Predicted Variables in Programming
AssigneeGoogle Inc.
InventorsJay Yagnik, Aleksandr Darin, Thierry Coppey, Thomas Deselaers, Victor Carbune
Life of this patent
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Abstract
The present disclosure is directed to a new framework the enables the combination of symbolic programming with machine learning, where the programmer maintains control of the overall architecture of the functional mapping and the ability to inject domain knowledge while allowing their program to evolve by learning from examples. In some instances, the framework provided herein can be referred to as “predictive programming.”
Claims33
A computer-implemented method, the method comprising:
- obtaining, by one or more computing devices, a computer program that comprises a set of computer-executable instructions, wherein the computer program defines a variable that serves as a placeholder for storing data;
- providing, by the one or more computing devices, observation data to a machine learning system;
- receiving, by the one or more computing devices, a predicted value for the variable produced by the machine learning system based at least in part on the observation data;
- setting, by the one or more computing devices, the variable equal to the predicted value;
- and
- after setting, by the one or more computing devices, the variable equal to the predicted value, executing, by the one or more computing devices, the computer program, wherein executing the computer program comprises implementing at least one instruction of the set of computer-executable instructions that controls an operation of the one or more computing devices based at least in part on the variable.
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One or more non-transitory computer-readable media that collectively store instructions that, when executed by one or more processors, cause the one or more processors to:
- communicate using an application programming interface that enables a set of client code to interface with a machine learning system to receive a predicted value for a predicted variable defined within the set of client code;
- wherein the application programming interface further enables the set of client code to pass observation data to the machine learning system, wherein the machine learning system infers the predicted value for the predicted variable based at least in part on the observation data;
- wherein the application programming interface further enables the set of client code to pass feedback data to the machine learning system, wherein the machine learning system uses the feedback data to update a learned inference model that predicts the predicted value for the predicted variable.
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Dependencies are inferred from the claim text.