Machine learning (ML) and artificial intelligence (AI) are now present in virtually every aspect of the iPhone, but Apple hasn’t promoted these innovations as aggressively as some of its rivals. Apple has not had a mainstream image for being a leader in this field in the past. This is due in part to the fact that people equate AI with digital assistants, and critics often describe Siri as less helpful than Google Assistant or Amazon Alexa. And, when it comes to machine learning, many tech fans believe that more data equals improved models — but Apple is not popular for data gathering in the same way that, say, Google is. Well, this is soon to be changing. This is the third part of series on how notable tech giants work/develop with AI.
How seriously Apple is taking the situation can be seen from their acquisition of AI related companies. According to report released by GlobalData, Apple is the leading buyer of companies in the global artificial intelligence market. Apple bought the most AI firms between 2016 and 2020, outnumbering Accenture, Google, Microsoft, and Facebook, all of which had a large number of AI acquisitions.
Apple has acquired companies such as Emotient, Turi, Glimpse, RealFace, Shazam, SensoMotoric, Silk Labs, Drive.ai, Laserlike, SpectralEdge, Voysis, XNOR.ai, and others in recent years in order to improve the AI and machine learning capabilities of its products and services. Also Apple does not disclose all its purchases, thus it’s likely that Apple has acquired other artificial intelligence firms that have gone unnoticed.
Apple CEO Tim Cook in February shareholder meeting stated that the company had purchased nearly 100 companies in the previous six years. “We’re not afraid to look at acquisitions of any size,” said Cook. “Focus is on small, innovative companies that complement our products and help push them forward.” Many of Apple’s investments have been made with the aim of strengthening Siri. Apple purchased Inductiv to enhance Siri’s data, while Voysis was bought to upgrade Siri’s natural language understanding. Meanwhile, PullString makes it easy for iOS developers to incorporate Siri features into their applications.
Apple’s AI spree does not end with purchasing AI startups. They have poached talent from their direct rivals. John Giannandrea, Apple’s Senior Vice President for Machine Learning and AI Strategy, as well as with Bob Borchers, VP of Product Marketing previously worked at Google before joining Apple in the last few years. Borchers returned to Apple after a brief hiatus, serving as senior director of marketing for the iPhone until 2009. And Giannandrea’s departure from Google to Apple in 2018 was highly publicized; he was Google’s head of AI and search at the time.
Google and Apple are two very separate businesses. Google is known for engaging in, and in some cases leading, the AI research group, while Apple used to do the majority of its work behind closed doors. That has changed in recent years, as machine learning drives several features in Apple’s devices and the company has expanded its involvement with the AI community, embracing the openness of AI research which could be attributed the new personnel.
So, what is Apple doing with ML and AI? In recent conferences, Apple has made a habit of crediting machine learning with enhancing certain features in the iPhone, Apple Watch, or iPad, but it hardly goes into any detail — and most people who purchase an iPhone never watch such presentations in the first place. Contrast this with Google, which puts AI at the heart of much of its user messaging. There are several examples of machine learning being used in Apple’s apps and applications, the majority of which are introduced in the last few years.
ML is used to assist the iPad’s program in differentiating between a user unintentionally pressing their palm against the screen while drawing with the Apple Pencil and a deliberate press intended to provide feedback. It is used to track users’ consumption patterns in order to maximize smartphone battery life and charging, allowing users to invest more time between charges but still protecting the battery’s long-term viability. It’s used to make app suggestions.
Then there’s Siri, which is perhaps the first thing any iPhone user thinks about when they hear the term “artificial intelligence.” Machine learning powers many facets of Siri, from voice recognition to Siri’s efforts to provide useful responses. Clever iPhone users will also note that machine learning is at the root of the Images app’s ability to automatically sort photographs into pre-made galleries or to correctly return photos of a friend named Jane when her name is inserted into the app’s search area.
Only a few consumers may be aware that machine learning is in action. For example, when you press the shutter button on your iPhone, it can take several photos in quick succession. After that, an ML-trained algorithm analyzes each image and can combine the best sections of each image into a single result. Phones have long used image signal processors (ISP) to improve picture output digitally and in real time, but Apple improved the process in 2018 by having the ISP in the iPhone work closely with the company’s newly introduced machine learning-focused processor, the Neural Engine.
AI and machine learning will only become more popular in the coming years, if major tech firms and venture capital investments are to be believed. Whatever happens, Giannandrea and Borchers suggests machine learning now plays a role in many of what Apple does for its products, as well as many of the features that users use on a regular basis. And, with the Neural Engine in Macs from last fall, AI’s importance at Apple is likely to rise. Think Different is a virtue of Apple it is definitely thinking differently in AI department.
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Written by Wanonno Iqtyider