Meta Platforms, Inc.

Facebook, Inc.

Kshitiz Malik

Nanshu Wang

Wenhai Yang

Honglei Liu

David Scott Reiss

Prince Gill

Anuj Kumar

Ruchir Srivastava

Denis Savenkov

Shreyan Bakshi

Elizabeth Kelsey Santoro

Koon Hui Geoffrey Goh

Fadi Botros

Fan Wang

Meryem Pinar Donmez Ediz

Omer Muzaffar

Vikas Seshagiri Rao Bhardwaj

In one embodiment, a method includes receiving a first input by a user from a client system associated with the user, wherein the first input is in a voice modality, analyzing the first input to generate one or more candidate hypotheses, determining one or more modalities for presenting output generated by the one or more computing systems to the user at the client system, and sending instructions to the client system for presenting one or more suggested auto-completions corresponding to one or more of the candidate hypotheses, respectively, wherein each suggested auto-completion comprises the corresponding candidate hypothesis, and wherein the one or more suggested auto-completions are presented in the one or more determined modalities.

Voice-based auto-completions and auto-responses for assistant systems

In one embodiment, a method includes accessing visual signals comprising images portraying textual content in a real-world environment associated with a first user from a client system associated with the first user, recognizing the textual content based on machine-learning models and the visual signals, determining a context associated with the first user with respect to the real-world environment based on the visual signals, executing tasks determined based on the textual content and the determined context for the first user, and sending instructions for presenting execution results of the tasks to the first user to the client system.

Task execution based on real-world text detection for assistant systems

Exemplary embodiments relate to methods, mediums, and systems for moving language models from a server to the client device. Such embodiments may be deployed in an environment where the server is not able to provide modeling services to the clients, such as an end-to-end encrypted (E2EE) environment. Several different techniques are described to address issues of size and complexity reduction, model architecture optimization, model training, battery power reduction, and latency reduction.

Methods, mediums, and systems for providing a model for an end-user device

Methods, mediums, and systems for training a model

Methods, mediums, and systems for representing a model in a memory of device

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Menlo Park, CA, United States of America

Kshitiz Malik