Why Apple Believes It’s an AI Leader
In the wake of the Apple silicon announcement, I spoke at length with John Giannandrea, Apple’s Senior Vice President for Machine Learning and AI Strategy, as well as with Bob Borchers, VP of Product Marketing. They described Apple’s AI philosophy, explained how machine learning drives certain features, and argued passionately for Apple’s on-device AI/ML strategy.
[…]
“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.”
Previously:
Update (2020-08-10): Unfortunately, it’s not clear why Giannandrea believes local models would be more accurate.
Apple was saying this all along but no one really believed them because it sounded like excuse making.
One thing Axon appears not to have asked is how Apple grades the success of a machine learning model.
[…]
How does Apple’s machine learning team know when a change to something as crucial to the device as the keyboard is a success?
See also: Hacker News.
Previously:
