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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

#281
post #217

Earlier quoted context omitted.

Well, here's another one: they promised that your local (non-iCloud) photos don't leave the device. Yet they will now — among many other things they mentioned doing with your photos — allow you to generate "Memoji" that look like the people in your photos. Which includes the non-iCloud photos. I can't picture any way to use a RAG to do that. I can picture a way to do that that doesn't involve any model fine-tuning, b…

I believe this could be achieved by providing a seed image to the diffusion model and generating memoji based on it. This way fine tuning isn't required.

Yup this is pretty much it, and DALLE and others can do this already

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

#282

Earlier quoted context omitted.

Except Apple isn't really for the rest of us. Outside of America and a handful wealthy western countries it's for the top 5-20% earners only.

In the EU the market share is 30%

Yes but not evenly distributed, BeNeLux, Germany, Austria, and Nordic countries have a lot of iPhone users, while moving further east (or south) you see lower market share. Maybe it’s “two handfuls” of wealthy western countries rather than just one, but I think OPs point holds true.

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

#284

For people interested in AI research, there's nothing new here. IMO they should do a better job of referencing existing papers and techniques. The way they wrote about "adaptors" can make it seem like it's something novel, but it's actually just re-iterating vanilla LoRA. It was enough to convince one of the top-voted HackerNews comments that this was a "huge development". Benchmarks are nice though.

"AI for the rest of us."

This sounds like every newcomers to the stage except for big players like Apple.

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

#285
post #249

Earlier quoted context omitted.

Not at Apple's price points.

I think NVIDIA has the highest hardware markup at the moment.

You get considerably more ML FLOPS per dollar in a 4090 than any mac. It seems like the base M2 MAX is at roughly the same price point. It does grant you more RAM.

Quadro and Tesla cards might be a different story. I would still like to see concrete FLOPS/$ numbers.

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

#286

Halfway 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?

Bet it depends on the country.

In the USA, you won't be able to ask about sex, but you can probably ask about tank man.

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

#287
post #286

Earlier quoted context omitted.

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?

Bet it depends on the country. In the USA, you won't be able to ask about sex, but you can probably ask about tank man.

I would've thought the same until Microsoft started hiding tank man results in Bing. I'm not so sure if companies will start training different models for every oppressive regime.

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

#288

Earlier quoted context omitted.

Those who dislike censorship and enjoy hacking avoid iPhones for obvious reasons.

People who understand cybersecurity hygiene use iPhones for obvious reasons

Those cybersecurity experts are using GrapheneOS and certainly not an iPhone where they can't even check if all is going well...

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

#289
post #208
post #204

Earlier quoted context omitted.

I don't think the LoRAs are fine-tuned locally at all. It sounds like they use RAG to access data.

Consider a feature from earlier in the keynote: the thing Notes (and Math Notes) does now where it fixes up your handwriting into a facsimile of your handwriting, with the resulting letters then acting semantically as text (snapping to a baseline grid; being reflowable; being interpretable as math equations) but still having the kind of long-distance context-dependent variations that can't be accomplished by just gen…

I didn't see the presentation but judging by your description, this is achievable using in-context learning.

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

#290
post #285

Earlier quoted context omitted.

I think NVIDIA has the highest hardware markup at the moment.

You get considerably more ML FLOPS per dollar in a 4090 than any mac. It seems like the base M2 MAX is at roughly the same price point. It does grant you more RAM. Quadro and Tesla cards might be a different story. I would still like to see concrete FLOPS/$ numbers.

The M2 is a chip designed to be in a laptop (and it is quite powerful given its low power consumption). Presumedly they have a different chip or at least completely different configuration (RAM, network, etc.) in their data centers.
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