Probably the biggest issue in Apple’s adoption of machine learning
is how the company can succeed while sticking to its principles on
user privacy. The company encrypts user information so that no
one, not even Apple’s lawyers, can read it (nor can the FBI, even
with a warrant). And it boasts about not collecting user
information for advertising purposes.
While admirable from a user perspective, Apple’s rigor on this
issue has not been helpful in luring top AI talent to the company.
“Machine learning experts, all they want is data,” says a former
Apple employee now working for an AI-centric company. “But by its
privacy stance, Apple basically puts one hand behind your back.
You can argue whether it’s the right thing to do or not, but it’s
given Apple a reputation for not being real hardcore AI folks.”
This view is hotly contested by Apple’s executives, who say that
it’s possible to get all the data you need for robust machine
learning without keeping profiles of users in the cloud or even
storing instances of their behavior to train neural nets. “There
has been a false narrative, a false trade-off out there,” says
Federighi. “It’s great that we would be known as uniquely
respecting user’s privacy. But for the sake of users everywhere,
we’d like to show the way for the rest of the industry to get on
This is the crux of the whole piece, to my mind. The AI community is largely focused on privacy-invasive data collection and doing the computation in the cloud. Apple’s approach protects privacy by keeping the data (and performing the computation) on the device.
The other interesting angle in the piece is about most researchers wanting to publish their work, whereas Apple is attracting those who are more interested in the products themselves. But Apple is allowing their researchers on differential privacy to publish their work.