Apple, although was one of the first companies to pave way for voice-based digital assistants with Siri, it really never had the research data nor the resources to approach AI development as its rivals. Giannandrea and Google announced early this week that he was stepping down from his role, but exactly where Giannandrea was going was unknown at the time.
According to the letter, employees raised their concerns about the program internally and Google board member Diane Greene said that the company's work would not involve operating drones or launching weapons.
Craig Federighi, a senior software executive at Apple, took over the Siri division a year ago. The experience varies wildly on each device and on the most important product of all, HomePod, Siri is largely an afterthought when it comes to anything beyond music controls.
French manufacturing growth slowest in a year in March,…
Still, charges rose at only a modest pace, to suggest that manufacturers' margins remained under pressure. As has been the case since last November, Indian manufacturers raised their purchasing activity.
Giannandrea joined Google in 2010 when it purchased Metaweb, a start-up where he served as chief technology officer.
In 2016, he was named the head of a new combined division at Google, pairing its powerful search unit and artificial intelligence divisions, after Amit Singhal, Google's longtime search czar, left. Jeff Dean will lead Google's AI efforts, with the 19-year veteran and widely revered engineer continuing to lead Google Brain - the company's internal machine learning research team. In terms of quality, Apple Siri is way behind Google Assistant, which uses the same ground-breaking algorithms that power Google Translate and Google search image.
Interestingly, Apple's less than impressive progress in AI can be considered to be the result of its own data collection and privacy policies. This could mean Apple is finally on track to make Siri something worth using.
Apple has always taken a very strong stance on privacy, but this isn't great for machine learning since neural networks require enormous amounts of data to improve.