Openai Opco, LLC

Mark Chen

Ilya Sutskever

Aditya Ramesh

Prafulla Dhariwal

Alexander Nichol

Henrique Ponde De Oliveira Pinto

Jaroslaw Tworek

Hee Woo Jun

Wojciech Zaremba

Casey Chu

Disclosed herein are methods, systems, and computer-readable media for generating an image corresponding to a text input. In an embodiment, operations may include accessing a text description and inputting the text description into a text encoder. The operations may include receiving, from the text encoder, a text embedding, and inputting at least one of the text description or the text embedding into a first sub-model configured to generate, based on at least one of the text description or the text embedding, a corresponding image embedding. The operations may include inputting at least one of the text description or the corresponding image embedding, generated by the first sub-model, into a second sub-model configured to generate, based on at least one of the text description or the corresponding image embedding, an output image. The operations may include making the output image, generated by the first second sub-model, accessible to a device.

Systems and methods for hierarchical text-conditional image generation

Disclosed herein are methods, systems, and computer-readable media for generating computer code based on natural language input. In an embodiment, a method may comprise one or more of: receiving a docstring representing natural language text specifying a digital programming result; generating, using a trained machine learning model, and based on the docstring, a computer code sample configured to produce respective candidate results; causing the computer code sample to be executed; identifying, based on the executing, a computer code sample configured to produce a particular candidate result associated with the digital programming result; performing at least one of outputting, via a user interface, the identified computer code sample, compiling the identified computer code sample, transmitting the identified computer code sample to a recipient device, storing the identified computer code sample, and/or re-executing the identified computer code sample.

Systems and methods for generating code using language models trained on computer code

Disclosed herein are methods, systems, and computer-readable media for generating natural language based on computer code input. In an embodiment, a method may comprise one or more of: accessing a docstring generation model configured to generate docstrings from computer code; receiving one or more computer code samples; generating, using the docstring generation model and based on the received one or more computer code samples, one or more candidate docstrings representing natural language text, each of the one or more candidate docstrings being associated with at least a portion of the one or more computer code samples; identifying at least one of the one or more candidate docstrings that provides an intent of the at least a portion of the one or more computer code samples; and/or outputting, via a user interface, the at least one identified docstring with the at least a portion of the one or more computer code samples.

Systems and methods for generating natural language using language models trained on computer code

Growing community of inventors

Cupertino, CA, United States of America

Mark Chen