Cyrus Shaoul

Milan Singh Minsky

Henry Bowdoin Minsky

Max Carlson

Steven J Kommrusch

A method for training a learning system to identify components of time-based data streams includes processing, by a machine vision component in communication with a learning system, a video file to detect at least one object in the video file. The method includes generating by the machine vision component an output including data relating to the at least one object and the video file. The method includes analyzing, by a learning system, the output. The method includes identifying, by the learning system, an unidentified object in the processed video file.

Methods and systems for training and execution of improved learning systems for identification of components in time-based data streams

A computer vision learning system and corresponding computer-implemented method extract meaning from image content. The computer vision learning system comprises at least one image sensor that transforms light sensed from an environment of the computer vision learning system into image data representing a scene of the environment. The computer vision learning system further comprises a digital computational learning system that includes a network of actor perceiver predictor (APP) nodes and a library of visual methods available to the APP nodes for applying to the image data. The digital computational learning system employs the network in combination with the library to determine a response to a query and outputs the response determined. The query is associated with the scene. The computer vision learning system is capable of answering queries not just about what is happening in the scene, but what would happen based on the scene in view of hypothetical conditions and/or actions.

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Dummerston, VT, United States of America

Cyrus Shaoul