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Demos

  • Grover demo
    Detecting fake news  |  MOSAIC

    Online disinformation, or fake news intended to deceive, has emerged as a major societal problem. Currently, fake news articles are written by humans, but recently-introduced AI technology based on neural networks might enable adversaries to generate fake news. Our goal is to reliably detect this “neural fake news” so that its harm can be minimized.

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  • MOSAIC KG demo
    Inferring commonsense knowledge from text  |  MOSAIC

    Explore ATOMIC, an atlas of everyday commonsense reasoning focused on inferential knowledge, organized through 877k textual if-then descriptions. Then see ATOMIC in action via COMeT, a knowledge base construction engine that learns learns to access commonsense knowledge implicitly acquired when reading billions of words of text, producing new nodes and connections in commonsense knowledge graphs.

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  • Semantic Sanity demo

    Semantic Sanity

    PROTOTYPE
    Adaptive ArXiv paper feed  |  Semantic Scholar

    Semantic Sanity Preserver is an adaptive ArXiv feed inspired by ArXiv Sanity. This feed uses an AI model that quickly learns what papers you care about reading and recommends the latest ArXiv research to help you stay up to date on new publications in the field of computer science.

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  • Iconary demo

    Iconary

    PROTOTYPE
    Play a Pictionary-style game with 'AllenAI'  |  PRIOR

    Iconary showcases the world’s first AI system capable of playing a Pictionary-style drawing and guessing game together with a human partner. Team up with the AllenAI artificial intelligence to either draw or guess a set of unique phrases using a limited set of icons to compose your drawing. AllenAI combines advanced computer vision, language understanding, and common sense reasoning to make guesses based on your drawings and to produce its own complex scenes for you to try to guess.

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  • AllenNLP demo
    State-of-the-art open source NLP research library  |  AllenNLP

    AllenNLP is an open source NLP research library that makes it easy for researchers to design and evaluate new deep learning models for nearly any NLP problem, and makes state-of-the-art implementations of several important NLP models and tools readily available for researchers to use and build upon.

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  • AI2-THOR demo
    Photorealistic visual AI platform  |  PRIOR

    AI2-THOR is an open source visual AI platform that provides photorealistic environments complete with actionable objects that AI agents can explore and interact with. AI2-THOR is based on Unity 3D, which enables physical simulation for objects and scenes, and provides a Python API to interact with the Unity 3D game engine.

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  • Aristo demo
    Science question answering with AI  |  Aristo

    Aristo is a multidisciplinary project that aims to develop systems that have a deeper understanding of the world and are capable of demonstrating that understanding through question answering and explanation.

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Additional Demos

  • Open Information Extraction

    The Open IE system runs over sentences and creates extractions that represent relations in text.

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  • GeoS

    GeoS is an end-to-end system that solves high school geometry questions. Its input is question text in natural language and diagram in raster graphics, and its output is the answer to the question.

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  • Euclid

    Euclid combines detailed language understanding with diagram processing to answer a variety of algebra and geometry questions at the high school level.

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  • GPT-2 Explorer

    GPT-2 Explorer is an interactive interface for OpenAI’s GPT-2 language model, allowing you to use GPT-2 to generate new text word-by-word.

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  • CrowdSense

    CrowdSense is an interactive effort to better understand what types of questions people consider to be common sense.

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  • imSitu

    From the PRIOR team: imSitu is a dataset supporting situation recognition, the task of producing a concise summary of the situation an image depicts including the main activity, the participating actors, objects, substances, and locations, and the roles these participants play in the activity.

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