X Development LLC

Google Inc.

Rebecca Radkoff

David Andre

Nisarg Vyas

Rishabh Singh

Thomas Peter Hunt

Ryan Butterfoss

John Michael Stivoric

Grace Taixi Brentano

Jayendra Parmar

Lam Thanh Nguyen

Salil Vijaykumar Pradhan

Salil Pradhan

Falak Shah

Disclosed implementations relate to automating semantically-similar computing tasks across multiple contexts. In various implementations, an initial natural language input and a first plurality of actions performed using a first computer application may be used to generate a first task embedding and a first action embedding in action embedding space. An association between the first task embedding and first action embedding may be stored. Later, subsequent natural language input may be used to generate a second task embedding that is then matched to the first task embedding. Based on the stored association, the first action embedding may be identified and processed using a selected domain model to select actions to be performed using a second computer application. The selected domain model may be trained to translate between an action space of the second computer application and the action embedding space.

Automating semantically-related computing tasks across contexts

This specification is generally directed to techniques for robust natural language (NL) based control of computer applications. In many implementations, the NL control is at least selectively interactive in that the user feedback input is solicited, and received, in resolving action(s), resolving action set(s), generating domain specific knowledge, and/or in providing feedback on implemented action set(s). The user feedback input can be utilized in further training of machine learning model(s) utilized in the NL based control of the computer applications.

Robust natural language based control of computer applications

This specification is generally directed to techniques for generating interfacing source code between computing components based on natural language input. In various implementations, a natural language input that requests generation of interfacing source code to logically couple a first computing component with a second computing component may be processed to generate an interface request semantic embedding. The interface request semantic embedding may be processed based on one or more domain models associated with the first and second computing components to generate a pool(s) of candidate code snippets for logically coupling with first and second computing components. A plurality of candidate instances of interfacing source code may be generated between the first and second computing components. Each candidate software interface may include a different permutation of candidate code snippets from the pool(s) of candidate code snippets.

Generating interfacing source code

Implementations are directed to improving network anti-fragility. In some aspects, a method includes receiving parameter data from a network of nodes, the parameter data comprising attributes, policies, and action spaces for each node in the network of nodes; configuring one or more interruptive events on one or more nodes included in the network of nodes; determining a first action of each node in the network of nodes in response to the one or more interruptive events; determining a first performance metric, for each node, that corresponds to the first action, wherein the first performance matric is determined based on at least a first reward value associated with the first action; continuously updating the first action in an iterative process to obtain a final action, wherein a performance metric corresponding to the final action satisfies a performance threshold, and transmitting the final action for each node to the network of nodes.

Anti-fragile network

Growing community of inventors

San Francisco, CA, United States of America

Rebecca Radkoff