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Apple's On-Device and Server Foundation Models

machinelearning.apple.com

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Re: Apple's On-Device and Server Foundation Models

#11

Post by bigyikes »

mdhb wrote: post #5
kmeisthax wrote: post #3

> We train our foundation models on licensed data, including data selected to enhance specific features, as well as publicly available data collected by our web-crawler, AppleBot. Web publishers have the option to opt out of the use of their web content for Apple Intelligence training with a data usage control. And, of course, nobody has known to opt-out by blocking AppleBot-Extended until after the announcement wher…

So built on stolen data essentially.

Does that imply I just stole your comment by reading it?

No snark intended; I’m seriously asking. If the answer is “no” then where do you draw the line?

Re: Apple's On-Device and Server Foundation Models

#12

Post by advael »

I'm disappointed that they make the fundamental claim that their cloud service is private with respect to user inputs passed through it and don't even a little bit talk about how that's accomplished. Even just an explanation of what guarantees they make and how would be much more interesting than explanations of their flavor of RLHF or whatever nonsense. I read the GAZELLE* paper when it came out and wondered what it would look like if a large-scale organization tried to deploy something like it.

Of course, Apple will never give adequate details about security mechanisms or privacy guarantees. They are in the business of selling you security as something that must be handled by them and them alone, and that knowing how they do it would somehow be less secure (This is the opposite of how it actually works, but also Apple loves doublespeak, and 1984 allusions have been their brand since at least 1984). I view that, like any claim by a tech company that they are keeping your data secure in any context, as security theater. Vague promises are no promises at all. Put up or shut up.

* https://arxiv.org/pdf/1801.05507

Re: Apple's On-Device and Server Foundation Models

#14

Post by advael »

multimoon wrote: post #10
kmeisthax wrote: post #3

> We train our foundation models on licensed data, including data selected to enhance specific features, as well as publicly available data collected by our web-crawler, AppleBot. Web publishers have the option to opt out of the use of their web content for Apple Intelligence training with a data usage control. And, of course, nobody has known to opt-out by blocking AppleBot-Extended until after the announcement wher…

Apple just did more to make this a privacy focused feature versus just a data mine than literally anyone else to date and still people complain. Public content on the internet is public content on the internet - I thought we had all agreed years ago that if you didn’t want your content copied, don’t make it freely available and unlicensed on the internet.

No, they said they did. Huge difference

Re: Apple's On-Device and Server Foundation Models

#15

Post by killingtime74 »

advael wrote: post #12

I'm disappointed that they make the fundamental claim that their cloud service is private with respect to user inputs passed through it and don't even a little bit talk about how that's accomplished. Even just an explanation of what guarantees they make and how would be much more interesting than explanations of their flavor of RLHF or whatever nonsense. I read the GAZELLE* paper when it came out and wondered what it…

Don't they do it in this linked article? https://security.apple.com/blog/private-cloud-compute/

Re: Apple's On-Device and Server Foundation Models

#16

Post by zer00eyz »

kmeisthax wrote: post #3

> We train our foundation models on licensed data, including data selected to enhance specific features, as well as publicly available data collected by our web-crawler, AppleBot. Web publishers have the option to opt out of the use of their web content for Apple Intelligence training with a data usage control. And, of course, nobody has known to opt-out by blocking AppleBot-Extended until after the announcement wher…

> publicly available data collected

Data, implies factual information. You can not copyright factual information.

The fact that I use the word "appalling" to describe the practice of doing this results in some vector relationship between the words. Thats the data, the fact, not the writing itself.

There are going to be a bunch of interesting court cases where the court is going to have to backtrack on copyrighting facts. Or were going to have to get some real odd legal interpretations of how LLM's work (and buy into them). Or we're going to have to change the law (giving everyone else first mover advantage).

Base on how things have been working I am betting that it's the last one, because it pulls up the ladder.

Re: Apple's On-Device and Server Foundation Models

#18

Post by urbandw311er »

epipolar wrote: post #13

It would be interesting to see how these models impact battery life. I’ve tried a few local LLMs on my iPhone 15 Pro via the PrivateLLM app, and the battery charge plummets just after a few minutes of usage.

Likely they’ll be able to take advantage of the hardware neural engine and be far more power efficient. Apple has demonstrated this is something it takes pretty seriously.

Re: Apple's On-Device and Server Foundation Models

#19

Post by GaggiX »

swatcoder wrote: post #4
GaggiX wrote: post #2

It would be cool to understand when the system will use one or the other (the ~3 billion on-device model or the bigger one on Apple servers).

Conceivably, they don't have precise answers for that yet, and won't until after they see what real-world usage looks like. They built out a system that's ready to scale to deliver features that may not work on available hardware, but they're also incentivized to minimize actual reliance on that cloud stuff as it incurs per-use costs that local runs don't.

Yeah this is probably right. If it works well enough during real-world usage it will be using the on-device model, if not then there is the bigger one on the servers. There is also GPT-4o, so they have 3 different models to use depending on the task.

Re: Apple's On-Device and Server Foundation Models

#20

Post by mdhb »

bigyikes wrote: post #11
mdhb wrote: post #5

Earlier quoted context omitted.

So built on stolen data essentially.

Does that imply I just stole your comment by reading it? No snark intended; I’m seriously asking. If the answer is “no” then where do you draw the line?

I don’t actually think this is complicated and reading a comment is not the same thing as scraping the internet and you obviously know that.

A few factors that come to mind would be:

- scale

- informed consent which there was none in this case

- how you are going to use that data. For example using everybody others work so the worlds richest company can make more money from it while giving back nothing in return is a bullshit move.

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