Machine learning (ML) and artificial intelligence (AI) are integral to nearly every feature on the iPhone and iPad, but unlike some of its competitors, Apple hasn’t been trumpeting these technologies. Ars Technica‘s Samuel Axon spent an hour talking with John Giannandrea, Apple’s Senior Vice President for Machine Learning and AI Strategy, and Bob Borchers, VP of Product Marketing, about the company’s strategy — and the privacy implications of all the new features based on AI and ML.
Both Giannandrea and Borchers joined Apple in the past couple of years; each previously worked at Google. Borchers actually rejoined Apple after time away; he was a senior director of marketing for the iPhone until 2009. And Giannandrea’s defection from Google to Apple in 2018 was widely reported; he had been Google’s head of AI and search.
Giannandrea made the case that Apple is best positioned to “lead the industry” in building machine intelligence-driven features and products:
We made the Pencil, we made the iPad, we made the software for both. It’s just unique opportunities to do a really, really good job. What are we doing a really, really good job at? Letting somebody take notes and be productive with their creative thoughts on digital paper. What I’m interested in is seeing these experiences be used at scale in the world.
He contrasted this with Google. “Google is an amazing company, and there’s some really great technologists working there,” he said. “But fundamentally, their business model is different and they’re not known for shipping consumer experiences that are used by hundreds of millions of people.”
There’s a common narrative that boils machine learning down to the idea that more data means better models, which in turn means better user experiences and products. It’s one of the reasons why onlookers often point to Google, Amazon, or Facebook as likely rulers of the AI roost; those companies operate massive data collection engines, in part because they operate and have total visibility into what has become key digital infrastructure for much of the world. By that measure, Apple is deemed by some unlikely to perform as well, because its business model is different and it has publicly committed to limit its data collection.
When I presented these perspectives to Giannandrea, he didn’t hold back:
Yes, I understand this perception of bigger models in data centers somehow are more accurate, but it’s actually wrong. It’s actually technically wrong. It’s better to run the model close to the data, rather than moving the data around. And whether that’s location data—like what are you doing— [or] exercise data—what’s the accelerometer doing in your phone—it’s just better to be close to the source of the data, and so it’s also privacy preserving.
MacDailyNews Take: There’s a bunch of interesting information about Apple, artificial intelligence, machine learning, Apple silicon, and more in the full article and it’s not massively long, so have at it!