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MIT Press CISnet brings together many of the MIT Press?s recent and classic titles in computer and information science in a fully searchable online library. Subscribers have access to a growing collection of MIT Press books on topics including programming, artificial intelligence, machine learning, human computer interaction, databases, digital libraries, networking, and robotics. CISnet is accessible from any computer with an Internet connection and from Web-enabled handhelds including the iPhone.

"The MIT Intelligence Quest will advance the science and engineering of both human and machine intelligence. Launched on February 1, 2018, this effort seeks to discover the foundations of human intelligence and drive the development of technological tools that can positively influence virtually every aspect of society."

START, the world's first Web-based question answering system, has been on-line and continuously operating since December, 1993. It has been developed by Boris Katz and his associates of the InfoLab Group at the MIT Computer Science and Artificial Intelligence Laboratory. Unlike information retrieval systems (e.g., search engines), START aims to supply users with "just the right information," instead of merely providing a list of hits. Currently, the system can answer millions of English questions about places (e.g., cities, countries, lakes, coordinates, weather, maps, demographics, political and economic systems), movies (e.g., titles, actors, directors), people (e.g., birth dates, biographies), dictionary definitions, and much, much more. Below is a list of some of the things START knows about, with example questions. You can type your question above or select from the following examples.

MIT Computer Science and Artificial Intelligence Laboratory

Our basic research question is: How can people and computers be connected so that-collectively-they act more intelligently than any individuals, groups, or computers have ever done before?'

"Scene recognition is one of the hallmark tasks of computer vision, allowing defining a context for object recognition. Here we introduce a new scene-centric database called Places, with 205 scene categories and 2.5 millions of images with a category label. Using convolutional neural network (CNN), we learn deep scene features for scene recognition tasks, and establish new state-of-the-art performances on scene-centric benchmarks. Here we provide the Places Database and the trained CNNs for academic research and education purposes."

"The GLTR demo enables forensic inspection of the visual footprint of a language model on input text to detect whether a text could be real or fake. It is a collaborative effort between Hendrik Strobelt, Sebastian Gehrmann, and Alexander Rush from the MIT-IBM Watson AI lab and Harvard NLP."