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11 条记录 (耗时 0.023 秒)

    SYSTEMS AND METHODS FOR DEEP REINFORCEMENT LEARNING USING A BRAIN-ARTIFICIAL INTELLIGENCE INTERFACE
    5.
    发明申请
    SYSTEMS AND METHODS FOR DEEP REINFORCEMENT LEARNING USING A BRAIN-ARTIFICIAL INTELLIGENCE INTERFACE 审中-公开

    公开(公告)号：US20190101985A1

    公开(公告)日：2019-04-04

    申请号：US16149785

    申请日：2018-10-02

    申请人： THE TRUSTEES OF COLUMBIA UNIVERSITY IN THE CITY OF NEW YORK

    发明人： Paul Sajda ,  Sameer Saproo ,  Victor Shih ,  Sonakshi Bose Roy ,  David Jangraw

    IPC分类号： G06F3/01 ,  G06N5/04 ,  G06N20/00

    摘要： The present disclosure relates to systems and methods for providing a hybrid brain-computer-interface (hBCI) that can detect an individual's reinforcement signals (e.g., level of interest, arousal, emotional reactivity, cognitive fatigue, cognitive state, or the like) in and/or response to objects, events, and/or actions in an environment by generating reinforcement signals for improving an AI agent controlling the environment, such as an autonomous vehicle. Although the disclosed subject matter is discussed within the context of an autonomous vehicle virtual reality game in the exemplary embodiments of the present disclosure, the disclosed system can be applicable to any other environment in which the human user's sensory input is to be used to influence actions within the environment. Furthermore, the systems and methods disclosed can use neural, physiological, or behavioral signatures to inform deep reinforcement learning based AI systems to enhance user comfort and trust in automation.

    RAPID IMAGE ANNOTATION VIA BRAIN STATE DECODING AND VISUAL PATTERN MINING
    8.
    发明申请
    RAPID IMAGE ANNOTATION VIA BRAIN STATE DECODING AND VISUAL PATTERN MINING 审中-公开
    通过大脑状态解码和视觉图形采矿快速图像估计

    公开(公告)号：US20140108302A1

    公开(公告)日：2014-04-17

    申请号：US14060398

    申请日：2013-10-22

    申请人： The Trustees of Columbia University In the City of New York

    发明人： Shih-Fu Chang ,  Jun Wang ,  Paul Sajda ,  Eric Pohlmeyer ,  Barbara Hanna ,  David Jangraw

    IPC分类号： G06N5/02

    CPC分类号： G06N5/02 ,  G06F17/30749 ,  G06F17/30817 ,  G06N5/04

    摘要： Human visual perception is able to recognize a wide range of targets but has limited throughput. Machine vision can process images at a high speed but suffers from inadequate recognition accuracy of general target classes. Systems and methods are provided that combine the strengths of both systems and improve upon existing multimedia processing systems and methods to provide enhanced multimedia labeling, categorization, searching, and navigation.

    摘要翻译： 人的视觉感知能够识别广泛的目标，但吞吐量有限。 机器视觉可以高速处理图像，但是遇到一般目标类别的识别准确性不足。 提供了系统和方法，其结合了两个系统的优点，并改进了现有的多媒体处理系统和方法，以提供增强的多媒体标签，分类，搜索和导航。