Apple Inc.

Sophie Lebrecht

Mohammad Rastegari

Hessam Bagherinezhad

Anish Jnyaneshwar Prabhu

Ali Farhadi

Maxwell Christian Horton

Carlo Eduardo Cabanero Del Mundo

Matthew S Weaver

Alexander James Oscar Craver Kirchhoff

Daniel Carl Tormoen

Robert Stephen Karl, Jr

Garrett L Weinberg

Jessica J Peck

David S Bakin

Saman Naderiparizi

Sayyed Karen Khatamifard

Madhu Chinthakunta

Ping Wen Ong

Andrew Luke Chronister

Stephen G Garcia

James C Gabriel

Jonathan Gelsey

Andrew Z Luo

Systems and processes for operating an intelligent automated assistant are provided. In one example process, a spoken user input, including at least a spoken message and an identity of an intended recipient, is received. After storing the spoken message and the recipient identity, if a user becomes proximate to a first electronic device and the identity of the user is determined to match the recipient identity, the spoken message is provided to the user at the first electronic device.

Intelligent message delivery

In one embodiment, a method includes providing, to a client system of a user, a user interface for display. The user interface may include a first set of options for selecting an artificial intelligence (AI) task for integrating into a user application, a second set of options for selecting one or more devices on which the user wants to deploy the selected AI task, and a third set of options for selecting one or more performance constraints specific to the selected devices. User specifications may be received based on user selections in the first, second, and third sets of options. A custom AI model may be generated based on the user specifications and sent to the client system of the user for integrating into the user application. The custom AI model once integrated may enable the user application to perform the selected AI task on the selected devices.

Model selection interface

Systems and processes for training and compressing a convolutional neural network model include the use of quantization and layer fusion. Quantized training data is passed through a convolutional layer of a neural network model to generate convolutional results during a first iteration of training the neural network model. The convolutional results are passed through a batch normalization layer of the neural network model to update normalization parameters of the batch normalization layer. The convolutional layer is fused with the batch normalization layer to generate a first fused layer and the fused parameters of the fused layer are quantized. The quantized training data is passed through the fused layer using the quantized fused parameters to generate output data, which may be quantized for a subsequent layer in the training iteration.

Compressed convolutional neural network models

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Seattle, WA, United States of America

Sophie Lebrecht