Why Artificial Intelligence at Apple

1. How Apple views Machine Learning

‘It’s transformational, but not more so than other advances, like touch screens, or flat panels, or object-oriented programming. In Apple’s view, machine learning isn’t the final frontier.’

 machine learning (ML) mindset seems at odds with the Apple ethos of controlling the user experience. With ML, results emerge from the data and not directly from Apple designers. More on this below.

The company’s machine learning talent is shared throughout the entire company, available to product teams.

• : It may be harder for Apple to attract talent because (1) their concerns about user privacy reduce the pool of data engineers can use to train models and (2) 
•     (Aug 2016) and Siri (2011).

 Apple approaches new technology

1. stuff available externally to get tech off the ground.
2. “As it becomes clear a technology area is critical to our ability to deliver a great product over time, we build our in-house capabilities to deliver the experience we want. To make it great, we want to own and innovate internally.” – Senior Vice President of Software Engineering 

in Speech Technology: When Siri Senior Director Alex  arrived three years ago, Apple was still licensing much of its speech technology for Siri from a third party, a situation due for a change. Now Apple has its own speech technology.

Apple thinks it has some advantage in ML because it has control of the entire product delivery system.

• Apple makes its own chips, so Siri Senior Director Acero was able to work directly with the silicon design team and the engineers who write the firmware for the devices to maximize performance of the neural net.
• The needs of the Siri team influenced even aspects of the iPhone’s design.

• 2. External opinion: Problems with AI at Apple

  How the AI establishment views AI at 

  • is constrained by its lack of a search engine (which can deliver the data that helps to train neural networks) and its inflexible insistence on protecting user information (which potentially denies Apple data it otherwise might use).
  • Apple isn’t a part of the AI community and was seen to be behind the pack with respect to AI. It did not publicise Siri’s transition to neural nets in July 2014.
  • Apple doesn’t publish much 

     

    • Two key problems:

      1. Protecting personal preferences and information that neural nets identify.
      2. Gathering information required to train neural nets to  behaviours WITHOUT collecting the personal information of users.

      Problem 1: Protecting personal preferences and information that neural nets identify. E.g. if a machine learning model infers that you like apples.

      Solution: Keep some of the most sensitive things where the ML is occurring entirely local to the device (e.g. word people type using iPhone QuickType keyboard). That is, these inferences are not sent to the Apple servers.

      • Example from Damien Tremblay’s website: you might be having a conversation and someone mentions a term that is a potential search. Other companies might have to analyze the whole conversation in the cloud to identify those terms, he says, but an Apple device can detect it without having the data leave the user’s possession — because the system is constantly looking for matches on a knowledge base kept on the phone. (It’s part of that 200 megabyte “brain.”)

       2: Gathering information required to train neural nets to recognise WITHOUT collecting the personal information of users.

       

    • Train models on publicly available corpuses of
    • data, tagging it with random identifiers not associated with Apple IDs.
    • ‘Differential privacy’: Basically a method that  information in a way that doesn’t identify individuals at all. Apple implements this by adding mathematical noise to certain pieces of data so that Apple can detect usage patterns without identifying individual users. (virtual coin-tossing and cryptographic protocols.)
      • .: to surface newly popular words that aren’t in Apple’s knowledge base or its vocabulary, links that suddenly emerge as more relevant answers to queries, or a surge in the usage of certain emojis.
      • “The traditional way that the industry solves this problem is to send every word you type, every character you type, up to their servers, and then they trawl through it all  they spot interesting things. We do end-to-end encryption, so we don’t do that.