The challenge that motivates the ANA group is to foster a healthy future for the Internet. The interplay of private sector investment, public sector regulation and public interest advocacy, as well as the global diversity in drivers and aspirations, makes for an uncertain future.
We focus on finding novel approaches to improve the performance of modern computer systems without unduly increasing the complexity faced by application developers, compiler writers, or computer architects.
Led by Web inventor and Director, Tim Berners-Lee and CEO Jeff Jaffe, the W3C focus is on leading the World Wide Web to its full potential by developing standards, protocols and guidelines that ensure the long-term growth of the Web
Using AI methods, we are developing an attack tree generator that automatically enumerates cyberattack vectors for industrial control systems in critical infrastructure (electric grids, water networks and transportation systems). The generator can quickly assess cyber risk for a system at scale.
BlueDBM is an architecture of computer clusters consisting of fast distributed flash storage and in-storage accelerators, which often outperforms larger and more expensive clusters in applications such as graph analytics.
We aim to study the impact of computer-supported roleplaying in changing social perspectives of digital media users. Such media could take the form of videogames, VR systems, training software, and other types of interactive narrative technology.
Mixed-methods qualitative (interviews and coding) and computational (AI) approach to understanding relationships between social identities, cultural values, and virtual identity technologies (e.g., online profiles and avatars).
We aim to better understand the features of network protocols that facilitate denial of service attacks, in order to design more robust protocols and architectures in the future and evaluate existing designs more accurately.
Déjà Vu is a new platform for end-user development of apps with rich functionality. It features a novel theory of modularity for binding concepts; an extensive library of reusable concepts; and a WYSIWYG tool for specifying bindings and customizing visual layout
Our research aims to scale hard-to-parallelize applications through new programming models and multicore architectures. Our goal is to enable
programmers to write efficient and scalable parallel programs as easily as they
write sequential programs today.
Communicating through computers has become an extension of our daily reality. But as speaking via screens has become commonplace, our exchanges are losing inflection, body language, and empathy. Danielle Olson ’14, a first-year PhD student at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), believes we can make digital information-sharing more natural and interpersonal, by creating immersive media to better understand each other’s feelings and backgrounds.
Most robots are programmed using one of two methods: learning from demonstration, in which they watch a task being done and then replicate it, or via motion-planning techniques such as optimization or sampling, which require a programmer to explicitly specify a task’s goals and constraints.
Hyper-connectivity has changed the way we communicate, wait, and productively use our time. Even in a world of 5G wireless and “instant” messaging, there are countless moments throughout the day when we’re waiting for messages, texts, and Snapchats to refresh. But our frustrations with waiting a few extra seconds for our emails to push through doesn’t mean we have to simply stand by.
The butt of jokes as little as 10 years ago, automatic speech recognition is now on the verge of becoming people’s chief means of interacting with their principal computing devices. In anticipation of the age of voice-controlled electronics, MIT researchers have built a low-power chip specialized for automatic speech recognition. Whereas a cellphone running speech-recognition software might require about 1 watt of power, the new chip requires between 0.2 and 10 milliwatts, depending on the number of words it has to recognize.
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.
For people struggling with obesity, logging calorie counts and other nutritional information at every meal is a proven way to lose weight. The technique does require consistency and accuracy, however, and when it fails, it’s usually because people don't have the time to find and record all the information they need.A few years ago, a team of nutritionists from Tufts University who had been experimenting with mobile-phone apps for recording caloric intake approached members of the Spoken Language Systems Group at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), with the idea of a spoken-language application that would make meal logging even easier.
Anonymity networks, which sit on top of the public Internet, are designed to conceal people’s Web-browsing habits from prying eyes. The most popular of these, Tor, has been around for more than a decade and is used by millions of people every day.
Every language has its own collection of phonemes, or the basic phonetic units from which spoken words are composed. Depending on how you count, English has somewhere between 35 and 45. Knowing a language’s phonemes can make it much easier for automated systems to learn to interpret speech.In the 2015 volume of Transactions of the Association for Computational Linguistics, CSAIL researchers describe a new machine-learning system that, like several systems before it, can learn to distinguish spoken words. But unlike its predecessors, it can also learn to distinguish lower-level phonetic units, such as syllables and phonemes.
In a computer operating system, the file system is the part that writes data to disk and tracks where the data is stored. If the computer crashes while it’s writing data, the file system’s records can become corrupt. Hours of work could be lost, or programs could stop working properly.At a symposium this fall, MIT researchers will present the first file system that is mathematically guaranteed not to lose track of data during crashes. Although the file system is slow by today’s standards, the techniques the researchers used to verify its performance can be extended to more sophisticated designs. Ultimately, formal verification could make it much easier to develop reliable, efficient file systems.
CSAIL’s Spoken Language Systems Group has unveiled a new technique for automatically tracking speakers in audio recordings. The new technique tackles the task of speaker diarization, or computationally determining how many speakers are present in a recording.