Future Wei Technologies, Inc.

Huawei Technologies Co., Limited

Salesforce, Inc.

Jun Zhang

Jian Li

Lifeng Liu

Yingxuan Zhu

Xiaotian Yin

Yongxiang Tao

Nicholas Joseph DelTorto, Iii

Dayao Liang

A computer implemented method for self-learning of a control system. The method includes creating an initial knowledge base. The method learns first principles using the knowledge base. The method creates initial control commands derived from the knowledge base. The method generates constraints for the control commands. The method performs constrained reinforcement learning by executing the control commands with the constraints and observing feedback to improve the control commands. The method enriches the knowledge base based on the feedback.

System and method of efficient, continuous, and safe learning using first principles and constraints

Systems and method are provided for tracking actions from one or more users of an electronic product catalog, assigning a first token to each of the tracked actions that are unrelated to product item information, assigning second tokens to the one or more tracked actions which have catalog information or product identity, assigning third tokens to the one or more tracked actions based on at least one category of a page of the electronic product catalog, and assigning fourth tokens to at least one search queries and/or search refinement of the one or more tracked actions. The server may generate a sequence of tokens in one or more vectors for each of the one or more users, may encode profile information for the one or more users as a matrix of vectors, and may determine at least one product recommendation for the one or more users from the electronic catalog.

Systems and methods of product recommendation and integrated language modelling

A computer-implemented method of answering questions comprises: receiving, by one or more processors, a query; based on the query, generating, by the one or more processors, a matrix; based on the matrix, modifying, by the one or more processors, a dynamic memory; based on the matrix, determining, by the one or more processors, a first response from the dynamic memory; based on the matrix, determining, by the one or more processors, a second response from a database; based on the first response and the second response, determining, by the one or more processors, a third response; and in response to the query, providing, by the one or more processors, the third response.

Dynamic semantic networks for language understanding and question answering

Methods, apparatus, and systems are provided for integrated driver expression recognition and vehicle interior environment classification to detect driver condition for safety. A method includes obtaining an image of a driver of a vehicle and an image of an interior environment of the vehicle. Using a machine learning method, the images are processed to classify a condition of the driver and of the interior environment of the vehicle. The machine learning method includes general convolutional neural network (CNN) and CNN with adaptive filters. The adaptive filters are determined based on influence of filters. The classification results are combined and compared with predetermined thresholds to determine if a decision can be made based on existing information. Additional information is requested by self-motivated learning if a decision cannot be made, and safety is determined based on the combined classification results. A warning is provided to the driver based on the safety determination.

Integrated system for detection of driver condition

Various embodiments include systems and methods structured to provide recognition of an object in an image using a learning module trained using decomposition of the object into components in a number of training images. The training can be based on an overall objectness score of the object, an objectness score of each component of the object, a pose of the object, and a pose of each component of the object for each training image input. Additional systems and methods can be implemented in a variety of applications.

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Cambridge, MA, United States of America

Jun Zhang