if you want a job at Apple, this might be a good time to apply. The company was reported to be in the process of ramping up its hiring of artificial intelligence experts, recruiting from PhD programs, posting dozens of job listings and greatly increasing the size of its AI staff.
The goal is to challenge Google in an area the Internet search giant has long dominated: smartphone features that give users what they want before they ask.
As part of its push, the company is currently trying to hire at least 86 more employees with expertise in the branch of artificial intelligence known as machine learning, according to a recent analysis of Apple job postings. The company has also stepped up its courtship of machine-learning PhD's, joining Google, Amazon, Facebook and others in a fierce contest, leading academics say.
But some experts say the iPhone maker's strict stance on privacy is likely to undermine its ability to compete in the rapidly progressing field.
Machine learning, which helps devices infer from experience what users are likely to want next, relies on crunching vast troves of data to provide unprompted services, such as the scores for a favorite sports team or reminders of when to leave for an appointment based on traffic.
The larger the universe of users providing data about their habits, the better predictions can be about what an individual might want. But Apple analyzes its users' behavior under self-imposed constraints to better protect their data from outsiders.
That means Apple largely relies on analyzing the data on each user’s iPhone rather than sending it to the cloud, where it can be studied alongside information from millions of others.
"They want to make a phone that responds to you very quickly without knowledge of the rest of the world," said Joseph Gonzalez, co-founder of Dato, a machine learning startup. "It's harder to do that."
Beyond Siri
The Cupertino-based tech titan’s strategy will came into clearer focus last 9 September 2015, after it launched its new iPhones and latest mobile operating system, iOS 9. The release also includes a variety of intelligent reminders, which analysts expect will rival the offerings from Google's Android.
While Apple helped pioneer mobile intelligence - it’s Siri introduced the concept of a digital assistant to consumers in 2011 - the company has since lost ground to Google and Microsoft, whose digital assistants have become more adept at learning about users and helping them with their daily routines.
As users increasingly demand phones that understand them and tailor services accordingly, Apple cannot afford to let the gap persist, experts say. The iPhone generated almost two-thirds of Apple's revenue in the most recent quarter, so even a small advantage for Android poses a threat.
"What seemed like science fiction only four years ago has become an expectation," said venture capitalist Gary Morgenthaler, who was one of the original investors in Siri before it was acquired by Apple in 2010.
Playing Catch-up
While Apple got off to a slow start on hiring for machine learning jobs, it is closing in on its competitors, said Oren Etzioni, who is CEO of the Allen Institute for Artificial Intelligence and a professor at the University of Washington.
"In the past, Apple has not been at the vanguard of machine learning and cutting edge artificial intelligence work, but that is rapidly changing,” he said. “They are after the best and the brightest, just like everybody else.”
Acquisitions of startups such as podcasting app Swell, social media analytics firm Topsy and personal assistant app Cue have also expanded Apple’s pool of experts in the field.
Apple does not reveal the number of people working on its machine learning efforts.
But one former Apple employee in the area, who asked not to be named to protect professional relationships, estimated the number of machine learning experts had tripled or quadrupled in the past few years.
Many of the currently posted positions are slated for software efforts, from building on Siri’s smarts to the burgeoning search features in iOS. The company is also hiring machine learning experts for divisions such as product marketing and retail, suggesting a broad-based effort to capitalize on data.
The goal is to challenge Google in an area the Internet search giant has long dominated: smartphone features that give users what they want before they ask.
As part of its push, the company is currently trying to hire at least 86 more employees with expertise in the branch of artificial intelligence known as machine learning, according to a recent analysis of Apple job postings. The company has also stepped up its courtship of machine-learning PhD's, joining Google, Amazon, Facebook and others in a fierce contest, leading academics say.
But some experts say the iPhone maker's strict stance on privacy is likely to undermine its ability to compete in the rapidly progressing field.
Machine learning, which helps devices infer from experience what users are likely to want next, relies on crunching vast troves of data to provide unprompted services, such as the scores for a favorite sports team or reminders of when to leave for an appointment based on traffic.
The larger the universe of users providing data about their habits, the better predictions can be about what an individual might want. But Apple analyzes its users' behavior under self-imposed constraints to better protect their data from outsiders.
That means Apple largely relies on analyzing the data on each user’s iPhone rather than sending it to the cloud, where it can be studied alongside information from millions of others.
"They want to make a phone that responds to you very quickly without knowledge of the rest of the world," said Joseph Gonzalez, co-founder of Dato, a machine learning startup. "It's harder to do that."
Beyond Siri
The Cupertino-based tech titan’s strategy will came into clearer focus last 9 September 2015, after it launched its new iPhones and latest mobile operating system, iOS 9. The release also includes a variety of intelligent reminders, which analysts expect will rival the offerings from Google's Android.
While Apple helped pioneer mobile intelligence - it’s Siri introduced the concept of a digital assistant to consumers in 2011 - the company has since lost ground to Google and Microsoft, whose digital assistants have become more adept at learning about users and helping them with their daily routines.
As users increasingly demand phones that understand them and tailor services accordingly, Apple cannot afford to let the gap persist, experts say. The iPhone generated almost two-thirds of Apple's revenue in the most recent quarter, so even a small advantage for Android poses a threat.
"What seemed like science fiction only four years ago has become an expectation," said venture capitalist Gary Morgenthaler, who was one of the original investors in Siri before it was acquired by Apple in 2010.
Playing Catch-up
While Apple got off to a slow start on hiring for machine learning jobs, it is closing in on its competitors, said Oren Etzioni, who is CEO of the Allen Institute for Artificial Intelligence and a professor at the University of Washington.
"In the past, Apple has not been at the vanguard of machine learning and cutting edge artificial intelligence work, but that is rapidly changing,” he said. “They are after the best and the brightest, just like everybody else.”
Acquisitions of startups such as podcasting app Swell, social media analytics firm Topsy and personal assistant app Cue have also expanded Apple’s pool of experts in the field.
Apple does not reveal the number of people working on its machine learning efforts.
But one former Apple employee in the area, who asked not to be named to protect professional relationships, estimated the number of machine learning experts had tripled or quadrupled in the past few years.
Many of the currently posted positions are slated for software efforts, from building on Siri’s smarts to the burgeoning search features in iOS. The company is also hiring machine learning experts for divisions such as product marketing and retail, suggesting a broad-based effort to capitalize on data.
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