After years of trailing other tech giants in AI, Apple has a new ambition: to become the industry’s leading purveyor of products powered by machine learning.
What’s new: In an interview with Ars Technica, the company’s AI chief argues that its pro-privacy, on-device approach is the best way to build such applications.
Think different: John Giannandrea, the former head of Google’s AI and search who joined Apple in 2018, outlined the iPhone maker’s effort to infuse the technology into a wide range of products and services.
- Apple is putting a marketing push behind augmented reality apps and upgrades to its personal digital assistant Siri. It also touts AI features such as managing its devices’ energy consumption based on user habits and fusing successive photos into a single high-quality image.
- Like Google, Huawei, Qualcomm, and Samsung, Apple designed specialized chips to run AI software on smartphones, tablets, and watches. Its laptops are expected to include the chip later this year.
- Rather than executing tasks in the cloud, a chip subsystem called the Neural Engine processes most machine learning tasks on Apple devices. Processing data on the device helps preserve user privacy and reduces latency, so the software runs closer to real time, according to Giannandrea.
- Despite the company’s pro-privacy stance, it does collect and label some anonymized data, Giannandrea said. It also asks users to donate data with prompts like, “Would you like to make Siri better?”
Buying in: Apple lists dozens of AI job openings, but it has acquired much of its AI technology by buying other companies. It purchased at least 20 machine learning startups — more than any of its rivals — since buying Siri in 2010, according to venture tracker CB Insights.
Why it matters: Apple’s privacy-centric, edge-based approach stands out from much of the industry’s reliance on aggressive data collection and processing in the cloud. The difference could help counteract the longstanding impression that it’s behind other tech giants in AI.
We’re thinking: AI’s voracious appetite for data boosts the accuracy of supervised learning systems, but it poses risks to user privacy. Apple’s effort to avoid collecting and exposing user data is refreshing — and raises the stakes for small data techniques that enable systems to learn effectively with fewer examples.