X Development LLC

Google Inc.

Bodymedia, Inc.

Nisarg Vyas

David Andre

Rishabh Singh

Rebecca Radkoff

Thomas Peter Hunt

Jayendra Parmar

Eric Teller

Artem Goncharuk

John Michael Stivoric

Jonathan Farringdon

Christopher D Pacione

Scott A Safier

Suresh Vishnubhatla

Raymond Pelletier

Ryan Butterfoss

Salil Pradhan

Falak Shah

Donna Wolf

Dhara Kotecha

This specification is generally directed to techniques for creating reduced-dimensionality embeddings (e.g., embedding layers of neural networks) with dimensions that are interpretable by and/or are meaningful to humans. In various implementations, a datum may sampled from a document. A dimensionality reduction process may be performed based on the sampled datum to generate a semantically-interpretable embedding having a number of individually-interpretable dimensions. The dimensionality reduction process may include: analyzing the sampled datum according to a number of distinct semantic queries to determine respective numeric solutions. The number of distinct semantic queries may correspond to the number of individually-interpretable dimensions. Each numeric solution may offer an inconclusive clue about the sampled datum. The dimensionality reduction process may also include populating the dimensions of the semantically-interpretable embedding with respective numeric solutions.

Interpretable embeddings

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

Training and/or utilization of a neural decompiler that can be used to generate, from a lower-level compiled representation, a target source code snippet in a target programming language. In some implementations, the lower-level compiled representation is generated by compiling a base source code snippet that is in a base programming language, thereby enabling translation of the base programming language (e.g., C++) to a target programming language (e.g., Python). In some of those implementations, output(s) from the neural decompiler indicate canonical representation(s) of variables. Technique(s) can be used to match those canonical representation(s) to variable(s) of the base source code snippet. In some implementations, multiple candidate target source code snippets are generated using the neural decompiler, and a subset (e.g., one) is selected based on evaluation(s).

Training and/or using neural network model to generate target source code from lower-level representation

Systems and methods for non-invasively determining parameters related to blood glucose are disclosed. Embodiments are disclosed wherein a wearable sensor device comprises non-invasive sensors generating various sensed data which is then utilized to determine a glucose-related parameter.

Apparatus for providing derived glucose information utilizing non-invasive physiological sensors

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Bopal Ahmedabad Gujarat, India

Nisarg Vyas