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How AI and Machine Learning Work at Apple

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Re: How AI and Machine Learning Work at Apple

#131

Post by throwanem »

sangnoir wrote: post #121
dummyacc111 wrote: post #120

Earlier quoted context omitted.

> Until Apple does something truly radical (like 100% encrypted information that is processing on-device) This is exactly what Apple does, which should answer question B.

> This is exactly what Apple does That is not true, even the puff-piece article admits as much if you read it carefully. This can be proven trivially: is Siri processed locally?

Answering only the trivially erroneous objection, and ignoring two other better-founded ones, does not reflect confidence in your stance.

Re: How AI and Machine Learning Work at Apple

#132

Post by fsloth »

sangnoir wrote: post #63
IBM wrote: post #43

Earlier quoted context omitted.

If what AI/ML talent wants is to do research and draw a Silicon Valley salary then you're right, they're not going to work at Apple. Some people don't want to work at Apple because they can't describe their job on their LinkedIn pages or tell their friends and family about what they do. Others don't like that they have to buy their own meals at Caffe Macs. I don't think Apple worries about this too much. What they do…

> If what AI/ML talent wants is to do research and draw a Silicon Valley salary then you're right, they're not going to work at Apple. Let me simplify that further: "If what AI/ML talent wants is to publish ...then you're right, they're not going to work at Apple". Money may or may not be a factor, but Apples secrecy won't allow the talent to publish their research. Given that AI is highly driven by research right no…

"what talent would want to work somewhere where they can't publish research papers?"

There are lot of people out there who don't particularly merit the publish-or-perish ethos of academia and would rather publish only after they have something worthwhile to publish and not just footnotes to an established scheme.

E.g. https://www.theguardian.com/commentisfree/2014/feb/14/higgs-...

and so on.

Re: How AI and Machine Learning Work at Apple

#133

Post by ThomPete »

throwanem wrote: post #59
ThomPete wrote: post #54

Earlier quoted context omitted.

There is only one way to win and that is to publish. Innovation in this field doesn't happen in a vacuum but by multiple simpler components combine in ever more emerging complexity.

Perhaps they seek to win a game other than the one you have in mind.

I don't think there are any other game to win for a company like Apple.

Re: How AI and Machine Learning Work at Apple

#134

Post by sangnoir »

leereeves wrote: post #130
sangnoir wrote: post #127

Earlier quoted context omitted.

> “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!

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.

> 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

#135

Post by sangnoir »

throwanem wrote: post #131
sangnoir wrote: post #121

Earlier quoted context omitted.

> This is exactly what Apple does That is not true, even the puff-piece article admits as much if you read it carefully. This can be proven trivially: is Siri processed locally?

Answering only the trivially erroneous objection, and ignoring two other better-founded ones, does not reflect confidence in your stance.

In the footnote to the point gp was replying, I had said

> Until Apple does something truly radical (like 100% encrypted information that is processing on-device)

You can call it trivial, I will argue otherwise. No one knows outside of Apple how big a percentage cloud-based processing is, but my point remains - Apple's does what everyone else is doing, the only difference is proportion.

Re: How AI and Machine Learning Work at Apple

#136

Post by plg »

has anyone considered the possibility that perhaps Apple has made a judgement that the recent resurgence in AI/ML is, (like all of them before, over the past 50 years) overblown, and Apple would rather spend their time and resources on other things?

There's no doubt that the recent advances in deep learning have improved ML/AI in certain specific domains ... but it seems like every 15-20 years or so we see an advance and an accompanying narrative that "AI is back! fully automated future is near!"... which fizzles out, again

Also, Apple has a more humanist tradition than Google, FB, etc, and it's my impression that they value the human element perhaps more.

Sure, there's Siri, but Siri strikes me more like an ongoing experiment than a fully fledged whole hog "let's put all our eggs in this ML/AI basket"

Re: How AI and Machine Learning Work at Apple

#138

Post by leereeves »

sangnoir wrote: post #134
leereeves wrote: post #130

Earlier quoted context omitted.

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.

> 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.

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

#139

Post by sangnoir »

leereeves wrote: post #138
sangnoir wrote: post #134

Earlier quoted context omitted.

> 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.

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.

"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

#140

Post by leereeves »

sangnoir wrote: post #139
leereeves wrote: post #138

Earlier quoted context omitted.

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.

"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…

I'm not sure what you mean by "the AI behind it". A particular model, the theory, the hardware, or something else?
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