Earlier quoted context omitted.
iPhone share is ~59% of smartphones in the US. Their customer base is effectively all demographics.
Those who dislike censorship and enjoy hacking avoid iPhones for obvious reasons.
Apple's On-Device and Server Foundation Models
261–270 of 562 posts
Re: Apple's On-Device and Server Foundation Models
#262Re: Apple's On-Device and Server Foundation Models
#263> For on-device inference, we use low-bit palletization, a critical optimization technique that achieves the necessary memory, power, and performance requirements. Did they go over the entire text with a thesaurus? I've never seen "palletization" be used as a viable synonym for "quantization" before, and I've read quite a few papers on LLM quantization
https://apple.github.io/coremltools/docs-guides/source/palet...
Re: Apple's On-Device and Server Foundation Models
#264Halfway down the article contains some great charts with comparisons to other relevant models, like Mistral-7B for the on-device models, and both gpt-3.5 and 4 for the server-side models. 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 ”bette…
I want to know what they consider "harmful". Is it going to refuse to operate for sex workers, murder mystery writers, or people who use knives?
Re: Apple's On-Device and Server Foundation Models
#265it would have been nice if they allowed you to build your own apple AI system (i refused to redefine apples AI as just AI :-p ) using clusters of mac minis and mac pros. but of course they still want that data for themselves like google does. its secure against everyone but apple and the NSA probably lol.
Re: Apple's On-Device and Server Foundation Models
#266> 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…
I hate to tell you, but I've been training a neural network on the internet for over a decade now. Specifically the one between my ears. Unfortunately, it seems to be gradually going insane.
I wouldn't say it's fair for any company to capitalize the content that users have created but have no way to monetize, and not even saying thanks
Re: Apple's On-Device and Server Foundation Models
#267Re: Apple's On-Device and Server Foundation Models
#268> For on-device inference, we use low-bit palletization, a critical optimization technique that achieves the necessary memory, power, and performance requirements. Did they go over the entire text with a thesaurus? I've never seen "palletization" be used as a viable synonym for "quantization" before, and I've read quite a few papers on LLM quantization
Re: Apple's On-Device and Server Foundation Models
#269> For on-device inference, we use low-bit palletization, a critical optimization technique that achieves the necessary memory, power, and performance requirements. Did they go over the entire text with a thesaurus? I've never seen "palletization" be used as a viable synonym for "quantization" before, and I've read quite a few papers on LLM quantization
https://apple.github.io/coremltools/docs-guides/source/palet...