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Re:How AI and Machine Learning Work at Apple by sangnoir on Wednesday August 24, 2016 @05:38PM (#12355451)
> The field might lose out (but there are always free riders) but does Apple?

Yes - those in the field who prefer to publish will not be willing to join Apple. How much of an opportunity cost this is to Apple remains an open question.

Re:How AI and Machine Learning Work at Apple by leereeves on Wednesday August 24, 2016 @06:34PM (#12355772)
In the article, Apple spun that as an advantage.

> “Our practices tend to reinforce a natural selection bias — those who are interested in working as a team to deliver a great product versus those whose primary motivation is publishing,” says Federighi.

And I think they may be right. Researchers often aren't the best product creators.

Re:How AI and Machine Learning Work at Apple by Chronic9q on Wednesday August 24, 2016 @09:12PM (#12356337)
> Researchers often aren't the best product creators.

You NEED researchers to build a robust autonomous vehicle or speech recognition system. Hell, you know how many electrical and materials scientists they have working on the iPhone?

Physical products actually requires science and research. Designing a responsive landing page does not.

Re:How AI and Machine Learning Work at Apple by leereeves on Thursday August 25, 2016 @03:44AM (#12357456)
You need people who understand the science but whose primary focus is developing the product, not developing new theory.

Edison and Ford vs Einstein and Faraday.

Re:How AI and Machine Learning Work at Apple by sangnoir on Thursday August 25, 2016 @04:30AM (#12357611)
> “Our practices tend to reinforce a natural selection bias — those who are interested in working as a team to deliver a great product versus those whose primary motivation is publishing,”

This is a blatantly false dichotomy: there's nothing about publishing that precludes anyone from "working as a team to deliver a great product"

> Researchers often aren't the best product creators.

Tell that to Andrew Ng!

Re:How AI and Machine Learning Work at Apple by leereeves on Thursday August 25, 2016 @05:03AM (#12357713)
Andrew Ng is a great example.

He's made enormous contributions to human knowledge, but doesn't seem to be interested in the hard work of bringing a product to market.

He even left Coursera to return to theory.

Re:How AI and Machine Learning Work at Apple by sangnoir on Thursday August 25, 2016 @08:56AM (#12358606)
> He's made enormous contributions to human knowledge, but doesn't seem to be interested in the hard work of bringing a product to market.

what exactly do you define as "the hard work of bringing a product to market"? To me, his work at Google certainly applies as bringing a product to market - the cat recognition deeplearning AI is a product.

Re:How AI and Machine Learning Work at Apple by leereeves on Thursday August 25, 2016 @09:29AM (#12358809)
To me, recognizing cats isn't a very useful product; that's just a demonstration of the technology.

That led to an interesting paper and a lot of potential applications.

By "the hard work of bringing a product to market", I meant the process of perfecting the product for actual applications.

Re:How AI and Machine Learning Work at Apple by sangnoir on Thursday August 25, 2016 @09:58AM (#12359015)
"recognizing cats" isn't a product at all - the AI behind it is the product, albeit barebones. I've heard folk on HN call such a thing an "MVP".

> By "the hard work of bringing a product to market", I meant the process of perfecting the product for actual applications

I'll have to disagree - "perfecting the product" is one of many roles required to bring a product to market. Other roles that are just as important are "dreamer/visionary", "practical starter/founder", "scale-up person". Sometimes one person can assume multiple roles, but rarely all roles, all of them are hard work.

Re:How AI and Machine Learning Work at Apple by leereeves on Thursday August 25, 2016 @10:38AM (#12359328)
I'm not sure what you mean by "the AI behind it". A particular model, the theory, the hardware, or something else?