21–30 of 562 posts
> Our foundation models are fine-tuned for users’ everyday activities, and can dynamically specialize themselves on-the-fly for the task at hand. We utilize adapters, small neural network modules that can be plugged into various layers of the pre-trained model, to fine-tune our models for specific tasks. For our models we adapt the attention matrices, the attention projection matrix, and the fully connected layers in…
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/
> 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.
Frankly I tried a samsung device which I would have assume is the worst here, and the promises are exactly the same. They show you two prompts, one for locally processed services (e.g. translation), and one when data is about to leave your device, and you can accept or reject them separately. But both of them are basically unverifiable promises and closed source services.
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.
> 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 fa…
Where on Earth did you get that from?
Will these smaller on device models lead to a crash in GPU prices?
> 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…
This is wrong. AppleBot identifier hasn't changed: https://support.apple.com/en-us/119829
There is no AppleBot-Extended. And if you blocked it in the past it remains blocked.
> 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…
They include data about the ratio of which outputs human graders preferred (for server side it’s better than 3.5, worse than 4).
BUT, the interesting chart to me is „Human Evaluation of Output Harmfulness” which is much, much ”better„ than the other models. Both on-device and server-side.
I wonder if that’s part of wanting to have gpt as the „level 3”. Making their own models much more cautious, and using OpenAI’s models in a way that makes it clear „it was ChatGPT that said this, not us”.
Instruction following accuracy seems to be really good as well.