![]() ![]() This allows the machine to algorithmically make informed guesses about data it hasn't previously encountered, if the new data is similar to that with which it was trained. They then give it time to spin its cycles getting more accurate at labeling or interpreting that data over time, based on positive or negative feedback. With machine learning, in addition to telling a computer what to do, programmers give it a data set relevant to the task and a methodology for analyzing that data set. But what if something else happens-even a minor variation? Well, programmers can get quite creative and elaborate to define sophisticated behaviors, but the machine is incapable of making judgments of its own. Traditional models of computer programming involve telling the computer what to do at all times, and in advance if precisely this happens, then do exactly this. While computers can process certain data more quickly or accurately than humans can, they are still ultimately not intelligent. Quick primer: What is machine learning, exactly? Contrast this with Google, for example, which places AI at the center of much of its messaging to consumers. "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." How does Apple use machine learning today?Īpple has made a habit of crediting machine learning with improving some features in the iPhone, Apple Watch, or iPad in its recent marketing presentations, but it rarely goes into much detail-and most people who buy an iPhone never watched those presentations, anyway. "Google is an amazing company, and there's some really great technologists working there," he said. What I'm interested in is seeing these experiences be used at scale in the world. What are we doing a really, really good job at? Letting somebody take notes and be productive with their creative thoughts on digital paper. It's just unique opportunities to do a really, really good job. ![]() We made the Pencil, we made the iPad, we made the software for both. Speaking again of the handwriting example, Giannandrea made the case that Apple is best positioned to “lead the industry” in building machine intelligence-driven features and products: and you just focus on what happened, as opposed to how it happened." 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.įurther Reading The basics of modern AI-how does it work and will it destroy society this year?Borchers chimed in too, adding, "This is clearly our approach, with everything that we do, which is, 'Let's focus on what the benefit is, not how you got there.' And in the best cases, it becomes automagic. The introduction of Macs with Apple silicon later this year will bring many of the same machine intelligence developments to the company's laptops and desktops, too. Machine intelligence-driven functionality increasingly dominates the keynotes where Apple executives take the stage to introduce new features for iPhones, iPads, or the Apple Watch. And with ML, many tech enthusiasts say that more data means better models-but Apple is not known for data collection in the same way as, say, Google.ĭespite this, Apple has included dedicated hardware for machine learning tasks in most of the devices it ships. That's partially because people associate AI with digital assistants, and reviewers frequently call Siri less useful than Google Assistant or Amazon Alexa. Further Reading Apple has hired Google’s head of search and artificial intelligenceHistorically, Apple has not had a public reputation for leading in this area. ![]()
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