourcing service.

At this year’s Computer Vision and Pattern Recognition conference, CSAIL researchers will present a fully automated version of the same system. Where the previous paper reported the analysis of one type of neural network trained to perform one task, the new paper reports the analysis of four types of neural networks trained to perform more than 20 tasks, including recognizing scenes and objects, colorizing grey images, and solving puzzles. Some of the new networks are so large that analyzing any one of them would have been cost-prohibitive under the old method.

The researchers also conducted several sets of experiments on their networks that not only shed light on the nature of several computer-vision and computational-photography algorithms, but could also provide some evidence about the organization of the human brain.

Neural networks are so called because they loosely resemble the human nervous system, with large numbers of fairly simple but densely connected information-processing “nodes.” Like neurons, a neural net’s nodes receive information signals from their neighbors and then either “fire” — emitting their own signals — or don’t. And as with neurons, the strength of a node’s firing response can vary.

In both the new paper and the earlier one, the MIT researchers doctored neural networks trained to perform computer vision tasks so that they disclosed the strength with which individual nodes fired in response to different input images. Then they selected the 10 input images that provoked the strongest response from each node.

In the earlier paper, the researchers sent the images to workers recruited through Mechanical Turk, who were asked to identify what the images had in common. In the new paper, they use a computer system instead.

Paper folding patterns to produce any 3 D structure.

In a 1999 paper, Erik Demaine — now an MIT professor of electrical engineering and computer science, but then an 18-year-old PhD student at the University of Waterloo, in Canada — described an algorithm that could determine how to fold a piece of paper into any conceivable 3-D shape.

It was a milestone paper in the field of computational origami, but the algorithm didn’t yield very practical folding patterns. Essentially, it took a very long strip of paper and wound it into the desired shape. The resulting structures tended to have lots of seams where the strip doubled back on itself, so they weren’t very sturdy.

At the Symposium on Computational Geometry in July, Demaine and Tomohiro Tachi of the University of Tokyo will announce the completion of a quest that began with that 1999 paper: a universal algorithm for folding origami shapes that guarantees a minimum number of seams.

“In 1999, we proved that you could fold any polyhedron, but the way that we showed how to do it was very inefficient,” Demaine says. “It’s efficient if your initial piece of paper is super-long and skinny. But if you were going to start with a square piece of paper, then that old method would basically fold the square paper down to a thin strip, wasting almost all the material. The new result promises to be much more efficient. It’s a totally different strategy for thinking about how to make a polyhedron.”

Demaine and Tachi are also working to implement the algorithm in a new version of Origamizer, the free software for generating origami crease patterns whose first version Tachi released in 2008.

Maintaining boundaries

The researchers’ algorithm designs crease patterns for producing any polyhedron — that is, a 3-D surface made up of many flat facets. Computer graphics software, for instance, models 3-D objects as polyhedra consisting of many tiny triangles. “Any curved shape you could approximate with lots of little flat sides,” Demaine explains.

Technically speaking, the guarantee that the folding will involve the minimum number of seams means that it preserves the “boundaries” of the original piece of paper. Suppose, for instance, that you have a circular piece of paper and want to fold it into a cup. Leaving a smaller circle at the center of the piece of paper flat, you could bunch the sides together in a pleated pattern; in fact, some water-cooler cups are manufactured on this exact design.

In this case, the boundary of the cup — its rim — is the same as that of the unfolded circle — its outer edge. The same would not be true with the folding produced by Demaine and his colleagues’ earlier algorithm. There, the cup would consist of a thin strip of paper wrapped round and round in a coil — and it probably wouldn’t hold water.

“The new algorithm is supposed to give you much better, more practical foldings,” Demaine says. “We don’t know how to quantify that mathematically, exactly, other than it seems to work much better in practice. But we do have one mathematical property that nicely distinguishes the two methods. The new method keeps the boundary of the original piece of paper on the boundary of the surface you’re trying to make. We call this watertightness.”

A closed surface — such as a sphere — doesn’t have a boundary, so an origami approximation of it will require a seam where boundaries meet. But “the user gets to choose where to put that boundary,” Demaine says. “You can’t get an entire closed surface to be watertight, because the boundary has to be somewhere, but you get to choose where that is.”

Decisions when outcomes are uncertain

Markov decision processes are mathematical models used to determine the best courses of action when both current circumstances and future consequences are uncertain. They’ve had a huge range of applications — in natural-resource management, manufacturing, operations management, robot control, finance, epidemiology, scientific-experiment design, and tennis strategy, just to name a few.

But analyses involving Markov decision processes (MDPs) usually make some simplifying assumptions. In an MDP, a given decision doesn’t always yield a predictable result; it could yield a range of possible results. And each of those results has a different “value,” meaning the chance that it will lead, ultimately, to a desirable outcome.

Characterizing the value of given decision requires collection of empirical data, which can be prohibitively time consuming, so analysts usually just make educated guesses. That means, however, that the MDP analysis doesn’t guarantee the best decision in all cases.

In the Proceedings of the Conference on Neural Information Processing Systems, published last month, researchers from MIT and Duke University took a step toward putting MDP analysis on more secure footing. They show that, by adopting a simple trick long known in statistics but little applied in machine learning, it’s possible to accurately characterize the value of a given decision while collecting much less empirical data than had previously seemed necessary.

In their paper, the researchers described a simple example in which the standard approach to characterizing probabilities would require the same decision to be performed almost 4 million times in order to yield a reliable value estimate.

With the researchers’ approach, it would need to be run 167,000 times. That’s still a big number — except, perhaps, in the context of a server farm processing millions of web clicks per second, where MDP analysis could help allocate computational resources. In other contexts, the work at least represents a big step in the right direction.

“People are not going to start using something that is so sample-intensive right now,” says Jason Pazis, a postdoc at the MIT Laboratory for Information and Decision Systems and first author on the new paper. “We’ve shown one way to bring the sample complexity down. And hopefully, it’s orthogonal to many other ways, so we can combine them.”

Unpredictable outcomes

In their paper, the researchers also report running simulations of a robot exploring its environment, in which their approach yielded consistently better results than the existing approach, even with more reasonable sample sizes — nine and 105. Pazis emphasizes, however, that the paper’s theoretical results bear only on the number of samples required to estimate values; they don’t prove anything about the relative performance of different algorithms at low sample sizes.

Pazis is joined on the paper by Jonathan How, the Richard Cockburn Maclaurin Professor of Aeronautics and Astronautics at MIT, and by Ronald Parr, a professor of computer science at Duke.

The Microsoft Windows targeting system

The ransomware program WannaCry, launched on May 12, targets the Microsoft Windows operating system. While this malware has infected over 200,000 computers worldwide, the attack affected around 100 computers across the 50,000 devices on the MIT network.

This limited impact is due to the many security services provided to the community by MIT Information Systems and Technology (IS&T).

“MIT values an open network to foster research, innovation and collaborative learning,” says IS&T Associate Vice President Mark Silis. “We continuously strive to balance potential security risks with the benefits of our open network environment by offering a number of security services to our community, including Sophos anti-virus, CrowdStrike anti-malware, and CrashPlan backup.

“IS&T staff are working with faculty, staff, and students to secure their devices and address any remaining issues related to WannaCry. In the weeks ahead, our department will continue to educate and advise the MIT community.”

A post on the CISCO Talos blog provides in-depth technical details about the WannaCry ransomware attack.

Preventive measures

IS&T strongly recommends that community members take this opportunity to make sure their Windows machines are fully patched, especially with the MS17-010 Security Update. Microsoft has even released patches for Windows XP, Windows 8, and Windows Server 2003, which are no longer officially supported.

In addition, IS&T recommends installing Sophos and CrowdStrike. These programs successfully block the execution of WannaCry ransomware on machines where they have been installed. A third program, CrashPlan, is also recommended. This cloud-based offering, which runs continuously in the background, securely encrypts and backs up data on computers. Should files be lost due to ransomware or a computer breakdown, restoring data is straightforward.

IS&T offers these three programs to the MIT community at no cost and can help with installation questions. The department also encourages users to enable operating system firewalls on computers and laptops.

Drive suggest another approach to developing flying cars

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

The problem is that robots that are good at one mode of transportation are usually bad at another. Airborne drones are fast and agile, but generally have too limited of a battery life to travel for long distances. Ground vehicles, on the other hand, are more energy efficient, but slower and less mobile.

Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) are aiming to develop robots that can both maneuver around on land and take to the skies. In a new paper, the team presented a system of eight quadcopter drones that can fly and drive through a city-like setting with parking spots, no-fly zones, and landing pads.

“The ability to both fly and drive is useful in environments with a lot of barriers, since you can fly over ground obstacles and drive under overhead obstacles,” says PhD student Brandon Araki, lead author on the paper. “Normal drones can’t maneuver on the ground at all. A drone with wheels is much more mobile while having only a slight reduction in flying time.”

Araki and CSAIL Director Daniela Rus developed the system, along with MIT undergraduate students John Strang, Sarah Pohorecky, and Celine Qiu, and Tobias Naegeli of ETH Zurich’s Advanced Interactive Technologies Lab. The team presented their system at IEEE’s International Conference on Robotics and Automation (ICRA) in Singapore earlier this month.

How it works

The project builds on Araki’s previous work developing a “flying monkey” robot that crawls, grasps, and flies. While the monkey robot could hop over obstacles and crawl about, there was still no way for it to travel autonomously.

To address this, the team developed various “path-planning” algorithms aimed at ensuring that the drones don’t collide. To make them capable of driving, the team put two small motors with wheels on the bottom of each drone. In simulations, the robots could fly for 90 meters or drive for 252 meters, before their batteries ran out.

Adding the driving component to the drone slightly reduced its battery life, meaning that the maximum distance it could fly decreased 14 percent to about 300 feet. But since driving is still much more efficient than flying, the gain in efficiency from driving more than offsets the relatively small loss in efficiency in flying due to the extra weight.

“This work provides an algorithmic solution for large-scale, mixed-mode transportation and shows its applicability to real-world problems,” says Jingjin Yu, a computer science professor at Rutgers University who was not involved in the research.

The team also tested the system using everyday materials such as pieces of fabric for roads and cardboard boxes for buildings. They tested eight robots navigating from a starting point to an ending point on a collision-free path, and all were successful.

Rus says that systems like theirs suggest that another approach to creating safe and effective flying cars is not to simply “put wings on cars,” but to build on years of research in adding driving capabilities to drones.

Hardware offers alternative to 3-D

Last year, a team of forensic dentists got authorization to perform a 3-D scan of the prized Tyrannosaurus rex skull at the Field Museum of Natural History in Chicago, in an effort to try to explain some strange holes in the jawbone.

Upon discovering that their high-resolution dental scanners couldn’t handle a jaw as big as a tyrannosaur’s, they contacted the Camera Culture group at MIT’s Media Lab, which had recently made headlines with a prototype system for producing high-resolution 3-D scans.

The prototype wasn’t ready for a job that big, however, so Camera Culture researchers used $150 in hardware and some free software to rig up a system that has since produced a 3-D scan of the entire five-foot-long T. rex skull, which a team of researchers — including dentists, anthropologists, veterinarians, and paleontologists — is using to analyze the holes.

The Media Lab researchers report their results in the latest issue of the journal PLOS One.

“A lot of people will be able to start using this,” says Anshuman Das, a research scientist at the Camera Culture group and first author on the paper. “That’s the message I want to send out to people who would generally be cut off from using technology — for example, paleontologists or museums that are on a very tight budget. There are so many other fields that could benefit from this.”

Das is joined on the paper by Ramesh Raskar, a professor of media arts and science at MIT, who directs the Camera Culture group, and by Denise Murmann and Kenneth Cohrn, the forensic dentists who launched the project.

The system uses a Microsoft Kinect, a depth-sensing camera designed for video gaming. The Kinect’s built-in software produces a “point cloud,” a 3-D map of points in a visual scene from which short bursts of infrared light have been reflected back to a sensor. Free software called MeshLab analyzes the point cloud and infers the shape of the surfaces that produced it.

A high-end commercial 3-D scanner costs tens of thousands of dollars and has a depth resolution of about 50 to 100 micrometers. The Kinect’s resolution is only about 500 micrometers, but it costs roughly $100. And 500 micrometers appears to be good enough to shed some light on the question of the mysterious holes in the jaw of the T. rex skull.

Cretaceous conundrum

Discovered in 1990, the Field Museum’s T. rex skeleton, known as Sue, is the largest and most complete yet found. For years, it was widely assumed that the holes in the jaw were teeth marks, probably from an attack by another tyrannosaur. Ridges of growth around the edges of the holes show that Sue survived whatever caused them.

But the spacing between the holes is irregular, which is inconsistent with bite patterns. In 2009, a group of paleontologists from the University of Wisconsin suggested that the holes could have been caused by a protozoal infection, contracted from eating infected prey, that penetrated Sue’s jaw from the inside out.

The 3-D scan produced by the MIT researchers and their collaborators sheds doubt on both these hypotheses. It shows that the angles at which the holes bore through the jaw are inconsistent enough that they almost certainly weren’t caused by a single bite. But it also shows that the holes taper from the outside in, which undermines the hypothesis of a mouth infection.

New generation of computers

As embedded intelligence is finding its way into ever more areas of our lives, fields ranging from autonomous driving to personalized medicine are generating huge amounts of data. But just as the flood of data is reaching massive proportions, the ability of computer chips to process it into useful information is stalling.

Now, researchers at Stanford University and MIT have built a new chip to overcome this hurdle. The results are published today in the journal Nature, by lead author Max Shulaker, an assistant professor of electrical engineering and computer science at MIT. Shulaker began the work as a PhD student alongside H.-S. Philip Wong and his advisor Subhasish Mitra, professors of electrical engineering and computer science at Stanford. The team also included professors Roger Howe and Krishna Saraswat, also from Stanford.

Computers today comprise different chips cobbled together. There is a chip for computing and a separate chip for data storage, and the connections between the two are limited. As applications analyze increasingly massive volumes of data, the limited rate at which data can be moved between different chips is creating a critical communication “bottleneck.” And with limited real estate on the chip, there is not enough room to place them side-by-side, even as they have been miniaturized (a phenomenon known as Moore’s Law).

To make matters worse, the underlying devices, transistors made from silicon, are no longer improving at the historic rate that they have for decades.

The new prototype chip is a radical change from today’s chips. It uses multiple nanotechnologies, together with a new computer architecture, to reverse both of these trends.

Instead of relying on silicon-based devices, the chip uses carbon nanotubes, which are sheets of 2-D graphene formed into nanocylinders, and resistive random-access memory (RRAM) cells, a type of nonvolatile memory that operates by changing the resistance of a solid dielectric material. The researchers integrated over 1 million RRAM cells and 2 million carbon nanotube field-effect transistors, making the most complex nanoelectronic system ever made with emerging nanotechnologies.

The RRAM and carbon nanotubes are built vertically over one another, making a new, dense 3-D computer architecture with interleaving layers of logic and memory. By inserting ultradense wires between these layers, this 3-D architecture promises to address the communication bottleneck.

Photon photon interactions at room temperature

Ordinarily, light particles — photons — don’t interact. If two photons collide in a vacuum, they simply pass through each other.

An efficient way to make photons interact could open new prospects for both classical optics and quantum computing, an experimental technology that promises large speedups on some types of calculations.

In recent years, physicists have enabled photon-photon interactions using atoms of rare elements cooled to very low temperatures.

But in the latest issue of Physical Review Letters, MIT researchers describe a new technique for enabling photon-photon interactions at room temperature, using a silicon crystal with distinctive patterns etched into it. In physics jargon, the crystal introduces “nonlinearities” into the transmission of an optical signal.

“All of these approaches that had atoms or atom-like particles require low temperatures and work over a narrow frequency band,” says Dirk Englund, an associate professor of electrical engineering and computer science at MIT and senior author on the new paper. “It’s been a holy grail to come up with methods to realize single-photon-level nonlinearities at room temperature under ambient conditions.”

Joining Englund on the paper are Hyeongrak Choi, a graduate student in electrical engineering and computer science, and Mikkel Heuck, who was a postdoc in Englund’s lab when the work was done and is now at the Technical University of Denmark.

Photonic independence

Quantum computers harness a strange physical property called “superposition,” in which a quantum particle can be said to inhabit two contradictory states at the same time. The spin, or magnetic orientation, of an electron, for instance, could be both up and down at the same time; the polarization of a photon could be both vertical and horizontal.

If a string of quantum bits — or qubits, the quantum analog of the bits in a classical computer — is in superposition, it can, in some sense, canvass multiple solutions to the same problem simultaneously, which is why quantum computers promise speedups.

Most experimental qubits use ions trapped in oscillating magnetic fields, superconducting circuits, or — like Englund’s own research — defects in the crystal structure of diamonds. With all these technologies, however, superpositions are difficult to maintain.