Gene Munster for Loup Ventures:
In June Apple announced Core ML, a platform that allows app developers to easily integrate machine learning (ML) into an app. Of the estimated 2.4m apps available on the App Store, we believe less than 1% leverage ML today – but not for long. We believe Core ML will be a driving force in bringing machine learning to the masses in the form of more useful and insightful apps that run faster and respect user privacy.
While not a complete list, Apple has since used AI in the following areas:
- Facial recognition in photos
- Next word prediction on the iOS keyboard
- Smart responses on the Apple Watch
- Handwriting interpretation on the Apple Watch
- Chinese handwriting recognition
- Drawing based on pencil pressure on the iPad
- Extending iPhone battery life by modifying when data is refreshed (hard to imagine that our iPhone batteries would be even worse if not for AI)
On Machine Learning differences between Apple and Android:
- Speed. ML on Apple is processed locally which speeds up the app. Typically, Android apps process ML in the cloud. Apple can process ML locally because app developers can easily test the hardware running the app (iOS devices). In an Android world, hardware fragmentation makes it harder for app developers to run ML locally.
- Availability. Core ML powered apps are always available, even without network connectivity. Android ML powered apps can require network connectivity, which limits their usability.
- Privacy. Apple’s privacy values are woven into Core ML; terms and conditions do not allow Apple to see any user data captured by an app. For example, if you take a picture using an app that is powered by Core ML’s vision, Apple won’t see the photo. If a message is read using an app powered by Core ML’s natural language processor, the contents won’t be sent to Apple. This differs from Android apps, which typically share their data with Google as part of their terms and conditions.
Excellent overview of CoreML by Gene. As I said in my post on Apple’s Machine Learning Journal, Apple is investing heavily in Machine Learning. While their their stock as the privacy tech company may slow them at times, they easily make up for it in customer satisfaction and end-run adoption by developers. Apple is perfectly fine being the tortoise (as opposed to the hare) when it comes to solving problems the right way.
Machine Learning and AR are going to be transformative for technology and how it affects our daily lives.