AKA “we completely missed the boat on this thing and are going to pretend it was intentional by focusing on an inevitable inflection point a few years out from today instead.”
Google is doing this exact same thing with Gemini, the platform behind Bard / Assistant.
Gemini has large scale models, that live in data centers, and handles complex queries. They also have a “Nano” version of the model that can live on a phone and handle simpler on-device tasks.
The smaller models are great for things like natural language UI and smart home controls. It’s also way faster and capable of working offline. A big use case for offline AI has been hiking with the Apple Watch in areas with no reception.
Also battery management, background tasks power distribution and hardware energy efficiency, i mean it would be great to have ai that adapted hardware energy consumption settings depending on my use case, yes i know that algorithms already exist to do that, but it would be great to have much much more flexible energy manager based on ai that accommodate and adapt to my use cases
This is a Financial Times article, regurgitated by Ars Technica. The article isn’t by a tech journalist, it’s by a business journalist, and their definition of “AI” is a lot looser than what you’re thinking of.
I’m pretty sure they’re talking about things that Apple is already doing not just on current hardware but even on hardware from a few years ago. For example the keyboard on iOS now uses pretty much the same technology as ChatGPT but scaled way way down to the point where “Tiny Language Model” would probably be more accurate. I wouldn’t be surprised if the training data is as small as ten megabytes, compared to half a terabyte for ChatGPT.
The model will learn that you say “Fuck Yeah!” to one person and “That is interesting, thanks for sharing it with me.” to someone else. Very cool technology - but it’s not AI. The keyboard really will suggest swear words now by the way - if you’ve used them previously in a similar context to the current one. The old algorithmic keyboard had hardcoded “do not swear, ever” logic.
Remember, this probably isn’t an either or thing. Both Apple and Google have been offloading certain AI tasks to devices to speed up response time and process certain requests offline.
Yep, though Google is happy to process your data in the cloud constantly while Apple consistently tries to find ways to achieve it locally, which is generally better for privacy and security but also cheaper for them too.
I’m going to blow your mind here…the ‘cloud’ is just two or three data centres with replication turned on. It’s mostly a buzz word to charge a bit more
Eh, it’s a bit more than that. I work on a private cloud, the implications of it being a cloud versus traditional bare metal or virtualization platforms are around the APIs, quick spin up/down cycles, fully integrated recovery, imaging and remote console systems, integration with automated deployment platforms and others. It’s not just a buzz word.
Most of that’s on any half decent commercial server. You’re right there’s definitely some differences though.
I actually worked on our corporate move from private servers (main, backup and dr) to Azure cloud which had the only two server locations (melb and Sydney) and the mythology around cloud seemed a bit much
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AKA “we completely missed the boat on this thing and are going to pretend it was intentional by focusing on an inevitable inflection point a few years out from today instead.”
Can’t wait for the Apple announcement, “AI features require iPhone 18 or later. Your older phone CPU just isn’t powerful enough.”
Which wouldn’t be a problem if there was a cloud option.
iPhone 18 pro max actually) as they did already with iPhone 15 pro max and console games and it still overheating
Google is doing this exact same thing with Gemini, the platform behind Bard / Assistant.
Gemini has large scale models, that live in data centers, and handles complex queries. They also have a “Nano” version of the model that can live on a phone and handle simpler on-device tasks.
The smaller models are great for things like natural language UI and smart home controls. It’s also way faster and capable of working offline. A big use case for offline AI has been hiking with the Apple Watch in areas with no reception.
Also battery management, background tasks power distribution and hardware energy efficiency, i mean it would be great to have ai that adapted hardware energy consumption settings depending on my use case, yes i know that algorithms already exist to do that, but it would be great to have much much more flexible energy manager based on ai that accommodate and adapt to my use cases
How’s that supposed to work?
I’m picturing a backpack full of batteries and graphics cards. Maybe they’re talking about a more limited model?
This is a Financial Times article, regurgitated by Ars Technica. The article isn’t by a tech journalist, it’s by a business journalist, and their definition of “AI” is a lot looser than what you’re thinking of.
I’m pretty sure they’re talking about things that Apple is already doing not just on current hardware but even on hardware from a few years ago. For example the keyboard on iOS now uses pretty much the same technology as ChatGPT but scaled way way down to the point where “Tiny Language Model” would probably be more accurate. I wouldn’t be surprised if the training data is as small as ten megabytes, compared to half a terabyte for ChatGPT.
The model will learn that you say “Fuck Yeah!” to one person and “That is interesting, thanks for sharing it with me.” to someone else. Very cool technology - but it’s not AI. The keyboard really will suggest swear words now by the way - if you’ve used them previously in a similar context to the current one. The old algorithmic keyboard had hardcoded “do not swear, ever” logic.
Yes, like google is doing with their tensor chips in the pixels
Google is already doing this with Gemini Nano. https://store.google.com/intl/en/ideas/articles/pixel-feature-drop-december-2023
They’re making their own silicone now. You can achieve a lot more efficiency when you’re streamlined the whole way through.
It’s silicon. Silicon is a naturally occurring chemical element, whereas silicone is a synthetic substance.
Silicon is for computer chips, silicone is for boobies.
Remember, this probably isn’t an either or thing. Both Apple and Google have been offloading certain AI tasks to devices to speed up response time and process certain requests offline.
Yep, though Google is happy to process your data in the cloud constantly while Apple consistently tries to find ways to achieve it locally, which is generally better for privacy and security but also cheaper for them too.
Yea thats why they look trough your images for “cp”
Who looks at images?
Ok have to correct myself, they crawled back from this 2 weeks ago due to backlash, but i doubt they wont do it at least in a similar way or hidden like they did with reducing power on older devices to “save battery” https://www.wired.com/story/apple-photo-scanning-csam-communication-safety-messages/
I’m going to blow your mind here…the ‘cloud’ is just two or three data centres with replication turned on. It’s mostly a buzz word to charge a bit more
Charging more for cloud? As if apple is not finding an excuse to charge even more for their overpriced phones by going offline.
Eh, it’s a bit more than that. I work on a private cloud, the implications of it being a cloud versus traditional bare metal or virtualization platforms are around the APIs, quick spin up/down cycles, fully integrated recovery, imaging and remote console systems, integration with automated deployment platforms and others. It’s not just a buzz word.
Most of that’s on any half decent commercial server. You’re right there’s definitely some differences though.
I actually worked on our corporate move from private servers (main, backup and dr) to Azure cloud which had the only two server locations (melb and Sydney) and the mythology around cloud seemed a bit much