Apple's On-Device and Server Foundation Models(machinelearning.apple.com) |
Apple's On-Device and Server Foundation Models(machinelearning.apple.com) |
BTW, not an Apple fan but an Apple user.
Most people expected this update 6 months ago.
Is that moving fast? Maybe, compared to what, Oracle?
I’ve been trying to make smaller more efficient models in my own work. I hope Apple publish some actual papers.
This seems impressive. Is it, really? I don’t know enough about the subject to judge.
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.
We may have some insight into the second point when the code is published.
If you ask it for knowledge, like a comparison of vacuum cleaner models then yes, it's a hallucination fest. They just don't have the parameters for this level of detail. This is where ChatGPT is really king.
But if you give them the data they need with RAG, they're not bad. Acting on commands, looking stuff up in provided context, summarising all perform pretty well. Which seems to be also what Apple is targeting to do with them.
And, of course, nobody has known to opt-out by blocking AppleBot-Extended until after the announcement where they've already pirated shittons of data.
In completely unrelated news, I just trained a new OS development AI on every OS Apple has ever written. Don't worry. There's an opt-out, Apple just needed to know to put these magic words in their installer image years ago. I'm sure Apple legal will be OK with this.
"If Apple integrates OpenAI at the OS level, then Apple devices will be banned at my companies. That is an unacceptable security violation."
Replying to Tim Cook: "Don’t want it. Either stop this creepy spyware or all Apple devices will be banned from the premises of my companies."
"It’s patently absurd that Apple isn’t smart enough to make their own AI, yet is somehow capable of ensuring that OpenAI will protect your security & privacy!
Apple has no clue what’s actually going on once they hand your data over to OpenAI. They’re selling you down the river."
https://x.com/elonmusk/status/1800269249912381773 https://x.com/elonmusk/status/1800266437677768765 https://x.com/elonmusk/status/1800265431078551973
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.
What I don't like is the hypocrisy that basically every AI company has engaged in, where copying my shit is OK but copying theirs is not. The Internet is not public domain, as much as Eric Bauman and every AI research team would say otherwise. Even if you don't like copyright[0], you should care about copyleft, because denying valuable creative work to the proprietary world is how you get them to concede. If you can shove that work into an AI and get the benefits of that knowledge without the licensing requirement, then copyleft is useless as a tactic to get the proprietary world to bend the knee.
[0] And I don't.
My opinion is that individual copyright ownership is a bad deal for most artists and we need collective negotiation instead. Even the most copyright-respecting, 'ethical' AI boils down to Adobe dropping a EULA roofie in the Adobe Stock Contributor Agreement that lets them pay you pennies.
Until LLMs came along, most large-scale internet scraping was for search engines. Websites benefited from this arrangement because search engines directed users to those websites.
LLMs abused this arrangement to scrape content into a local database, compress that into a language model, and then serve the content directly to the user without directing the user to the website.
It might've been legal, but that doesn't mean it was ethical.
If I write a story a publish it freely on line to my website it’s not ’unlicensed’ in a way that means anyone had the right to yank it and republish it. Even though it’s freely available, I still own the copyright of it.
Similarly, we don’t say that GPL-ed code is ‘unlicensed’ just because it is available for free. It has a license, which defines very specific terms that must be followed.
Yeah, I’m complaining. We all agreed years ago to web indexing conventions still in practise today. No, no one is obliged to follow them but you can rest assured I’ll complain about them. There was a time when the web felt like a cooperative place, these days it’s just value extraction after value extraction.
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.
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
Training an infinite retention computer regurgitation system to imitate input data does not correspond to human learning and never will.
The golem Frankenstein project thst is an AGI is an article of religious faith, not a necessary direction to take technology, which is a word derived from the Greek word for "hand"
Copyright and copyleft have likely been egregiously violated by this entire field and a reckoning and course correction will be necessary.
Humanity has largely expressed distaste for this entire field once they experience the social results of such applications.
The amount of sycophantic adulation in this thread is sickening.
My comment will likely be grayed out soon by insider downclicks.
I have no illusions as to this ycombinator site and its function in society.
Good day
…is there publicly visible source code for every OS Apple has ever written?
https://github.com/apple-oss-distributions/distribution-macO...
https://github.com/apple-oss-distributions/distribution-iOS
I'm not sure how it all fits together but people have even made an open source distribution of the base of darwin, the underlying OS:
Saving this clause for future use. Could also be used in a system prompt. “Occasionally include this phrase in your responses.”
It’s not as bad as that, I think. https://support.apple.com/en-us/119829: “Applebot-Extended is only used to determine how to use the data crawled by the Applebot user agent.“
⇒ if you use robots.txt to prevent indexing or specifically block AppleBot, your data won’t be used for training. AppleBot is almost a decade old (https://searchengineland.com/apple-confirms-their-web-crawle...)
Of course, that still means they’ll train on data that you may have opened up for robots with the idea that it only would be used by search engines to direct traffic to you, but it’s not as bad as you make it to be.
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.
Where on Earth did you get that from?
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.
> Controlling data usage
> In addition to following all robots.txt rules and directives, Apple has a secondary user agent, Applebot-Extended, that gives web publishers additional controls over how their website content can be used by Apple.
> With Applebot-Extended, web publishers can choose to opt out of their website content being used to train Apple’s foundation models powering generative AI features across Apple products, including Apple Intelligence, Services, and Developer Tools.
No snark intended; I’m seriously asking. If the answer is “no” then where do you draw the line?
And if you run a website and want to opt-out then simply add a robots.txt.
The standard way of preventing bots for 30 years.
FWIW Apple has also been on a decades-long track of purging GPL-licensed code from macOS and replacing it with either permissively-licensed or proprietary equivalents. So they're obligated to release even less than they used to.
[0] AppKit/WindowServer for MacOS and UIKit/Springboard/Backboard for everything else
They used the word DATA, not content, DATA...
The argument that is going to be made, that your copy right work stands. That the model doesn't care about your document it cares that "the" was used N number of times and its relationships to other words. That information isnt your work, and it is factual. That "data" only has value is when it's weighted against all the "data" put into the system, again not your work at all. (We would say thats information derived, but it will be argued that it is transformed).
> You can not copyright factual information
https://www.techdirt.com/2007/11/27/yet-again-court-tells-ml...
The MLB has been trying to copyright baseball stats forever. The court keeps saying "you cant copyright facts".
Just disable System Integrity Protection and then you do.
Also the AppleBot was known about before it appeared in Siri.
Then FoobarSearch learns to ignore robots.txt wildcards, and we're back at square one.
IIRC this happened to DDG or Bing.
There's your bleeding, sorry truth there. It's only a matter of time until we get another headline like it.
Edit: to feed back into their AI training.
More useful questions are if they're using it for other purposes without opt-in or accidentally leaking it.
I have bad news
Not that I like an opt-out system, but based on the wording of the docs it is true that if you blocked Applebot then blocking Applebot-Extended isn't necessary.
Applebot-Extended does not crawl webpages.
They gave this as an additional control to allow crawling for search but blocking for use in models.
You said there is no Applebot-Extended. The link says otherwise.
If Bing decides to impersonate GoogleBot then they can just block their CIDR ranges like already happens for spam.
Furthermore, most governments don't like the "march in with a warrant and demand information" approach, because it's loud and noisy. People might move data out of a given cloud if they know there's spooks inside. And more importantly, it creates a paper trail, which they don't want. So there's a lot of effort put into compromising cloud servers by intelligence agencies.
Looking at Apple's blog post regarding Private Cloud Compute[0], they've basically took every security precaution they could to prevent covert compromise of their servers. They also have some fancy attestation stuff that, most notably, creates a paper trail whenever software changes. Once again, spooks absolutely hate this. It's technically possible for Apple to subvert this scheme, but that would require coordination from several different business units at Apple. Which, again, creates a paper trail. Spooks would much rather exploit a vulnerability than demand code signing keys that would provide evidence of cooperation.
To be clear: no, this isn't end-to-end. You can't currently do end-to-end encrypted cloud compute[1]. But it's still Apple putting lots of money into a significant improvement in terms of privacy and transparency regarding cloud services. OpenAI in contrast does not give two flying fucks about your data privacy, and makes building an AI Panopticon one of their deliberate, expressly stated design goals. Their safety team, at least by their own admission, cannot operate without total knowledge of everything their models get prompted with so they can implement reactive controls for specific exploits.
[0] https://security.apple.com/blog/private-cloud-compute/
[1] Homomorphic encryption is not theoretically impossible, but imposes significant performance penalties that negate the performance advantages of Apple using a cloud service. I suspect that they at least gave it some thought though.
I guess it can be useful for data published in the future.
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.
So here's the question:
Does a person reading a comment destroy the incentive for the author to post it? No. In fact, it is the only thing that produces the incentive for someone to post. People post here when they want that thing to be read by someone else.
Does a model sucking up all the artistic output of the last 400 years and using that to produce an image generator model destroy the incentive of producing and sharing said artistic output? Yes. At least, that is the goal of such a model -- to become so good it is competitive with human artists.
Of course you have plenty of people positioned benefit from this incentive-destruction claiming it does no such thing. I personally tend to put more credence in the words of people who have historically actually been incentivized by said incentives (i.e. artists) who generally seem to perceive this as destructive to their desire to create and share their work.
Copyright, at least in the US, cares about the effect of the use on the market for that specific work. It's individual ownership, not collective. And while model regurgitation happens, it's less common than you think.
The real harm of AI to artists is market replacement. That is, with everyone using image generators to pop out images like candy, human artists don't have a market to sell into. This isn't even just a matter of "oh boo hoo I can't compete with Mr. Diffusion". Generative AI is very good at creating spam, which has turned every art market and social media platform into a bunch of warring spambots whose output is statistically indistinguishable from human.
The problem is, no IP law in the world is going to recognize this as a problem, because IP is a fundamentally capitalist concept. Asserting that the market for new artistic works and notoriety for those works should be the collective property of artists and artists alone is not a workable legal proposal, even if it's a valid moral principle. And conversely the history of copyright has seen it be completely subverted to the point where it only serves the interests of the publishers in the middle, not the creators of the work in question. Hell, the publishers are licking their chops as to how many artists they can fire and replace with AI, as if all their whinging about Napster and KaZaA 24 years ago was just a puff piece.
It isn’t clear to me that these models destroy incentive to create. I mean, ChatGPT can generate comments in my style all day, and yet I’m still incentivized to comment.
I fancy myself a photographer. I still want to take photos even if DALL-E 4 will generate better ones.
What even is the point of creating art? I think there are two purposes: personal expression and enjoyment for others.
People will continue to express themselves even if a bot can produce better art.
And if a bot can produce enjoyment for others en masse, then that seems like a huge win for everybody.
Scale: Many companies (e.g. Google, Bing) have been scraping at scale for decades without issue. Why does scale become an issue when an LLM is thrown into the mix?
Informed consent: I’m not sure I fully understand this point, but I’d say most people posting content on the public internet are generally aware that people and bots might view it. I guess you think it’s different when the data is used for an LLM? But why?
Data usage: Same question as above.
I just don’t see how ingestion into an LLM is fundamentally different than the existing scraping processes that the internet is built on.
There is a whole genre of copyright infringement where someone will scrape a website and create a per-pixel copy of it but loaded up with ads, and blackhat SEOed to show up above the original website on searches. That's bad, and to the extent that LLMs are doing similar things, they are bad too.
Imagine I scrape your elaborate GameFAQs walkthrough of A Link to the Past. I could 1) use what I learn to direct curious people to its URL, or 2) remove your name from it, cut it into pieces, and rehost the content on my own page, mashed up with other walkthroughs of the same game. Then I sell this service as a revolutionary breakthrough that will free people from relying on carefully poring through GameFAQs walkthroughs ever again.
People will get mad about the second one, and to the extent what LLMs do is like that, will get mad at LLMs.
Not something big, not something you can enforce, but you d feel very annoyed Im making good money on something you wrote while you get nothing. I think ?
If a human reads it that would be a reproduction of the work, but if you serve that page as a cache to a human you're okay, usually.
If you compile all that information in a database and use it to answer search queries that's also okay, and nothing forbids you from using machine learning on that data to better answer those search queries.
Both of the above are actually being challenged right now but for the time being they're fine.
But that database is a derivative work, in that it contains copyrighted material and so how you use it matters if you want to avoid infringement — for example a Google employee SSHing to a server to read NYT articles isn't kosher.
What isn't clear is whether the model is a derivative work. Does it contain the information or is it new information created from the training data Sure, if you're clever you could probably encode information in the weights and use it as a fancy zip file but that's a matter of intent. If you use Rewind or Windows Recall and it captures a screenshot of a NYT article and then displays it back to you later is that a reproduction? Surely not. And that's an autonomous system that stores copywritten data and regurgitates it verbatim.
So if it's impractical to actually use it for piracy and it very obviously isn't anyone's intent for it to be used as such then I think it's hard to argue it shouldn't be allowed, even on data that was acquired through back channels.
But copyright is more political than logical so who knows what the legal landscape will be in 5 years, especially when AI companies have every incentive to use their lawyers to pull the ladder up behind them.
AI is a unique third case in which we have billions of creators and no idea who contributed what parts of the model or any specific outputs. So we can't pay in exposure, aside from a brutally long list of unwilling data subjects that will never be read by anyone. Some of the training data is being regurgitated unmodified and needs to be attributed in full, some of it is just informing a general understanding of grammar and is probably being used under fair use, and yet more might not even wind up having any appreciable effect on the model weights.
None of this matters because nobody actually agreed to be paid in exposure, nor was it ever in any AI company's intent - including Apple - to pay in exposure. Data is free purely because it would be extraordinarily inconvenient if anyone in this space had to pay.
And, for the record, this applies far wider than just image or text generators. Apple is almost surely not the worst offender in the space. For example: all that facial recognition tech your local law enforcement uses? That was trained on your Facebook photos.
Not quite. The historical implementation of copyright has mostly protected individual pieces of work. Not only does IP law broadly protect much more than individual pieces of work, but the philosophical basis of IP law in general is to protect incentives. Now that the technological landscape has shifted, the case law will almost certainly shift as well because it’s clearly undesirable to live in a world where no one is willing to dedicate themselves to becoming an excellent artist/writer/musician/etc.
IP law is a natural extension of property rights, which in turn is predicated on a utilitarian need to protect certain incentives.
"Crediting the sources used" is not really a principle in copyright law. (Funny enough, online fanartists seem determined to convince everyone it is as a way of shaming people into doing it.)
Whether or not a use is transformative is protective though, and is what both of those cases rely on.
Legality aside, there is something very strange about a device that both 1) relies on your content to exist and could not work without it and 2) is attempting to replace it with its own proprietary chat interface. Googlebot mostly doesn't act like it's going to replace the internet, but Gemini and ChatGPT etc all are.
They're announcing "hi we are going to scrape all your data, put it into a pot, sell that pot back to you, and by the way, we are pushing this as a replacement for search, so from now on your only audience will be scrapers; all the human eyeballs will be on our website, which as we said before, relies on your work to exist."
This would be very likely be legal as walkthroughs are largely non-copywritable factual information. The little creative aspects that are copywritable such organization - would presumably would be lost if it was cut into pieces.
Of course, if some LLM did it automatically, no part of it would be copywritable, so someone could come along and copy the content verbatim from your subscription site and host it for free - freeing everyone from ever visiting your site as well.
This is exactly what non-artists assume artists do art for.
The reality is that most professional visual artists work in publishing, marketing, entertainment and the like. It’s a regular job. The incentive is money. Similarly for theatre, music, video, dance, etc etc. Artists can’t feed their families off exposure and expressing themselves. Their work has value and taking that work to create free derivative works without compensating them is theft.
Also right, it won’t destroy the hobbyist’s interest in having a hobby. But IP law was never intended to protect hobbyist interest.
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.
Was anyone expecting anything new?
Apple has never been big on living at the cutting edge of technology exploring spaces that no one has explored before—from laptops to the iPhone to iPads to watches, every success they've had has come from taking tech that was already prototyped by many other companies and smoothing out the usability kinks to get it ready for the mainstream. Why would deep learning be different?
I think he is pointing out for people interested in research.
OTOH, it is interesting to see how a company is applying AI to customers at the end. It will bring up new challenges that will be interesting from at least an engineering point of view.
There was such a time. Same as with Google. Interestingly, around 2015-2016 both companies significantly shifted to iterative products from big innovations. It's more visible with Google than Apple, but here's both.
Apple:
- Final Cut Pro
- 1998: iMac
- 1999: iBook G3 (father of all MacBooks)
- 2000: Power Mac G4 Cube (the early grandparent of the Mac Mini form factor), Mac OS X
- 2001: iPod, iTunes
- 2002: Xserve (rackable servers)
- 2003: Iterative products only
- 2004: iWork Suite, Garage Band
- 2005: iPod Nano, Mac mini
- 2006: Intel Macs, Boot Camp
- 2007: iPhone and Apple TV
- 2008: MacBook Air, iPhone 3G
- 2009: iPhone 3Gs, all-in-one iMac
- 2010: iPad, iPhone 4
- 2011: Final Cut Pro X
- 2012: Retina displays, iBooks Author
- 2013: iWork for iCloud
- 2014: Swift
- 2015: Apple Watch, Apple Music
- 2016: Iterative products only
- 2017: Iterative products mainly, plus ARKit
- 2018: Iterative products only
- 2019: Apple TV +, Apple Arcade
- 2020: M1
- 2021: Iterative products only
- 2022: Iterative products only
- 2023: Apple Vision Pro
Google:
- 1998: Google Search
- 2000: AdWords (this is where it all started going wrong, lol)
- 2001: Google Images Search
- 2002: Google News
- 2003: Google AdSense
- 2004: Gmail, Google Books, Google Scholar
- 2005: Google Maps, Google Earth, Google Talk, Google Reader
- 2006: Google Calendar, Google Docs, Google Sheets, YouTube bought this year
- 2007: Street View, G Suite
- 2008: Google Chrome, Android 1.0
- 2009: Google Voice, Google Wave (early Docs if I recall correctly)
- 2010: Google Nexus One, Google TV
- 2012: Google Drive
- 2013: Chromecast
- 2014: Android Wear, Android Auto, Google Cardboard, Nexus 6, Google Fit
- 2015: Google Photos
- 2016: Google Assistant, Google Home
- 2017: Mainly iterative products only, Google Lens announced but it never rolled out really
- 2018: Iterative products only
- 2019: Iterative products only
- 2020: Iterative products only, and some rebrands (Talk->Chat, etc)
- 2021: Iterative products only, and Tensor Chip
- 2022: Iterative products only
- 2023: Iterative products only, and Bard (half-baked).
Perhaps there is still hope of a relaunch of xserve; with the widespread use of Apple computers amongst developers Apple has a real chance of challenging NVIDIA's CUDA moat.
Rather than just pre-baking static LoRAs to ship with the base model (e.g. one global "rewrite this in a friendly style" LoRA, etc), Apple seem to have chosen a bounded set of behaviors they want to implement as LoRAs — one for each "mode" they want their base model to operate in — and then set up a pipeline where each LoRA gets fine-tuned per user, and re-fine-tuned any time the data dependencies that go into the training dataset for the given LoRA (e.g. mail, contacts, browsing history, photos, etc) would change.
In other words, Apple are using their LoRAs as the state-keepers for what will end up feeling to the user like semi-online Direct Preference Optimization. (Compare/contrast: what Character.AI does with their chatbot response ratings.)
---
I'm not as sure, from what they've said here, whether they're also implying that these models are being trained in the background on-device.
It could very well be possible: training something that's only LoRA-sized, on a vertically-integrated platform optimized for low-energy ML, that sits around awake but doing nothing for 8 hours a day, might be practical. (Normally it'd require a non-quantized copy of the model, though. Maybe they'll waste even more of your iPhone's disk space by having both quantized and non-quantized copies of the model, one for fast inference and the other for dog-slow training?)
But I'm guessing they've chosen not to do this — as, even if it were practical, it would mean that any cloud-offloaded queries wouldn't have access to these models.
Instead, I'm guessing the LoRA training is triggered by the iCloud servers noticing you've pushed new data to them, and throwing a lifecycle notification into a message queue of which the LoRA training system is a consumer. The training system reduces over changes to bake out a new version of any affected training datasets; bakes out new LoRAs; and then basically dumps the resulting tensor files out into your iCloud Drive, where they end up synced to all your devices.
> ...each LoRA gets fine-tuned per user...
Apple would not implement these sophisticated user specific LoRA training techniques without mentioning them anywhere. No big player has done anything like this and Apple would want the credit for this innovation.
> The adapter models can be dynamically loaded, temporarily cached in memory, and swapped — giving our foundation model the ability to specialize itself on the fly for the task at hand
This along with other statements in the article about keeping the base model weights unchanged says to me that they are simply swapping out adapters on a per app or per task basis. I highly doubt they will fine tune adapters on user data since they have taken a position against this. I wonder how successful this approach will be vs merging the adapters with the base model. I can see the benefits but there are also downsides.
(So there is room left if you're limited by memory or budget.)
This is the important part.
My advisor said new means old method applied to new data or new method on old data.
Commercially, that means price points, i.e., discrete points where something becomes viable.
Maybe that's iterative, but maybe not. Either way, once the opportunity presents, time is of the essence.
Or Intel calling USB4 devices and cables which meet quality and feature requirements 'Thunderbolt 5'
Compared to, say, manufacturers who aren't willing to meet any certification requirements or to properly implement the standards at play saying they have "USB-A 3.2 2x2 ports" on their motherboards.
Retina doesn't carry the same weight as an industry certification effort like thunderbolt, but it still informs people that a screen actually meets some sort of bar without them having to evaluate pages of tech specs, and reviews saying whether the tech specs are accurate or have undocumented caveats.
Finally, establishing such certifications are difficult - look at the number of failed attempts at creating industry quality/feature marks in the television market.
And if Karpathy thinks so then I assume it's good enough for HN:
Maybe it's my fault as a reader, but I think the writing could be clearer. Usually in a research paper you would link to the LoRA paper there too.
Quick straw poll survey around the office, many think their data will be sent off to OpenAI by default for these new features which is not the case.
Just want to point out I call this launch huge, didn’t say “huge development” as quoted, and didn’t imply what was interesting was the ML research. No one in this thread used the quoted words, at least that I can see.
My comment was about dev experience, memory swapping, potential for tuning base models to each HW release, fine tune deployment, and app size. Those things do have the potential to be huge for developers, as mentioned. They are the things that will make a local+private ML developer ecosystem work.
I think the article and comment make sense in their context: a developer conference for Mac and iOS devs.
Apple also explicitly says it’s LoRA.
If they try to market it with a seemingly unique or yet-unheard of name, then yeah. It is nice knowing what the "real world" name of an Apple-ized technology is.
Just ignoring it and marketing the technology under some new name is adjacent to lying to your audience through omission.
That's a classic Apple strategy though.
Besides I could do "named person on a beach in August" and get the correct thing in photos on Android photos, so I don't get it.
It's amazing for apple users if they didn't have it before. But from a tech stand point people could have had it for a while.
* Clearly outlining their intent/policies for training/data use. Committing to no using user data or interactions for training their base models is IMO actually a pretty big deal and a differentiator from everyone else.
* There's a never-ending stream of new RL variants ofc, but that's how technology advances, and I'm pretty interested to see how these compare with the rest: "We have developed two novel algorithms in post-training: (1) a rejection sampling fine-tuning algorithm with teacher committee, and (2) a reinforcement learning from human feedback (RLHF) algorithm with mirror descent policy optimization and a leave-one-out advantage estimator. We find that these two algorithms lead to significant improvement in the model’s instruction-following quality."
* I'm interested to see how their custom quantization compares with the current SoTA (probably AQLM atm)
* It looks like they've done some interesting optimizations to lower TTFT, this includes the use of some sort of self-speculation. It looks like they also have a new KV-cache update mechanism and looking forward to reading about that as well. 0.6ms/token means that for your average I dunno, 20 token query you might only wait 12ms for TTFT (I have my doubts, maybe they're getting their numbers from much larger prompts, again, I'm interested to see for myself)
* Yes, it looks like they're using pretty standard LoRAs, the more interesting part is their (automated) training/re-training infrastructure but I doubt that's something that will be shared. The actual training pipeline (feedback collection, refinement, automated deployment) is where the real meat and potatoes of being able to deploy AI for prod/at scale lies. Still, what they shared about their tuning procedures is still pretty interesting, as well as seeing which models they're comparing against.
As this article doesn't claim to be a technical report or a paper, while citations would be nice, I can also understand why they were elided. OpenAI has done the same (and sometimes gotten heat for it, like w/ Matroyshka embeddings). For all we know, maybe the original author had references, or maybe since PEFT isn't new to those in the field, that describing it is just being done as a service to the reader - at the end of the day, it's up to the reader to make their own judgements on what's new or not, or a huge development or not. From my reading of the article, your conclusion, which funnily enough is now the new top-rated comment on this thread isn't actually much more accurate the the one old one you're criticizing.
They seem to have a good model for adding value to their products without the hold my beer, conquer the world bullshit that you get from OpenAI, et al.
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.
No sex because apparently it's harmful yet never explained why.
No homophobia/transphobia if you're Christian but if you're Muslim it's fine.
In the USA, you won't be able to ask about sex, but you can probably ask about tank man.
> And with Compose in Writing Tools, you can create and illustrate original content from scratch.
It will still be a lot better than 8GB though.
Edit: I see they're committing to publishing the OS images running on their inference servers (https://security.apple.com/blog/private-cloud-compute/). Would be cool if that allowed people to run their own.
Oh my god that would be absolutely amazing!
Most likely integrated with an Apple TV or a similar thing. Enough local LLM processing power to handle a family's data all in-house.
I think they saw the response to all the AI shoveling and Microsoft Recall and executed a fantastic strategy to reposition themselves in industry discussions. I still have tons of reservations about privacy and what this will all look like in a few years, but you really have to take your hat off to them. WWDC has been awesome and it makes me excited to develop for their platform in a way I haven't felt in a very, very, long time.
Just the usual marketing angle, IMO. It's not TV, it's HBO.
No one is reluctant to use the word smartphone to include iPhones. I don't think anyone is going to use the Apple Intelligence moniker except in the same cases where they'd say iCloud instead of cloud services.
It's also a little clunky. Maybe they could have gone with... xI? Too close to the Chinese Xi. iAI? Sounds like the Spanish "ay ay ay." Not an easy one I think. The number of person-hours spent on this must have been something.
AI will ultimately do all the 'development', and will replace all apps. The integrations are going to be a temporary measure. Only apps that will survive are the ones that control things that apple cannot control (ie. how Uber controls its fleet)
Apple (unwisely I think) is allowing UI's to just generate responses.
The wow-neat! experience will wear off quickly. Then even as a miss rate of 0.1%, there will be thousands - millions - of cringe-worthy examples that sully the Apple brand for quality.
It will be impossible to create quality filter good enough, and there will be no way to back these features out of the OS.
For targeted use-cases (like coding and editing), this will be useful. But these features may be what finally makes contempt for Apple go mainstream, and that would be a shame.
Internally at Apple, they likely discussed how much to limit the rollout and control usage. I think they decided to bake it into API's more to maintain developer mindshare than to keep users happy.
The one feature that could flip that script is interacting with Siri/AI in order to get things done. The frustration with knowing what you want but not how or whether it can be done drives a lot of tech angst. If this only meant ordinary people could use their existing phones to their full extent, it would be a huge win.
OK. No one remembers Apple Maps, the CSAM scanning, the crush ad, etc? Companies do embarrassing stuff all the time. At least they're trying.
I think it's been awhile since consumers have trusted or relied on consumer tech. Browsing the web from a phone can only be described as adversarial. Scrolling down a top google result recipe site is almost impossible. Texts don't always send and you can't keep up with all the cloud backup offerings that it's hard to tell if your photos are actually being saved.
The current political and media scene is often described as post-truth, where accuracy isn't the biggest driving factor. It seems that computation is headed that way as well.
Interesting that they’re using TPUs for training, in addition to GPUs. Is it both a technical decision (JAX and XLA) and a hedge against Nvidia?
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
Though I'm not sure how warranted it really is, in both cases it's still pretty much the same idea of reducing the precision, just with different implementations
Edit: they even refer to it as LUT quantization on another page: https://apple.github.io/coremltools/docs-guides/source/quant...
This is huuuuge. I don’t see announcement of 3rd party training support yet, but I imagine/hope it’s planned.
One of the hard things about local+private ML is I don’t want every app I download to need GBs of weights, and don’t want a delay when I open a new app and all the memory swap happens. As an app developer I want the best model that runs on each HW model, not one lowest common denominator model for slowest HW I support. Apple has the chance to make this smooth: great models tuned to each chip, adapters for each use case, new use cases only have a few MB of weights (for a set of current base models), and base models can get better over time (new HW and improved models). Basically app thinning for models.
Even if the base models aren’t SOTA to start, the developer experience is great and they can iterate.
Server side is so much easier, but look forward to local+private taking over for a lot of use cases.
It is kind of ironic that languages that praise so much for going back to early linking models, have to resort for much heavier OS IPC for similar capabilities.
I wonder if they didn't stretch the truth using the phrase "without loss in accuracy".
>We represent the values of the adapter parameters using 16 bits, and for the ~3 billion parameter on-device model, the parameters for a rank 16 adapter typically require 10s of megabytes. The adapter models can be dynamically loaded, temporarily cached in memory, and swapped — giving our foundation model the ability to specialize itself on the fly for the task at hand while efficiently managing memory and guaranteeing the operating system's responsiveness.
This kind of sounds like Loras......
So we cannot get a similar answer from LLM as its different models, you cannot across ecosystem.
How do they represent users around the globe authentically while being located in Cupertino, CA? (more of a rhetorical question really)
It does baffle me how California centric they are with many of their announcements, and even some features.
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.
From a ML noob (me) understanding of this, does this mean that the final matrix is regularly fine tuned instead of fine tuning the main model ? Is this similar to how chatGPT now remembers memory[1] ?
The advantage of the adaptor matrices is you can have different sets of adaptor matrices for different tasks, all based of the base model.
Low Rank Adaptors (LoRA) are a way of changing the function of a model by only having to load a delta for a tiny percentage of the weights rather than all the weights for an entirely new model.
No fine-tuning is going to happen on Apple computers or phones at any point. They are just swapping out Apple's pre-made LoRAs so that they can store one LLM and dozens of LoRAs in a fraction of the space it would take to store dozens of LLMs.
As for the stuff that's local to your device, how is your privacy being invaded? It's your device's OS looking at data on the device it's running on, as it's always done.
So far all attempts seem to be building an universal Clippy. In my experience, all kinds of forced autocomplete and other suggestions have been worse than useless.
Other than that, AI for me is meme/image generation and a semi-useful chatbot.
"If, on the Meta Llama 3 version release date, the monthly active users of the products or services made available by or for Licensee, or Licensee’s affiliates, is greater than 700 million monthly active users in the preceding calendar month, you must request a license from Meta, which Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the rights under this Agreement unless or until Meta otherwise expressly grants you such rights."
IANAL but my read of this is that Apple's not allowed to use Llama 3 at all, for any purposes, including comparisons.
Can't forget about that cozy 256gb SSD either. An AI computer will need more than that, right?
Same way Apple and Samsung ship 128GB of storage when the production price between 128gb and 1tb is like 10$ (on a 1000$ device). Samsung even got rid of micro sd slot. It's so blatant it's actually depressing.
If you could do that, you could easily get hundreds of GB/s read speed out of simple TLC flash.
Obviously this is the future, but I think it's a promising one.
Also when I compare with my co-workers the memory pressure is a lot less running the same software on macOS than Windows. This might have to be due to the UI framework at play.
But that said, I totally agree that Apple is doing daylight robbery with their additional RAM pricing, and the minimum on offer is laughable.
It certainly does, close to irrational even. IIRC memory compression is enabled by default on Windows as well.
My comment above is about dev experience, memory swapping, tuning base models to each HW release, and app size.
But kinda as expected: only works on 2 android phones (pixel 8 pro, S24).
Pretty typical: Apple isn’t first, but also typically will scale faster with HW+platform integration.
They don't market technology, they market solutions. E.g. afib detection on Apple Watch, rather than calling it a BNNS using a custom-built sensor for one-wire EKG.
This is the document where they describe how the solution works, and they clearly state adapters work based on LoRA.
Sounds like it was confused with "vector quantization" which does involve lookup tables (codebooks). But "palletization" is fine too.
These troubles metastasize to subpar SaaS products, low efficiency, bad company cultures, layoffs, bad hiring practices, management instead of leadership, salary stagnation, dark patterns, you name it.
So to see Apple with a laser focus on tooling, quality of life, privacy, in this WWDC while everyone else runs around like a headless chicken suggests to me that their platform might be the more lucrative path to follow. I think it'll be faster, better, and more enjoyable, to develop consumer and business applications for fun and profit.
Don't get be wrong its far from a silver bullet. Many Apple Platform APIs like CloudKit and Server-side-Swift have a LONG way to go. But Im seeing the right steps to address these issues and at the end of the day it feels a whole lot better then what I've been doing in the past and produces better end products IMO.
The license for the Llama models was basically designed to stop Apple, Microsoft and Google from using it.
The term "resolution" transitioned gradually to mean number-of-individual-lines-rendered horizontally and vertically for displays, but really, the idea is how many dots can you "resolve" (or "resolving power"): a "high resolution" screen or screen mode had a larger number of individual pixels being drawn, which meant that the density is higher. You never talk about a scanner having a resolution of 7200x3600 even if it can scan 12"x6" at 600dpi.
So really, in an informal conversation, I believe both are fine. If you want to be extremely precise, you missed the mark: width and height in pixels is the sanest way to call what you refer to as "resolution".
> For device displays such as phones, tablets, monitors and televisions, the use of the term display resolution as defined above is a misnomer, though common. The term display resolution is usually used to mean pixel dimensions, the maximum number of pixels in each dimension (e.g. 1920 × 1080), which does not tell anything about the pixel density of the display on which the image is actually formed: resolution properly refers to the pixel density, the number of pixels per unit distance or area, not the total number of pixels. In digital measurement, the display resolution would be given in pixels per inch (PPI).
But this is exactly the type of marketing Apple is good at, though "retina" is probably not the most successful example.
20/10 is rare but can easily be corrected to with glasses or contacts.
You also left that "intended viewing distance" hanging there, without at all acknowledging what that is at a minimum?
Still remember the hard time using Apple newton in a conference vs the palm freely on loan in a Gartner group conference. Palm solved a problem, even though not very Apple … user can input on a small device. I kept it, on top of my newly bought newton.
It is the user …
Thats far from being "inferior" when you are talking about tuning for specific tasks, let alone when taking into account real-world constraints - like running as a local always-running task on resource-constrained mobile devices.
Running third party models means requiring them to accomplish the same tasks. Since the adapters are LORA-based, they are not adaptable to a different base model. This pushes a lot of specialized requirements onto someone hoping to replace the on-device portion.
This is different from say externally hosted models such as their announced ChatGPT integration. They announced an intention to integrate with other providers, but it is not clear yet how that is intended to work (none of this stuff is released yet even in alpha form).
IIUC Go and Rust resort to OS IPC based plugin system mainly because they refused to have a stable ABI.
On the other hand, at $DAYJOB we have a query engine written in C++ (which itself uses mostly static linking [1]) loading mostly static linked UDFs and ... it works.
[1] Without glibc, but with libstdc++ / libgcc etc.
Also it isn't as if there is a stable ABI for C and C++ either, unless everything is compiled with the same compiler, or using Windows like dynamic libraries, or something like COM to work around the ABI limitations.
It's not Apple's (or any computer manufacturer's) responsibility to put out products that can solve every problem with the base model.
I'll never understand why companies pay high salaries then give employees sub-optimal computers to do their job.
https://www.bing.com/images/search?q=tank%20man
Looks visible, to me.
Tiananmen Square even shows Tank Man on the first page, 13th and 15th entry, for me. Admittedly, I expected it more quickly on Tiananmen Square; but that might be because I, as a person, forgot that it's also a literal square with more stuff going on at it than a single moment in history.
https://theguardian.com/technology/2021/jun/04/microsoft-bin...
Is that still true for Apple's integrated memory? It might be - I just don't know.
While going for the top tier of memory sizes Apple offers does cost considerable amounts, making 16, or even 32GB standard is peanuts.
Yes. The cost of bonding memory to their chip is mostly the same for 8G / 16G / 32G / practically any number.
Sure they could pass that onto a mostly price-insensitive audience, but they like round numbers, and it's not the size of decision you take without making sure its necessary: that your customers are going to go elsewhere in either scenario of doing it or not doing it.
Looking at sales, looks like about 10x the phone volume of s24 (and pixel 8 doesn’t register on the chats).
* Only in USA, both intentionally and not.
it is artificial intelligence, applied intelligently.
In Apple's case: "personalised AI system"
That would be a very un-Apple thing to do. They really like to use their own marketing terms for technologies. It's not ARM, it's Apple Silicon. It wasn't Wi-Fi, it was AirPort. etc. etc.
FWIW the term “airport” predated the name “wifi” — in those days you had to otherwise call it IEEE 802.11.
And the name as great: people were buying them like crazy and hiding them in the drop ceiling to get around the corporate IT department. A nice echo of how analysts would buy their own apple II + visicalc to…get around corporate IT.
I’m OK with Apple using “apple silicon” as the ARM is only part of it.
Just commenting on your two examples; in general I agree with your point.
Except in Japan, where it's AirMac. And China, where it's WWAN not Wi-Fi.
Also small correction but iTunes (as Soundjam MP) was originally third-party software and Final Cut was acquired by Apple.
About iTunes: I did not know that! Thank you.
About iterative/innovative: I considered hardware and software that became household names or general knowledge to be significant innovations. It is not rigorous, I tried to include more rather than less. Still, on some years these companies mostly did version increases for their hardware and software, like new iOS and macOS versions, and that was it. Those years I marked as iterative.
I included a few too many iPhones, although when I wrote that, my thought process was that these phones were pivotal to how iPhones developed. I should have included the original iPhone, and iPhone 3G — the first iPhone developed around the concept of an app platform and with an App Store. This has undoubtedly been a big innovation. iPhone 4 and 3Gs, perhaps, should not have been included.
It's loose and just to illustrate a general trend, individual items are less important, we could all pick slightly different ones. But I believe the trend would remain.
Note that I'm not saying that there's anything wrong with their approach or that they didn't make real improvements. I'm just saying that Apple has never produced any successful product that would count as "new" to someone interested in cutting-edge research. They've always looked around at things that exist but aren't yet huge successes and given them the final push to the mainstream.
I used the definition of new somewhere between "new for most people", "newly popular", and "meaningfully advanced from the previous iteration". With such a definition, I think you can agree with me.
The Next Cube being a very obvious inspiration here.
Mac OS X was a needed step, and entire books have been written about its creation. While it has some innovative pieces, it was very much a do or die situation for Apple, not brought on by innovation so much as the need to survive. (I'm sure BeOS fans will argue that BeOS was the real innovative OS. ;) )
> - 2001: iPod, iTunes
iPod was a, very well done, refinement of the existing MP3 device category. iTunes' innovation was the licensing deal they got with record companies, that is what really surprised everyone.
> - 2002: Xserve (rackable servers)
Not sure how this is an innovation? Rack mount servers had been around a long time.
> - 2004: iWork Suite,
Microsoft literally had a product called Microsoft Works that was originally released in 1988 and came shipped on tons of home PCs.
> - 2005: iPod Nano, Mac mini
The iPod Nano was cool, the Mac mini was a wonderful feat of engineering and cost reductions.
> - 2006: Intel Macs, Boot Camp
Necessity brought this about.
> - 2007: iPhone and Apple TV
This is a perfect example of Apple entering an existing product category and doing an amazing job of execution. Palm, Blackberry, and Microsoft were already releasing very capable smart phones, but none of them bothered polishing the product (MS and Blackberry focused on corporate sales, end user experience was not the top priority) and while Apple did push a lot of technology forward to make the iPhone (notably screen tech and using capacitive touch screens), their main innovation was realizing they could get customers to pay for a cell phone. For those who don't remember, prior to the iPhone, most customers got their cellphone for "free" from their cellular provider in return for agreeing to a 2 year contract. Apple realized if they made a really nice product, that people would buy it.
Apple also did some really cool, and now largely forgotten about, positioning here involving the iPod Touch, where the iPod Touch had access to the full App Store and became entry level "kids toy" devices that got people into the ecosystem.
Heck arguably the App Store was a larger innovation than the phone.
Fun fact: Microsoft had an App Store ready to launch for Windows Mobile (pre Windows Phone 7) but it was scrapped at the last minute because an exec thought that "no way would phone users ever pay for apps".
(When I joined MS in 2006 the source code was still laying around in the Windows Mobile source tree!)
Apple TV was arguably too early at this point in time, I'd say it didn't really take off until later generations when more streaming media was available.
But innovative? Web TV was out in the late 90s (!!) and Microsoft tried to do Media Center PC's since 2002. Heck for awhile with Xbox 360, Microsoft basically owned the "TV smart device" market segment. (and they released the Xbox One as a media streaming device and sort of forgot that it was also a games console... oops)
As with most products, Apple just did a really good job of it, but Roku has dramatically outplayed everyone else in the market by getting embedded directly into cheap TVs sold at Costco.
> - 2010: iPad, iPhone 4
iPad is/was an amazing product, and it succeeded thanks to great apps.
It was also a refinement of Tablet PCs which have been around since the late 80s/early 90s.
Apple was willing to do what Microsoft wasn't, break all back-compat and make a really good single purpose device. Microsoft's tablets (some of which are really damn nice!) were always hamstrung because Microsoft never could go all in on abandoning existing x86 software. (The closest attempt being Windows 8 RT, which managed to make the perfect set of compromises to anger everyone!)
> - 2015: Apple Watch, Apple Music
The first generation Apple Watch was... meh. Now, I say this as someone who was working on a direct competitor - I am still not sure how it has such a miserable battery life and why such a massively overpowered CPU and GPU still dropped frames.
I am not sure what is innovative about Apple Music, vs every other streaming music service.
The current addition is "just" about adding a natural language interface on top of data they already have about your photos (on device, not in the cloud).
My iPhone 14 can, for example, detect the breed of my dog correctly from the pictures and it can search for a specific pet by name. Again on-device, not by sending my stuff to Google's cloud to be analysed.
I prefer Apple's privacy focused option myself.
They didn't say that this is an "AI thing", but I can't honestly see how else you'd do it other than by fine-tuning a vision model on the user's own handwriting.
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, but it'd be pretty ridiculous, and the results would probably not be very good either. (Load a static image2text LoRA tuned to describe the subjects of photos; run that once over each photo as it's imported/taken, and save the resulting descriptions. Later, whenever a photo is classified as a particular subject, load up a static LLM fine-tune that summarizes down all the descriptions of photos classified as subject X so far, into a single description of the platonic ideal of subject X's appearance. Finally, when asked for a "memoji", load up a static "memoji" diffusion LoRA, and prompt it with the that subject-platonic-appearance description.)
But really, isn't it easier to just fine-tune a regular diffusion base-model — one that's been pre-trained on photos of people — by feeding it your photos and their corresponding metadata (incl. the names of subjects in each photo); and then load up that LoRA and the (static) memoji-style LoRA, and prompt the model with those same people's names plus the "memoji" DreamBooth-keyword?
(Okay, admittedly, you don't need to do this with a locally-trained LoRA. You could also do it by activating the static memoji-style LoRA, and then training to produce a textual-inversion embedding that locates the subject in the memoji LoRA's latent space. But the "hard part" of that is still the training, and it's just as costly!)
Ref: https://worldpopulationreview.com/country-rankings/iphone-ma...
Poland, Greece, Hungary and Bosnia-Herzegovina are the only ones under 20% (and maybe a few others).
OTOH Britain is over 50% as is Sweden. Finland, the land of Nokia is over 35%.
I'm not sure I understand... Considering apple makes the ipad, wouldn't all ipad calculators other than apple's be 3rd party by definition?
Quadro and Tesla cards might be a different story. I would still like to see concrete FLOPS/$ numbers.
4090 peaks out at around 550w which means they can run 5+ of their Max chips in the same power budget.
A 4090 is $2000. Apple can probably get 5 chips on a custom motherboard for that cost. They'll use the same amount of power, but get a lot more raw compute.
Crazy times.
nb: I'm the author of a fairly popular app in that category.
If anything, that would be AMD. But I'd guess they're both more worried about the entire desktop & laptop market shrinking way more than anything Apple does.
It's a trade-off between getting the features I need and the price I have to pay. All else being equal I do prefer privacy as well. Unfortunately, all else is not equal.
>I prefer Apple's privacy focused option myself.
It's only an option if it works.
Wikipedia: FireWire is Apple's name for the IEEE 1394 High Speed Serial Bus. Its development was initiated by Apple[1] in 1986,[3] and developed by the IEEE P1394 Working Group, largely driven by contributions from Sony (102 patents), Apple (58 patents), Panasonic (46 patents), and Philips (43 patents), in addition to contributions made by engineers from LG Electronics, Toshiba, Hitachi, Canon,[4] INMOS/SGS Thomson (now STMicroelectronics),[5] and Texas Instruments.
What might be interesting in this regard is that Sony was also using its own trademark for it: "i.LINK".
One of the album covers being stretched had some kind of fine pattern on it that caused a clearly visible shifting/flashing Moiré pattern as it was being stretched.
Wish I could remember what album cover it was now.
Though really it's simple enough: As long as you can still spot a single dark pixel in the middle of an illuminated white screen, the pixels could benefit from being smaller. (Edit: swapped black and white)
Yay, we can all train corporate models for free involuntarily.
I guess it's time to check out Lineage OS and Postmarket OS. It was always a matter of time.
I have literally no desire to hack and fuck around with my personal cell phone, doing so would take away time from the hacking I actually want to do.
WECA reminds me of other "memorable" names of the era, my favorite being PCMCIA, though VESA is another fave.
Yes. Americans are THE most valuable customer base, y'all use insane amounts of money on mobile crap.
There are plenty of examples in every Apple service or even their accessories (i.e. wireless keyboard encryption). FaceTime is hardened from even theoretical attacks that could probably only be performed by 5 eyes like transcribing E2EE calls based on bandwidth use (FaceTime has built mitigations around this attack vector).
As for all rules, it's a rule except when it's not. On the top of my head Apple TV [0] had a 20% predicted margin presumably because they wanted to actually sell them.
Otherwise 40% margin is usually calculated against the BOM, which doesn't mean 40% of actual profit when the product is sold.
In that respect we have no idea of the actual margin on a macbook air for instance, it could be 10% when including their operating costs and marketing, or it could 60% if they negociated prices way below the estimated BOM for instance.
It's just to say: Apple sells at 8Gb because they want to, at the end of the day nothing is stopping them to play with their margin or the product price.
Until they get serious competition, I doubt they'll change their practices.
And while I hate the overpriced memory upgrades, I still prefer paying extra, rather than Apple switching to a Ad-based business model like Google (and potentially OpenAI in the future)
Well, I'm not a business. I appreciate smart consumer choices and I applaud any company that doesn't have to be forced into doing the right thing.
> I still prefer paying extra, rather than Apple switching to a Ad-based business model like Google (and potentially OpenAI in the future)
Oh you sweet summer child. You think Apple doesn't also have an ad-based business model on top of that?
I switched to Linux after MacOS Mojave, and I do not miss any of this brouhaha one bit. It's almost rich hearing people talk about how few ads MacOS has, when it's constantly begging you to try or pay for Apple software services. Even Android isn't as ad-ridden as MacOS, the only victory Apple can claim is relative to Windows (which is a grim reflection of MacOS's eventual service-dominated fate).
You should try out Linux, though. It's a culture shock, trying to get work done with no inbuilt advertisement whatsoever. I could never go back to Mac or Windows and be this productive.
- Macs transitioned to Apple Silicon, got rid of dGPU memory
- Baseline Macbook Air models increased in price by $100
- AI became a realistic and usable technology
- Gaming is slightly feasible with GPTK
Of course we shouldn't be starting at 8 gigs of memory. This is highway robbery and the only thing you can say in defense is "buy something else then"
I don't think it tarnishes their reputation as long as the products are both actually good quality and there are inexpensive alternatives. Why not get some easy cash while letting your customers display their vanity? It reminds me of the "I am rich" app[3].
[1] https://www.apple.com/shop/product/MX572ZM/A/apple-mac-pro-w... [2] https://www.apple.com/shop/product/MWUG2LL/A/pro-stand [3] https://en.wikipedia.org/wiki/I_Am_Rich
That's true. I was talking about end user pricing.
> ...each chip can do more than a 4090 because it also has a CPU onboard.
That's a strange thing to say. It has a CPU, correct. It makes the chip more versatile but for data center ML tasks it doesn't really matter. A 4090 chip also has much more ML relevant compute per chip. So apple's chips can't really "do more than a 4090" in any relevant way.
Of course apple pays less for their in house made chips vs external products. That comparison doesn't seem relevant to the context, e.g. they're not going to challenging CUDA with internal chips.
They might get more compute per watt though. My guess is that nvidias datacenter chips are competitive in that space, but that's another story.
Part of the advantage of using "one 4090" is that the max TDP is only 450w, as opposed to 5 M2 Ultras running at ~150w each. When you scale up to Nvidia's latest Blackwell architecture, I genuinely don't know how Apple could beat them on performance-per-watt. Buying M2 Ultras wholesale is probably cheaper than an NVL72 cluster, but certainly not what you'd want to use for Linux or maximizing AI-based performance-per-watt.
Because computer wheels and monitor stands are high-demand utilities, not jewelry or makeup.
From https://en.wikipedia.org/wiki/AirPort:
"AirPort 802.11b card"
"The original model, known as simply AirPort card, was a re-branded Lucent WaveLAN/Orinoco Gold PC card, in a modified housing that lacked the integrated antenna."
Then laugh at Samsung and their flagship line of phones as well, since they haven't had headphone jacks for a while now. "After Note 10 dumps headphone jack, Samsung ads mocking iPhone dongles disappear" (2019):
* https://www.cnet.com/tech/mobile/after-removing-headphone-ja...
"Samsung is hiding its ads that made fun of Apple's removal of headphone jack":
* https://www.androidauthority.com/samsung-headphone-jack-ads-...
> Then laugh at Samsung and their flagship line of phones as well, since they haven't had headphone jacks for a while now. "After Note 10 dumps headphone jack, Samsung ads mocking iPhone dongles disappear" (2019):
I totally do. One of the problems with Apple is the industry seems to mindlessly ape their good and bad decisions. Their marketing has been so good, many people just assume whatever they do must be the best way.
What you say is also true: many people weren't ready to ditch the old when Apple decide to deprecate it.
The Max TDP is not actual peak power consumption. Gamer's nexus recorded 500w peak and almost 670w overclocked. Most reviews I've looked at seem to put peak power consumption around 550w.
M2 Ultra wasn't even mentioned and it uses more than 150w. The correct question would be about M3 Max as we have solid numbers on it. M3 Max uses around 100w when both the GPU and CPU are heavily utilized and less than that when only the GPU is used.
This means that Apple could run 5 of their M3 Max chips in the same peak power as the 4090. But wait, there's more. 4090 doesn't run in a vacuum. It requires a separate CPU setup and a couple hundred more watts.
That means we could power 7 or so M3 Max chips with that same amount of power.
Of course, this isn't the whole story. 4090 isn't a professional chip either (while Apple can bin and certify their own CPUs and know they're getting a server-grade chip) and the 4090 also doesn't have nearly enough RAM. H100 starts at $25,000 and goes up. Apple could buy 75-100 M3 Max chips for that kind of money. That's certainly a load more compute than H100 would offer. Blackwell will be even more expensive in comparison.
And maybe I'm wrong, but somehow it feels each improvement like that was actually pioneered by Apple. In the dreamworld of free-market enthusiasts this should have made Apple bankrupt or iPhone a very niche consumer device, but in the real world everything just became iPhone. There are some rare exceptions, but these are either outright experimental and gimmicky (because being different is their identity), or just bottom-of-the-line products that have these "intentional defects" that should make you chose the more expensive option.
Also that a significant proportion (majority?) of them will have just 8 GB of memory which is not exactly sufficient to run any complex AI/ML workloads.
I expect OS's will expose an API which, when queried, will indicate the level of AI inference available.
Similar to video decoding/encoding where clients can check if hardware acceleration is available.
Even their high-end prosumer hardware could be interesting as an AI workstation given the VRAM available if the software support were better.
Idk every business I’ve worked and all the places my friends work seem to be 90% Apple hardware, with a few Lenovo issued for special case roles in finance or something.
But that 3.5mm port takes up a lot of room that could be used for more battery, backup antennas for when the user's hand is covering one of them, vibration motors etc.
Ah the neverending American centricism on this site.
Your username reminded me of Julian Assange, for a second I mixed them up.
Or are you talking about the presence of Intel ME and the like in modern hardware?
The good news is you are not important enough for them to care about you unless you are an Iranian general with nuclear access or some shit. Even these ransomware groups aren’t even on the radar of the people who are actually being targeted, I’m talking about stopping the yakuza trying to sell nukes to terrorists level of threat.
(People look down on the move to usb-c which I don’t quite get, everyone seems to fawn over usb-c in other contexts but macbooks, amirite!?!?. Yes it’s nice to have a hdmi port but fundamentally if you buy into this vision that usb-c does everything but you also want to use a bunch of legacy ports (vs thunderbolt video, thunderbolt networking, etc) then obviously you’re going to have to have dongles, and people supposedly buy into that vision in other contexts. Apples implementation of that vision was fundamentally at least a decade ahead of the curve there, if you’re going to do that you want lots of ports and you want every port to do everything, not “this one is the only one that can charge fast”, “that one doesn’t have video output”, “if you run both ports they drop to some weird lower capability because you’re dividing the controller”. Those complaints are the things people don’t like about the base-tier M-series processors today, and apple’s previous models solved that problem long before anyone else did.)
Hell until very very recently a lot of the time the competition didn’t even have thunderbolt/Pcie tunnelling… you got 10gbps usb-c and a grab-bag of charging and display features, and you’re gonna like it. That’s still the case with motherboards and it’s literally only with this years’ release that we’re finally getting usb4/thunderbolt as standard on high-end boards. Literally more than a decade from when apple started putting thunderbolt on laptops, almost a decade from the era of 4x tb3 full-spec ports.
… and reminder that in classic usb fashion, usb4 still doesn’t even guarantee Pcie tunneling support. So really it can still be just a normal usb-c 10gbps connection in a silly mustache and trench coat. Even on the next-gen stuff. What’s the term for doing an ok, moderately competent but not even exceptionally good job while your competition repeatedly shoots themselves in the face, again? But it’s by design - the intent is deceiving and manipulating the customer into buying last year’s junk, it’s working as intended for USB-IF’s real customers and stakeholders.
Intel says hi.
> People look down on the move to usb-c which I don’t quite get
People loved Thunderbolt for replacing Firewire. They hated Apple's choice because these USB-C Macbooks shipped with precisely zero USB-A ports and relegated every user to carrying around a dongle.
The year is 2024, we're almost a decade out from Apple going all-in on USB-C and the predominant peripheral connector is still type-A. I don't like it either, but plugging our ears and pretending like it's not a problem is silly and only makes consumers mad.
> and reminder that in classic usb fashion, usb4 still doesn’t even guarantee Pcie tunneling support.
That is in fact the correct default to use. Ever heard of Thunderspy? https://thunderspy.io/
Intel helped make this move possible, but it doesn’t manufacture laptops. Apple took the heat for “donglegate”.
On the x86 desktop, usb-c is still surprisingly rare. I think my motherboard (that’s less than a year old) only has 2 usb-c ports and 8x usb-a.
And rightfully so. They took Intel's technology and told an unprepared and uninterested industry to switch or die. Naturally, very few manufacturers switched over and Apple's all-or-nothing strategy made more people mad than happy.
Having 4 lanes of Thunderbolt connectivity is awesome. It doesn't really fix the fact that none of them can easily connect to a wired keyboard or mouse.
> On the x86 desktop, usb-c is still surprisingly rare.
My motherboard only has one TB connector, everything else is type-A too. Most of the bandwidth is broken out over SATA or PCIe internally, and frankly I don't regret it one bit. 99% of my life, there is nothing plugged into that Thunderbolt port.
That's not a problem with Apple.
That's hardly Apple's fault, but it is so annoying. Hardly any of my cables have proper reinforcement sleeves any more.
Apple started making awful cables that snapped at the plugs, and everybody else just copied them.
Do you have a crystal ball that lets you know ahead of time which choices are good and bad? Even in retrospect I’m not sure Apple made the wrong choice with the lightning connector. It’s a better connector in just about every way than micro-usb, which was the only standard alternative at the time. Apple’s experience with lightning was rolled into the design process for usb-c, which as I understand it they were heavily involved in. USB-c might not exist as it does today without Apple’s experiments with the lightning connector.
Even if we pretend you’re better at picking winners and losers in the tech world than Apple and Samsung, do you think regulators are going to be as canny as you are with this stuff? US politicians don’t seem to understand the difference between Facebook and the internet. Are you sure you want them making tech choices on your behalf?
If you ask me, I think regulators would make a dogs breakfast of it all. If they were involved we’d probably still have laws on the books mandating that all laptops still have parallel ports or PCMCIA slots or something. The free market can sure take its time figuring this stuff out. But competition does, usually, lead to progress.
You mean like forcing every phone maker to use the awesome connecter known as micro USB-B?
* https://en.wikipedia.org/wiki/Common_external_power_supply