Monthly Archives: May 2017

This easy-to-use system can help users with visual impairments

Computer scientists have been working for decades on automatic navigation systems to aid the visually impaired, but it’s been difficult to come up with anything as reliable and easy to use as the white cane, the type of metal-tipped cane that visually impaired people frequently use to identify clear walking paths.

White canes have a few drawbacks, however. One is that the obstacles they come in contact with are sometimes other people. Another is that they can’t identify certain types of objects, such as tables or chairs, or determine whether a chair is already occupied.

Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed a new system that uses a 3-D camera, a belt with separately controllable vibrational motors distributed around it, and an electronically reconfigurable Braille interface to give visually impaired users more information about their environments.

The system could be used in conjunction with or as an alternative to a cane. In a paper they’re presenting this week at the International Conference on Robotics and Automation, the researchers describe the system and a series of usability studies they conducted with visually impaired volunteers.

“We did a couple of different tests with blind users,” says Robert Katzschmann, a graduate student in mechanical engineering at MIT and one of the paper’s two first authors. “Having something that didn’t infringe on their other senses was important. So we didn’t want to have audio; we didn’t want to have something around the head, vibrations on the neck — all of those things, we tried them out, but none of them were accepted. We found that the one area of the body that is the least used for other senses is around your abdomen.”

Katzschmann is joined on the paper by his advisor Daniela Rus, an Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science; his fellow first author Hsueh-Cheng Wang, who was a postdoc at MIT when the work was done and is now an assistant professor of electrical and computer engineering at National Chiao Tung University in Taiwan; Santani Teng, a postdoc in CSAIL; Brandon Araki, a graduate student in mechanical engineering; and Laura Giarré, a professor of electrical engineering at the University of Modena and Reggio Emilia in Italy.

Parsing the world

The researchers’ system consists of a 3-D camera worn in a pouch hung around the neck; a processing unit that runs the team’s proprietary algorithms; the sensor belt, which has five vibrating motors evenly spaced around its forward half; and the reconfigurable Braille interface, which is worn at the user’s side.

The key to the system is an algorithm for quickly identifying surfaces and their orientations from the 3-D-camera data. The researchers experimented with three different types of 3-D cameras, which used three different techniques to gauge depth but all produced relatively low-resolution images — 640 pixels by 480 pixels — with both color and depth measurements for each pixel.

The algorithm first groups the pixels into clusters of three. Because the pixels have associated location data, each cluster determines a plane. If the orientations of the planes defined by five nearby clusters are within 10 degrees of each other, the system concludes that it has found a surface. It doesn’t need to determine the extent of the surface or what type of object it’s the surface of; it simply registers an obstacle at that location and begins to buzz the associated motor if the wearer gets within 2 meters of it.

Chair identification is similar but a little more stringent. The system needs to complete three distinct surface identifications, in the same general area, rather than just one; this ensures that the chair is unoccupied. The surfaces need to be roughly parallel to the ground, and they have to fall within a prescribed range of heights.

Tactile data

The belt motors can vary the frequency, intensity, and duration of their vibrations, as well as the intervals between them, to send different types of tactile signals to the user. For instance, an increase in frequency and intensity generally indicates that the wearer is approaching an obstacle in the direction indicated by that particular motor. But when the system is in chair-finding mode, for example, a double pulse indicates the direction in which a chair with a vacant seat can be found.

The Braille interface consists of two rows of five reconfigurable Braille pads. Symbols displayed on the pads describe the objects in the user’s environment — for instance, a “t” for table or a “c” for chair. The symbol’s position in the row indicates the direction in which it can be found; the column it appears in indicates its distance. A user adept at Braille should find that the signals from the Braille interface and the belt-mounted motors coincide.

In tests, the chair-finding system reduced subjects’ contacts with objects other than the chairs they sought by 80 percent, and the navigation system reduced the number of cane collisions with people loitering around a hallway by 86 percent.

Towards optical quantum computing

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, superconductingcircuits, or — like Englund’s own research — defects in the crystal structure of diamonds. With all these technologies, however, superpositions are difficult to maintain.

Because photons aren’t very susceptible to interactions with the environment, they’re great at maintaining superposition; but for the same reason, they’re difficult to control. And quantum computing depends on the ability to send control signals to the qubits.

That’s where the MIT researchers’ new work comes in. If a single photon enters their device, it will pass through unimpeded. But if two photons — in the right quantum states — try to enter the device, they’ll be reflected back.

The quantum state of one of the photons can thus be thought of as controlling the quantum state of the other. And quantum information theory has established that simple quantum “gates” of this type are all that is necessary to build a universal quantum computer.

Unsympathetic resonance

The researchers’ device consists of a long, narrow, rectangular silicon crystal with regularly spaced holes etched into it. The holes are widest at the ends of the rectangle, and they narrow toward its center. Connecting the two middle holes is an even narrower channel, and at its center, on opposite sides, are two sharp concentric tips. The pattern of holes temporarily traps light in the device, and the concentric tips concentrate the electric field of the trapped light.

The researchers prototyped the device and showed that it both confined light and concentrated the light’s electric field to the degree predicted by their theoretical models. But turning the device into a quantum gate would require another component, a dielectric sandwiched between the tips. (A dielectric is a material that is ordinarily electrically insulating but will become polarized — all its positive and negative charges will align in the same direction — when exposed to an electric field.)

When a light wave passes close to a dielectric, its electric field will slightly displace the electrons of the dielectric’s atoms.  When the electrons spring back, they wobble, like a child’s swing when it’s pushed too hard. This is the nonlinearity that the researchers’ system exploits.

The size and spacing of the holes in the device are tailored to a specific light frequency — the device’s “resonance frequency.” But the nonlinear wobbling of the dielectric’s electrons should shift that frequency.

Ordinarily, that shift is mild enough to be negligible. But because the sharp tips in the researchers’ device concentrate the electric fields of entering photons, they also exaggerate the shift. A single photon could still get through the device. But if two photons attempted to enter it, the shift would be so dramatic that they’d be repulsed.

Practical potential

The device can be configured so that the dramatic shift in resonance frequency occurs only if the photons attempting to enter it have particular quantum properties — specific combinations of polarization or phase, for instance. The quantum state of one photon could thus determine the way in which the other photon is handled, the basic requirement for a quantum gate.

Englund emphasizes that the new research will not yield a working quantum computer in the immediate future. Too often, light entering the prototype is still either scattered or absorbed, and the quantum states of the photons can become slightly distorted. But other applications may be more feasible in the near term. For instance, a version of the device could provide a reliable source of single photons, which would greatly abet a range of research in quantum information science and communications.

“This work is quite remarkable and unique because it shows strong light-matter interaction, localization of light, and relatively long-time storage of photons at such a tiny scale in a semiconductor,” says Mohammad Soltani, a nanophotonics researcher in Raytheon BBN Technologies’ Quantum Information Processing Group. “It can enable things that were questionable before, like nonlinear single-photon gates for quantum information. It works at room temperature, it’s solid-state, and it’s compatible with semiconductor manufacturing. This work is among the most promising to date for practical devices, such as quantum information devices.”

Systems In today’s computers can predict chemical reaction products

When organic chemists identify a useful chemical compound — a new drug, for instance — it’s up to chemical engineers to determine how to mass-produce it.

There could be 100 different sequences of reactions that yield the same end product. But some of them use cheaper reagents and lower temperatures than others, and perhaps most importantly, some are much easier to run continuously, with technicians occasionally topping up reagents in different reaction chambers.

Historically, determining the most efficient and cost-effective way to produce a given molecule has been as much art as science. But MIT researchers are trying to put this process on a more secure empirical footing, with a computer system that’s trained on thousands of examples of experimental reactions and that learns to predict what a reaction’s major products will be.

The researchers’ work appears in the American Chemical Society’s journal Central Science. Like all machine-learning systems, theirs presents its results in terms of probabilities. In tests, the system was able to predict a reaction’s major product 72 percent of the time; 87 percent of the time, it ranked the major product among its three most likely results.

“There’s clearly a lot understood about reactions today,” says Klavs Jensen, the Warren K. Lewis Professor of Chemical Engineering at MIT and one of four senior authors on the paper, “but it’s a highly evolved, acquired skill to look at a molecule and decide how you’re going to synthesize it from starting materials.”

With the new work, Jensen says, “the vision is that you’ll be able to walk up to a system and say, ‘I want to make this molecule.’ The software will tell you the route you should make it from, and the machine will make it.”

With a 72 percent chance of identifying a reaction’s chief product, the system is not yet ready to anchor the type of completely automated chemical synthesis that Jensen envisions. But it could help chemical engineers more quickly converge on the best sequence of reactions — and possibly suggest sequences that they might not otherwise have investigated.

Jensen is joined on the paper by first author Connor Coley, a graduate student in chemical engineering; William Green, the Hoyt C. Hottel Professor of Chemical Engineering, who, with Jensen, co-advises Coley; Regina Barzilay, the Delta Electronics Professor of Electrical Engineering and Computer Science; and Tommi Jaakkola, the Thomas Siebel Professor of Electrical Engineering and Computer Science.

Acting locally

A single organic molecule can consist of dozens and even hundreds of atoms. But a reaction between two such molecules might involve only two or three atoms, which break their existing chemical bonds and form new ones. Thousands of reactions between hundreds of different reagents will often boil down to a single, shared reaction between the same pair of “reaction sites.”

A large organic molecule, however, might have multiple reaction sites, and when it meets another large organic molecule, only one of the several possible reactions between them will actually take place. This is what makes automatic reaction-prediction so tricky.

In the past, chemists have built computer models that characterize reactions in terms of interactions at reaction sites. But they frequently require the enumeration of exceptions, which have to be researched independently and coded by hand. The model might declare, for instance, that if molecule A has reaction site X, and molecule B has reaction site Y, then X and Y will react to form group Z — unless molecule A also has reaction sites P, Q, R, S, T, U, or V.

It’s not uncommon for a single model to require more than a dozen enumerated exceptions. And discovering these exceptions in the scientific literature and adding them to the models is a laborious task, which has limited the models’ utility.

One of the chief goals of the MIT researchers’ new system is to circumvent this arduous process. Coley and his co-authors began with 15,000 empirically observed reactions reported in U.S. patent filings. However, because the machine-learning system had to learn what reactions wouldn’t occur, as well as those that would, examples of successful reactions weren’t enough.

Negative examples

So for every pair of molecules in one of the listed reactions, Coley also generated a battery of additional possible products, based on the molecules’ reaction sites. He then fed descriptions of reactions, together with his artificially expanded lists of possible products, to an artificial intelligence system known as a neural network, which was tasked with ranking the possible products in order of likelihood.

From this training, the network essentially learned a hierarchy of reactions — which interactions at what reaction sites tend to take precedence over which others — without the laborious human annotation.

Other characteristics of a molecule can affect its reactivity. The atoms at a given reaction site may, for instance, have different charge distributions, depending on what other atoms are around them. And the physical shape of a molecule can render a reaction site difficult to access. So the MIT researchers’ model also includes numerical measures of both these features.

According to Richard Robinson, a chemical-technologies researcher at the drug company Novartis, the MIT researchers’ system “offers a different approach to machine learning within the field of targeted synthesis, which in the future could transform the practice of experimental design to targeted molecules.”

“Currently we rely heavily on our own retrosynthetic training, which is aligned with our own personal experiences and augmented with reaction-database search engines,” Robinson says. “This serves us well but often still results in a significant failure rate. Even highly experienced chemists are often surprised. If you were to add up all the cumulative synthesis failures as an industry, this would likely relate to a significant time and cost investment. What if we could improve our success rate?”

The MIT researchers, Robinson says, “have cleverly demonstrated a novel approach to achieve higher predictive reaction performance over conventional approaches. By augmenting the reported literature with negative reaction examples, the data set has more value.”

3-D Chip Has combined computing and data storage in Computer

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.

However, such an architecture is not possible with existing silicon-based technology, according to the paper’s lead author, Max Shulaker, who is a core member of MIT’s Microsystems Technology Laboratories. “Circuits today are 2-D, since building conventional silicon transistors involves extremely high temperatures of over 1,000 degrees Celsius,” says Shulaker. “If you then build a second layer of silicon circuits on top, that high temperature will damage the bottom layer of circuits.”

The key in this work is that carbon nanotube circuits and RRAM memory can be fabricated at much lower temperatures, below 200 C. “This means they can be built up in layers without harming the circuits beneath,” Shulaker says.

This provides several simultaneous benefits for future computing systems. “The devices are better: Logic made from carbon nanotubes can be an order of magnitude more energy-efficient compared to today’s logic made from silicon, and similarly, RRAM can be denser, faster, and more energy-efficient compared to DRAM,” Wong says, referring to a conventional memory known as dynamic random-access memory.

“In addition to improved devices, 3-D integration can address another key consideration in systems: the interconnects within and between chips,” Saraswat adds.

“The new 3-D computer architecture provides dense and fine-grained integration of computating and data storage, drastically overcoming the bottleneck from moving data between chips,” Mitra says. “As a result, the chip is able to store massive amounts of data and perform on-chip processing to transform a data deluge into useful information.”

To demonstrate the potential of the technology, the researchers took advantage of the ability of carbon nanotubes to also act as sensors. On the top layer of the chip they placed over 1 million carbon nanotube-based sensors, which they used to detect and classify ambient gases.

Due to the layering of sensing, data storage, and computing, the chip was able to measure each of the sensors in parallel, and then write directly into its memory, generating huge bandwidth, Shulaker says.

Three-dimensional integration is the most promising approach to continue the technology scaling path set forth by Moore’s laws, allowing an increasing number of devices to be integrated per unit volume, according to Jan Rabaey, a professor of electrical engineering and computer science at the University of California at Berkeley, who was not involved in the research.

“It leads to a fundamentally different perspective on computing architectures, enabling an intimate interweaving of memory and logic,” Rabaey says. “These structures may be particularly suited for alternative learning-based computational paradigms such as brain-inspired systems and deep neural nets, and the approach presented by the authors is definitely a great first step in that direction.”

“One big advantage of our demonstration is that it is compatible with today’s silicon infrastructure, both in terms of fabrication and design,” says Howe.

“The fact that this strategy is both CMOS [complementary metal-oxide-semiconductor] compatible and viable for a variety of applications suggests that it is a significant step in the continued advancement of Moore’s Law,” says Ken Hansen, president and CEO of the Semiconductor Research Corporation, which supported the research. “To sustain the promise of Moore’s Law economics, innovative heterogeneous approaches are required as dimensional scaling is no longer sufficient. This pioneering work embodies that philosophy.”

The team is working to improve the underlying nanotechnologies, while exploring the new 3-D computer architecture. For Shulaker, the next step is working with Massachusetts-based semiconductor company Analog Devices to develop new versions of the system that take advantage of its ability to carry out sensing and data processing on the same chip.

So, for example, the devices could be used to detect signs of disease by sensing particular compounds in a patient’s breath, says Shulaker.

“The technology could not only improve traditional computing, but it also opens up a whole new range of applications that we can target,” he says. “My students are now investigating how we can produce chips that do more than just computing.”

“This demonstration of the 3-D integration of sensors, memory, and logic is an exceptionally innovative development that leverages current CMOS technology with the new capabilities of carbon nanotube field–effect transistors,” says Sam Fuller, CTO emeritus of Analog Devices, who was not involved in the research. “This has the potential to be the platform for many revolutionary applications in the future.”

This work was funded by the Defense Advanced Research Projects Agency, the National Science Foundation, Semiconductor Research Corporation, STARnet SONIC, and member companies of the Stanford SystemX Alliance.