Monthly Archives: September 2016

Publicly traded corporation

Cook joined Apple in 1998 and was named its CEO in 2011. As chief executive, he has overseen the introduction of some of Apple’s innovative and popular products, including iPhone 7 and Apple Watch. An advocate for equality and champion of the environment, Cook reminds audiences that Apple’s mission is to change the world for the better, both through its products and its policies.

“Mr. Cook’s brilliance as a business leader, his genuineness as a human being, and his passion for issues that matter to our community make his voice one that I know will resonate deeply with our graduates,” MIT President L. Rafael Reif says. “I am delighted that he will join us for Commencement and eagerly await his charge to the Class of 2017.”

Before becoming CEO, Cook was Apple’s chief operating officer, responsible for the company’s worldwide sales and operations, including management of Apple’s global supply chain, sales activities, and service and support. He also headed the Macintosh division and played a key role in the development of strategic reseller and supplier relationships, ensuring the company’s flexibility in a demanding marketplace.

“Apple stands at the intersection of liberal arts and technology, and we’re proud to have many outstanding MIT graduates on our team,” Cook says. “We believe deeply that technology can be a powerful force for good, and I’m looking forward to speaking to the Class of 2017 as they look ahead to making their own mark on the world.”

Prior to joining Apple, Cook was vice president of corporate materials at Compaq, responsible for procuring and managing product inventory. Before that, he served as chief operating officer of the Reseller Division at Intelligent Electronics.

Cook also spent 12 years with IBM, ending as director of North American fulfillment, where he led manufacturing and distribution for IBM’s personal computer company in North and Latin America.

Cook earned a BS in industrial engineering from Auburn University in 1982, and an MBA from Duke University in 1988.

“Tim Cook is a trailblazer and an inspiration to innovators worldwide,” says Liana Ilutzi, president of MIT’s Class of 2017. “He represents the best of the entrepreneurial and fearless spirit of the MIT community. While faithfully maintaining his integrity and humility, Tim runs one of the most influential companies on the planet. We are beyond excited to have him with us for Commencement!”

“We are looking forward to hearing Tim Cook speak at Commencement,” says Graduate Student Council President Arolyn Conwill. “We believe that his innovative leadership at Apple, along with his commitment to advocacy on sustainability, security, and equality, will inspire graduates to make a far-reaching, positive impact on the world.”

Cook joins a list of notable recent MIT Commencement speakers, including actor and filmmaker Matt Damon (2016); U.S. Chief Technology Officer Megan Smith ’86 SM ’88 (2015); DuPont CEO Ellen Kullman (2014); Dropbox co-founder and CEO Drew Houston ’05 (2013); and Khan Academy founder Sal Khan ’98, MEng ’98 (2012).

“I am delighted with the selection of Tim Cook as the Commencement speaker,” says Chancellor for Academic Advancement Eric Grimson, the longstanding chair of MIT’s Commencement Committee. “Apple is widely viewed as a company that champions innovation, that seeks creative and inventive solutions to problems across a wide range of domains, and that looks to balance technology with issues of social good. These are all themes that are of great importance to our graduates, and I am sure his remarks will be an inspiration to them.”

Lead to fully automated speech recognition

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.

At the Neural Information Processing Systems conference this week, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) are presenting a new approach to training speech-recognition systems that doesn’t depend on transcription. Instead, their system analyzes correspondences between images and spoken descriptions of those images, as captured in a large collection of audio recordings. The system then learns which acoustic features of the recordings correlate with which image characteristics.

“The goal of this work is to try to get the machine to learn language more like the way humans do,” says Jim Glass, a senior research scientist at CSAIL and a co-author on the paper describing the new system. “The current methods that people use to train up speech recognizers are very supervised. You get an utterance, and you’re told what’s said. And you do this for a large body of data.

“Big advances have been made — Siri, Google — but it’s expensive to get those annotations, and people have thus focused on, really, the major languages of the world. There are 7,000 languages, and I think less than 2 percent have ASR [automatic speech recognition] capability, and probably nothing is going to be done to address the others. So if you’re trying to think about how technology can be beneficial for society at large, it’s interesting to think about what we need to do to change the current situation. And the approach we’ve been taking through the years is looking at what we can learn with less supervision.”

Joining Glass on the paper are first author David Harwath, a graduate student in electrical engineering and computer science (EECS) at MIT; and Antonio Torralba, an EECS professor.

Visual semantics

The version of the system reported in the new paper doesn’t correlate recorded speech with written text; instead, it correlates speech with groups of thematically related images. But that correlation could serve as the basis for others.

If, for instance, an utterance is associated with a particular class of images, and the images have text terms associated with them, it should be possible to find a likely transcription of the utterance, all without human intervention. Similarly, a class of images with associated text terms in different languages could provide a way to do automatic translation.

Conversely, text terms associated with similar clusters of images, such as, say, “storm” and “clouds,”  could be inferred to have related meanings. Because the system in some sense learns words’ meanings — the images associated with them — and not just their sounds, it has a wider range of potential applications than a standard speech recognition system.

To test their system, the researchers used a database of 1,000 images, each of which had a recording of a free-form verbal description associated with it. They would feed their system one of the recordings and ask it to retrieve the 10 images that best matched it. That set of 10 images would contain the correct one 31 percent of the time.

Fabricate drones with a wide range

This fall’s new Federal Aviation Administration 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.

To demonstrate, researchers created a range of unusual-looking drones, including a five-rotor “pentacopter” and a rabbit-shaped “bunnycopter” with propellers of different sizes and rotors of different heights.

“This system opens up new possibilities for how drones look and function,” says MIT Professor Wojciech Matusik, who oversaw the project in CSAIL’s Computational Fabrication Group. “It’s no longer a one-size-fits-all approach for people who want to make and use drones for particular purposes.”

The interface lets users design drones with different propellers, rotors, and rods. It also provides guarantees that the drones it fabricates can take off, hover and land — which is no simple task considering the intricate technical trade-offs associated with drone weight, shape, and control.

“For example, adding more rotors generally lets you carry more weight, but you also need to think about how to balance the drone to make sure it doesn’t tip,” says PhD student Tao Du, who was first author on a related paper about the system. “Irregularly-shaped drones are very difficult to stabilize, which means that they require establishing very complex control parameters.”

Du and Matusik co-authored a paper with PhD student Adriana Schulz, postdoc Bo Zhu, and Assistant Professor Bernd Bickel of IST Austria. It will be presented next week at the annual SIGGRAPH Asia conference in Macao, China.

Today’s commercial drones only come in a small range of options, typically with an even number of rotors and upward-facing propellers. But there are many emerging use cases for other kinds of drones. For example, having an odd number of rotors might create a clearer view for a drone’s camera, or allow the drone to carry objects with unusual shapes.

Designing these less conventional drones, however, often requires expertise in multiple disciplines, including control systems, fabrication, and electronics.

“Developing multicopters like these that are actually flyable involves a lot of trial-and-error, tweaking the balance between all the propellers and rotors,” says Du. “It would be more or less impossible for an amateur user, especially one without any computer-science background.”

But the CSAIL group’s new system makes the process much easier. Users design drones by choosing from a database of parts and specifying their needs for things like payload, cost, and battery usage. The system computes the sizes of design elements like rod lengths and motor angles, and looks at metrics such as torque and thrust to determine whether the design will actually work. It also uses an “LQR controller” that takes information about a drone’s characteristics and surroundings to optimize its flight plan.

One of the project’s core challenges stemmed from the fact that a drone’s shape and structure (its “geometry”) is usually strongly tied to how it has been programmed to move (its “control”). To overcome this, researchers used what’s called an “alternating direction method,” which means that they reduced the number of variables by fixing some of them and optimizing the rest. This allowed the team to decouple the variables of geometry and control in a way that optimizes the drone’s performance.

“Once you decouple these variables, you turn a very complicated optimization problem into two easy sub-problems that we already have techniques for solving,” says Du. He envisions future versions of the system that could proactively give design suggestions, like recommending where a rotor should go to accommodate a desired payload.

“This is the first system in which users can interactively design a drone that incorporates both geometry and control,” says Nobuyuki Umetani, a research scientist at Autodesk, Inc., who was not involved in the paper. “This is very exciting work that has the potential to change the way people design.”

The project was supported, in part, by the National Science Foundation, the Air Force Research Laboratory and the European Union’s Horizon 2020 research and innovation program.