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At the 2024 , we introduced Apple Intelligence, a personal intelligence system integrated deeply into iOS 18, iPadOS 18, and macOS Sequoia. Apple Intelligence is comprised of multiple highly-capable generative models that are specialized for our users’ everyday tasks, and can adapt on the fly for their current activity. The foundation models built into Apple Intelligence have been fine-tuned for u
Stable Diffusion with Core ML on Apple SiliconAuthorsAtila Orhon, Michael Siracusa, Aseem Wadhwa Today, we are excited to release optimizations to Core ML for Stable Diffusion in macOS 13.1 and iOS 16.2, along with code to get started with deploying to Apple Silicon devices. Figure 1: Images generated with the prompts, "a high quality photo of an astronaut riding a (horse/dragon) in space" using S
An increasing number of the machine learning (ML) models we build at Apple each year are either partly or fully adopting the Transformer architecture. This architecture helps enable experiences such as , , , , and many others. This year at WWDC 2022, Apple is making available an open-source reference PyTorch implementation of the Transformer architecture, giving developers worldwide a way to seaml
Apple attended Interspeech 2019, the world's largest conference on the science and technology of spoken language processing. The conference took place in Graz, Austria from September 15th to 19th. See accepted papers below. Apple continues to build cutting-edge technology in the space of machine hearing, speech recognition, natural language processing, machine translation, text-to-speech, and arti
Apple introduced the "Hey Siri" feature with the iPhone 6 (iOS 8). This feature allows users to invoke Siri without having to press the home button. When a user says, "Hey Siri, how is the weather today?" the phone wakes up upon hearing "Hey Siri" and processes the rest of the utterance as a Siri request. The feature's ability to listen continuously for the "Hey Siri" trigger phrase lets users acc
Understanding how people use their devices often helps in improving the user experience. However, accessing the data that provides such insights — for example, what users type on their keyboards and the websites they visit — can compromise user privacy. We develop a system architecture that enables learning at scale by leveraging local differential privacy, combined with existing privacy best prac
Apple started using deep learning for face detection in iOS 10. With the release of the Vision framework, developers can now use this technology and many other computer vision algorithms in their apps. We faced significant challenges in developing the framework so that we could preserve user privacy and run efficiently on-device. This article discusses these challenges and describes the face detec
Hey Siri: An On-device DNN-powered Voice Trigger for Apple’s Personal Assistant The "Hey Siri" feature allows users to invoke Siri hands-free. A very small speech recognizer runs all the time and listens for just those two words. When it detects "Hey Siri", the rest of Siri parses the following speech as a command or query. The "Hey Siri" detector uses a Deep Neural Network (DNN) to convert the ac
Real-Time Recognition of Handwritten Chinese Characters Spanning a Large Inventory of 30,000 Characters Handwriting recognition is more important than ever given the prevalence of mobile phones, tablets, and wearable gear like smartwatches. The large symbol inventory required to support Chinese handwriting recognition on such mobile devices poses unique challenges. This article describes how we me
Deep Learning for Siri’s Voice: On-device Deep Mixture Density Networks for Hybrid Unit Selection Synthesis Siri is a personal assistant that communicates using speech synthesis. Starting in iOS 10 and continuing with new features in iOS 11, we base Siri voices on deep learning. The resulting voices are more natural, smoother, and allow Siri’s personality to shine through. This article presents mo
Most successful examples of neural nets today are trained with supervision. However, to achieve high accuracy, the training sets need to be large, diverse, and accurately annotated, which is costly. An alternative to labelling huge amounts of data is to use synthetic images from a simulator. This is cheap as there is no labeling cost, but the synthetic images may not be realistic enough, resulting
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