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Comment by sliken | Hacker News Reader

What kinds of "AI" is likely to be viable to run on a snapdragon 835+682?

Recognizing faces? Voice? Handwriting? Captions for photos? Natural Language queries (like google's AI assistant)? Positioning by recognizing landmarks? Simple autonmous driving (say RC cars)? Flying (quad rotors or rc planes)? Cars?

Or I guess a better question... will this change anything except decrease your need for a good network?

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Being able to do all these works on a cell phone, with no network, should be a big gain.

Cell phones were already powerful enough for most of this to begin with. Face recognition? Windows 98 on a 180MHz Evergreen Overclocked processor and 48MB RAM did that just fine. Voice? Ditto. Handwriting recognition? Palm Pilots with far less power could do it.

I think nobody's bothered to code that stuff in, because, well, despite trying time and time again to make these hot-shit features for a couple of decades, these features end up unused. Those that pay attention to history see this, and figure "Probably not worth trying, even in this day and age."

Maybe they are unused because the implementation was poor. And the implementation was poor because a Pentium 2 processor is shit and deep learning has only been practical in the last couple of years.

More specifically, I had a Palm Pilot, and you had to write using weird letter shapes for it to work.

The implementation worked great. You walked up to your computer, turned it on, and looked at the camera. Bam! You were logged in, assuming you had enough proper light on your face for the software to make out your facial shape.

It was just bloody annoying because you had to wait about 15 seconds for it to figure everything out. It was far faster to just use the keyboard.

High accuracy voice recognition done fully on the mobile client, instead of on the cloud, would be pretty big, and useful. I think we'll get there in 1-2 years

We had that with Dragon Naturally Speaking and had it working on Pentium-4 Class hardware.

I remember Dragon Naturally Speaking. You had to do a lot of training to your specific voice to get it to a level of accuracy I would describe as "not great." You'd have to slow down your speaking to get anything I'd consider acceptable output out of it. Modern systems are much better.

HMM based speech recognition works ok locally today (and has worked for a while as you say) but there is a large difference in word error rate, handling noisy environments etc when compared to SOTA recurrent neural network models. Those don't (yet) run realtime locally on mobile hardware, but we are not far off.

The difference between 8x% and 97% accurate speech recognition in terms of user experience is pretty drastic

> Windows 98 on a 180MHz Evergreen Overclocked processor and 48MB RAM did that just fine.

No it didn't

http://www.prnewswire.com/news-releases/visionics-introduces...

     Systems Requirements for FaceIt PC
     -- Microsoft Windows 95.
     -- 90 MHz Intel Pentium compatible or higher.
     -- 16 MB RAM/10 MB free hard disc space.
     -- VGA or higher video display adapter.
     -- CD-ROM drive.
     -- Microsoft Video for Windows (VFW) compatible video capture system with
        resolution 320 x 240, depth: 15 bit RGB, capture rate to memory:
        5 frames/sec.

there is a big difference in recognition accuracy between that and current state of the art systems. This isnt just a bit of incremental improvement. Todays systems are way better, but they also require a bit more processing.

Ahh well how can we forget FaceIt: the cutting edge of facial recognition. 20 years behind the curve but at least it can run on Windows 95, so that proves your point? Damn these kids with their bloated software, we got terrible results using rubbish software with 16mb of ram processing at 5 FPS and so should they!

/sarcasm

Looks like most of the use cases you mentioned could benefit from this? About "good network", today's wireless network's peak performance might be enough, but being mobile, you can't guarantee you always have that peak performance. Having a dedicated high performance SoC definitely helps those use cases especially under mobile.

Improved privacy, one of the big things for apple is the ability to do all of their machine learning offline, on the device, rather than online.

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