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.
This CoR brings together researchers at CSAIL working across a broad swath of application domains. Within these lie novel and challenging machine learning problems serving science, social science and computer science.
This CoR aims to develop AI technology that synthesizes symbolic reasoning, probabilistic reasoning for dealing with uncertainty in the world, and statistical methods for extracting and exploiting regularities in the world, into an integrated picture of intelligence that is informed by computational insights and by cognitive science.
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.
This community is interested in understanding and affecting the interaction between computing systems and society through engineering, computer science and public policy research, education, and public engagement.
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.
The goal of this project is to model the process of ‘full interpretation’ of object images, namely the ability to identify and localize all semantic features and parts that are recognized by human observers.
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
Espalier (formerly Object Spreadsheets) is a new computational paradigm that combines the usability advantages of spreadsheets with SQL-like expressive power, providing a way to build a wide class of interactive applications more easily than with existing tools.
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.
Almost every object we use is developed with computer-aided design (CAD). While CAD programs are good for creating designs, using them to actually improve existing designs can be difficult and time-consuming.
For all the progress made in self-driving technologies, there still aren’t many places where they can actually drive. Companies like Google only test their fleets in major cities where they’ve spent countless hours meticulously labeling the exact 3-D positions of lanes, curbs, off-ramps, and stop signs.
Every spring, engineering students from MIT and law students from Georgetown University overcome the distance between their institutions and disciplines in a semester-long flurry of virtual classroom meetings and late-night Google hangout sessions, culminating in presentations to policy experts in DC.
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.