Apples artificial intelligence (AI) chief says that Apple is using machine learning in almost every aspect of how we interact with our devices, but there is much more to come.
John Giannandrea says he moved from Google to Apple because the potential of machine learning (ML) to impact peoples lives is so much greater at the Cupertino company
Giannandrea spoke with ArsTechnicas Samuel Axon, outlining how Apple uses ML now.
Theres a whole bunch of new experiences that are powered by machine learning. And these are things like language translation, or on-device dictation, or our new features around health, like sleep and hand washing, and stuff weve released in the past around heart health and things like this. I think there are increasingly fewer and fewer places in iOS where were not using machine learning.
Its hard to find a part of the experience where youre not doing some predicative [work]. Like, app predictions, or keyboard predictions, or modern smartphone cameras do a ton of machine learning behind the scenes to figure out what they call saliency, which is like, whats the most important part of the picture? Or, if you imagine doing blurring of the background, youre doing portrait mode 
Savvy iPhone owners might also notice that machine learning is behind the Photos apps ability to automatically sort pictures into pre-made galleries, or to accurately give you photos of a friend named Jane when her name is entered into the apps search field 
Most [augmented reality] features are made possible thanks to machine learning 
Borchers also pointed out accessibility features as important examples. They are fundamentally made available and possible because of this, he said. Things like the sound detection capability, which is game-changing for that particular community, is possible because of the investments over time and the capabilities that are built in 
All of these things benefit from the core machine learning features that are built into the core Apple platform. So, its almost like, Find me something where were not using machine learning.
He was, though, surprised at areas where Apple had not been using ML before he joined the company.
When I joined Apple, I was already an iPad user, and I loved the Pencil, Giannandrea (who goes by J.G. to colleagues) told me. So, I would track down the software teams and I would say, Okay, wheres the machine learning team thats working on handwriting? And I couldnt find it.It turned out the team he was looking for didnt exista surprise, he said, given that machine learning is one of the best tools available for the feature today.
I knew that there was so much machine learning that Apple should do that it was surprising that not everything was actually being done.
That has changed, and will continue to change, however.
That has changed dramatically in the last two to three years, he said. I really honestly think theres not a corner of iOS or Apple experiences that will not be transformed by machine learning over the coming few years.
Its long been thought that Apples privacy focus wanting to do everything on the device, and not analyzing huge volumes of personal data means that it cant compete with Google, because it cant benefit from masses of data pulled from millions of users. Giannandrea says this is absolutely not the case.
I understand this perception of bigger models in data centers somehow are more accurate, but its actually wrong. Its actually technically wrong. Its better to run the model close to the data, rather than moving the data around.
In other words, you get better results when an ML model learns from your usage of your device than when it relies on aggregated data from millions of users. Local processing can also be used in situations where it simply wouldnt be realistic to send data to a server, like choosing the exact moment to act on you pressing the Camera app shutter release button for the best frame.
Understandably, Giannandrea wouldnt be drawn on what Apple is working on now, but did give one example of what might be possible when you combine the power of Apple Silicon Macs with machine learning.
Imagine a video editor where you had a search box and you could say, Find me the pizza on the table. And it would just scrub to that frame.
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