Also, just imagine being the group at Apple responsible for designing this section of the chip, starting probably almost a decade back – under the constant uncertainty of not knowing what direction ML workloads would develop in…
ML research was a rather known quantity, or the separate "Neural Engine" CPU explicitly aimed at existing ML pipelines wouldn't exist.
No, not really. Transformers were just one of the possible directions. Silicon design does not have the same time scale than software. Now, everyone is using transformers so it becomes harder to do anything else, and it’s been the case long enough that hardware had some time to align (but is still lagging). But who’s to say that a different architecture published last year won’t take the world by storm 2 years from now?
It’s easy to say it in hindsight, but transformers took a bit of effort to get where they are now.
Silicon design does not have the same time scale than software.
"Neural Engine" has been a part of iPhones since 2017. So, in development since at least 2013, possibly earlier. And it targeted the rather well established, known, and widely used ML practices.
GPT-like models didn't become even remotely useful until at least 5 years later.
I'm all for compassion, but engineers knew the NE was empty when it sat idle for 10 years on our computers.
- when you're given no usecase for your engineering piece, apart from "detour characters in pictures". It's an exageration but AI's contributions in iOS aren't visible; Meanwhile Google has features that people actually notice like removing tourists from your holidays photos — worse: it's mostly a simple collage feature working on the main CPU, and it has the same social effect as green bubbles in iMessage ("ah. Tourists on your photos. iPhone user?")
- and you tout it as "16 Neural Engine cores" during the sales, with no associated software, no listed material feature, just hand-waving,
- Siri maxxes out at "There is no contact named 'What's the weather today' in your agenda",
Then can't really claim that Apple engineers' problem was really the bad luck that ML wasn't the determining part of the future. It's more like misreading the room for 5 to 10 years straight.
Apple engineering's excellence on vertical integration and supply chain control gave them absolute power over our world (with merit), it just failed at that particular project. Which occupies 40% of our CPUs.
an exageration but AI's contributions in iOS aren't visible
If you want to ignore them, that’s right. In the real world, they’ve been talking about ML and how it’s making pictures or such-and-such aspect of the OS better for about a decade now. It might not be flashy, but it is used throughout the OS.
Meanwhile Google has features that people actually notice like removing tourists from your holidays photos — worse: it's mostly a simple collage feature working on the main CPU, and it has the same social effect as green bubbles in iMessage ("ah. Tourists on your photos. iPhone user?")
The feature to do this has been in the Photos application for years, what are you talking about?
Comments
Same for me!
Also, just imagine being the group at Apple responsible for designing this section of the chip, starting probably almost a decade back – under the constant uncertainty of not knowing what direction ML workloads would develop in…
ML research was a rather known quantity, or the separate "Neural Engine" CPU explicitly aimed at existing ML pipelines wouldn't exist.
However, very few used it for anything, even within Apple. I feel like it was a huge wasted opportunity.
No, not really. Transformers were just one of the possible directions. Silicon design does not have the same time scale than software. Now, everyone is using transformers so it becomes harder to do anything else, and it’s been the case long enough that hardware had some time to align (but is still lagging). But who’s to say that a different architecture published last year won’t take the world by storm 2 years from now?
It’s easy to say it in hindsight, but transformers took a bit of effort to get where they are now.
"Neural Engine" has been a part of iPhones since 2017. So, in development since at least 2013, possibly earlier. And it targeted the rather well established, known, and widely used ML practices.
GPT-like models didn't become even remotely useful until at least 5 years later.
I'm all for compassion, but engineers knew the NE was empty when it sat idle for 10 years on our computers.
- when you're given no usecase for your engineering piece, apart from "detour characters in pictures". It's an exageration but AI's contributions in iOS aren't visible; Meanwhile Google has features that people actually notice like removing tourists from your holidays photos — worse: it's mostly a simple collage feature working on the main CPU, and it has the same social effect as green bubbles in iMessage ("ah. Tourists on your photos. iPhone user?")
- and you tout it as "16 Neural Engine cores" during the sales, with no associated software, no listed material feature, just hand-waving,
- Siri maxxes out at "There is no contact named 'What's the weather today' in your agenda",
Then can't really claim that Apple engineers' problem was really the bad luck that ML wasn't the determining part of the future. It's more like misreading the room for 5 to 10 years straight.
Apple engineering's excellence on vertical integration and supply chain control gave them absolute power over our world (with merit), it just failed at that particular project. Which occupies 40% of our CPUs.
Some features using the Neural Engine:
* Face ID since the iPhone X released in 2017
* fall and crash detection
* Live captions in videos, calls, and spoken audio
* facial recognition in the Photos app
* voice isolation in calls
There's more, but I'll stop there.
Dictation, text to speech, some of that computational photography, etc too!
The ANE hasn't been sitting empty for 10 years. All those Photos features like face recognition and auto classification run on ANE.
If you want to ignore them, that’s right. In the real world, they’ve been talking about ML and how it’s making pictures or such-and-such aspect of the OS better for about a decade now. It might not be flashy, but it is used throughout the OS.
The feature to do this has been in the Photos application for years, what are you talking about?