Apple's New AI Training Method Boosts Accuracy Without Compromising User Privacy

Apple has unveiled a new approach to AI training that aims to enhance performance without compromising user privacy. In a move that underscores the company’s ongoing commitment to data protection, Apple says its AI will become smarter by comparing synthetic data to real user samples—without ever storing or accessing personal information.

According to a recent blog post first highlighted by Bloomberg, Apple’s new method will tap into data from users who have opted into its Device Analytics program. But rather than copying messages or emails from a user’s iPhone or Mac, the system uses synthetic datasets—computer-generated examples—to compare with real-world usage. Devices then determine which synthetic inputs most closely resemble the actual data and only send back a signal indicating the best match. No actual content is shared with Apple.

This privacy-first design allows Apple to fine-tune its AI text tools, such as email summaries, using only the most relevant synthetic inputs chosen by the system. The actual user data never leaves the device, preserving individual privacy while still allowing for improved AI performance.

So far, Apple has trained its AI solely on synthetic data, which Bloomberg’s Mark Gurman notes may have led to less accurate or valuable responses. The company has faced delays in rolling out its Apple Intelligence features and even made leadership changes in its Siri team. However, this new training method could mark a turning point.

Apple is rolling out the system in beta versions of iOS, iPadOS 18.5, and macOS 15.5. It also builds on Apple’s use of differential privacy, a technique introduced in 2016 with iOS 10. By adding randomized noise to datasets, the company ensures that individual users can’t be identified—even as the system learns and improves.