Alloy is a language for describing structures and a tool for exploring them. It has been used in a wide range of applications from finding holes in security mechanisms to designing telephone switching networks. Hundreds of projects have used Alloy for design analysis, for verification, for simulation, and as a backend for many other kinds of analysis and synthesis tools, and Alloy is currently being taught in courses worldwide.
Automatic speech recognition (ASR) has been a grand challenge machine learning problem for decades. Our ongoing research in this area examines the use of deep learning models for distant and noisy recording conditions, multilingual, and low-resource scenarios.
We study the fundamentals of Bayesian optimization and develop efficient Bayesian optimization methods for global optimization of expensive black-box functions originated from a range of different applications.
Data often has geometric structure which can enable better inference; this project aims to scale up geometry-aware techniques for use in machine learning settings with lots of data, so that this structure may be utilized in practice.
The MOOC Learner Project provides learning scientists, instructional designers and online education specialists with open source software that enables them to efficiently extract teaching and learning insights from the data collected when students learn on the edX or open edX platform.
The Robot Compiler allows non-engineering users to rapidly fabricate customized robots, facilitating the proliferation of robots in everyday life. It thereby marks an important step towards the realization of personal robots that have captured imaginations for decades.
Our research seeks to discover best practices for using avatars to enhance performance, engagement, and STEM identity development for diverse public middle and high school computer science students. As sites of our research we run workshops in which students learn computer science in fun, relevant ways, and develop self-images as computer scientists.
All humans process vast quantities of unannotated speech and manage to learn phonetic inventories, word boundaries, etc., and can use these abilities to acquire new word. Why can't ASR technology have similar capabilities? Our goal in this research project is to build speech technology using unannotated speech corpora.
Last week MIT’s Institute for Foundations of Data Science (MIFODS) held an interdisciplinary workshop aimed at tackling the underlying theory behind deep learning. Led by MIT professor Aleksander Madry, the event focused on a number of research discussions at the intersection of math, statistics, and theoretical computer science.
Google AI’s Jeff Dean has a seemingly straightforward objective: he wants to use a collection of trainable mathematical units organized in layers to solve complicated tasks that will ultimately benefit many parts of society.
The Imagination, Computation, and Expression Laboratory at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) has released a new video game called Grayscale, which is designed to sensitize players to problems of sexism, sexual harassment, and sexual assault in the workplace.
This week it was announced that MIT professors and CSAIL principal investigators Shafi Goldwasser, Silvio Micali, Ronald Rivest, and former MIT professor Adi Shamir won this year’s BBVA Foundation Frontiers of Knowledge Awards in the Information and Communication Technologies category for their work in cryptography.
Neural networks, which learn to perform computational tasks by analyzing huge sets of training data, have been responsible for the most impressive recent advances in artificial intelligence, including speech-recognition and automatic-translation systems.
Artificial intelligence (AI) in the form of “neural networks” are increasingly used in technologies like self-driving cars to be able to see and recognize objects. Such systems could even help with tasks like identifying explosives in airport security lines.
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.
In recent years, a host of Hollywood blockbusters — including “The Fast and the Furious 7,” “Jurassic World,” and “The Wolf of Wall Street” — have included aerial tracking shots provided by drone helicopters outfitted with cameras. Those shots required separate operators for the drones and the cameras, and careful planning to avoid collisions. But a team of researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and ETH Zurich hope to make drone cinematography more accessible, simple, and reliable.