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Using ML to build smarter iOS applications

As we know from press reports, Apple uses ML for fraud detection, and to mine useful data from beta testing reports; however, these are not examples visible on our mobile devices. Your iPhone itself has a handful of ML models built into its operating system, and some native apps helping to perform a wide range of tasks. Some use cases are well known and prominent while others are inconspicuous. The most obvious examples are Siri speech recognition, natural language understanding, and voice generation. Camera app uses face detection for focusing and Photos app uses face recognition to group photos with the same person into one album. Presenting the new iOS 10 in June 2016, Craig Federighi mentioned its predictive keyboard, which uses an LSTM algorithm (a type of recurrent neural network) to suggest the next word from the context, and also how Photos uses deep learning to recognize objects and classify scenes. iOS itself uses ML to extend battery life, provide contextual suggestions, match profiles from social networks and mail with the records in Contacts, and to choose between internet connection options. On Apple Watch, ML models are employed to recognize user motion activity types and handwritten input.

Prior to iOS 10, Apple provided some ML APIs like speech or movement recognition, but only as black boxes, without the possibility to tune the models or to reuse them for other purposes. If you wanted to do something slightly different, like detect the type of motion (which is not predefined by Apple), you had to build your own models from scratch. In iOS 10, CNN building blocks were added in the two frameworks at once: as a part of Metal API, and as a sublibrary of an Accelerate framework. Also, the first actual ML algorithm was introduced to iOS SDK: the decision tree learner in the GameplayKit.

ML capabilities continued to expand with the release of iOS 11. At the WWDC 2017, Apple presented the Core ML framework. It includes API for running pre-trained models and is accompanied by tools for converting models trained with some popular ML frameworks to Apple's own format. Still, for now it doesn't provide the possibility of training models on a device, so your models can't be changed or updated in runtime.

Looking in the App Store for the terms artificial intelligence, deep learning, ML , and similar, you'll find a lot of applications, some of them quite successful. Here are several examples:

Google Translate is doing speech recognition and synthesis, OCR, handwriting recognition, and automated translation; some of this is done offline, and some online.
Duolingo validates pronunciation, recommends optimal study materials, and employs Chatbots for language study.
Prisma, Artisto, and others turn photos into paintings using a neural artistic style transfer algorithm. Snapchat and Fabby use image segmentation, object tracking, and other computer vision techniques to enhance selfies. There are also applications for coloring black and white photos automatically.
Snapchat's video selfie filters use ML for real-time face tracking and modification.
Aipoly Vision helps blind people, saying aloud what it sees through the camera.
Several calorie counter apps recognize food through a camera. There are also similar apps to identify dog breeds, trees and trademarks.
Tens of AI personal assistants and Chatbots, with different capabilities from cow disease diagnostics, to matchmaking and stock trading.
Predictive keyboards, spellcheckers, and auto correction, for instance, SwiftKey.
Games that learn from their users and games with evolving characters/units.
There are also news, mail, and other apps that adapt to users' habits and preferences using ML .
Brain-computer interfaces and fitness wearables with the help of ML recognize different user conditions like concentration, sleep phases, and so on. At least some of their supplementary mobile apps do ML .
Medical diagnostic and monitoring through mobile health applications. For example, OneRing monitors Parkinson's disease using the data from a wearable device.

All these applications are built upon the extensive data collection and processing. Even if the application itself is not collecting the data, the model it uses was trained on some usually big dataset. In the following section, we will discuss all things related to data in ML applications.