3 ms·

theyre going to push "AI on the edge" and "IoT" nonsense again absolutely unbelievably cooked. anyone pushing that nonsense, short with leverage. low latency

theyre going to push "AI on the edge" and "IoT" nonsense again absolutely unbelievably cooked. anyone pushing that nonsense, short with leverage. low latency connectivity + goliath data centres will always beat on-device inference/training.

ale42 1y ago
> low latency connectivity + goliath data centres will always beat on-device inference/training. Except that it's not always an option...
KeplerBoy 1y ago
privacy is a thing people care about.
- webdevver 1y ago
  beyond delusional. imagine unironically saying this in the big '25.
- ChrisMarshallNY 1y ago
  > privacy is a thing people care about. Sadly, it seems that privacy is something that HN readers care about, but precious few others. Look at the success of Facebook. The covers have been off that stinker for years, yet people still regularly use it; often to the exclusion of more traditional media. I have quite a few friends that I don't get invited to their occasions, because they only advertise them on FB. They invite a bunch of randos they've never met, but not those of us, they see all the time. To be fair, if I sit down, and describe exactly what the ramifications of the "always on, always open" Facebook presence means, people will usually suddenly value privacy, but it seems that no one actually ever does that, at a level most folks can understand. Hysterical rantings (even when well-founded), by geeks, don't get through to most folks. It needs to be done in the vernacular, and via media they actually consume.
  - dboreham 1y ago
    There's a commercial version of privacy. E.g. Company A doesn't want to send their data to Company B (a competitor) for processing.
KaiserPro 1y ago
Low latency, low power, portable pick two. well actually you can't really, low latency is pretty hard to do full stop
Teknomadix 1y ago
Not necessarily. There are lots of use cases for on device AI inference. I run YOLO on an Nvidia Jetson powered Lenovo Think Edge, which processes incoming video at full frame rates on four channels with recognition and classification for a bespoke premises security system. No clouds involved other than the Nix package manager etc. Make sure your argument May carry more weight when you're talking about ultra low power devices like an Arduino running AI inference locally that seems like more of a stretch.
- webdevver 1y ago
  true, true, very true, but i observe you use a nvidia chip. which is perfectly logical. why would you use something that is worse in every single way, right? which is exactly what qcom offerings are...
  - cosmicgadget 1y ago
    Is power a single way?
  - Teknomadix 1y ago
    Yolo has actually been shown to be run on 15-year-old Canon cameras. They in no way have the power of my Nvidia Jetson. But they can run the model just the same. No cloud necessary. These small devices, well maybe not being able to process four individual video channels at 60 frames per second will still have the capability of processing at lower frame rates and with less channels. Using much cheaper hardware, and using a whole lot last energy. So I don't think you're argument really holds up.
westpfelia 1y ago
SOOOO buy Qualcomm. The second they start talking about AI-IOT stock is gonna sky rocket. We live in a broken world.
vachina 1y ago
> "AI on the edge" and "IoT" nonsense again. I love it when my device stays dumb (or at least connect-local) and not become abadonware 6 months after release because the cloud provider felt it a chore to keep running.
healthymomo 1y ago
tf are you on. just look at meta display glasses. it s all on board compute
- webdevver 1y ago
  its cool... but thats not gonna last long at all. soon theyre gonna put their own custom soc into it, just like google did. especially for such a specific, space/power/thermal constrained platform. itd be weird if meta didnt put their own custom soc into it. running a big tech company these days, theres enough custom work going around that basically all the big players have internal silicon teams. hell, even fintech shops with ~100 employees are doing tape-outs these days!
deleted 1y ago
[deleted]
leoedin 1y ago
> low latency connectivity That's not exactly easy. I doubt on-device training will become much of a thing. But on-device inference is desirable in all sorts of distributed use cases. We're still a long way off from reliable internet everywhere. Especially when you want to start pushing large quantities of sensor data down the pipe. I can't even get reliable internet on my phone in the centre of London.
cosmicgadget 1y ago
Realtime and offline would like a word.

ale42 1y ago

> low latency connectivity + goliath data centres will always beat on-device inference/training. Except that it's not always an option...

KeplerBoy 1y ago

privacy is a thing people care about.

webdevver 1y ago
beyond delusional. imagine unironically saying this in the big '25.
ChrisMarshallNY 1y ago
> privacy is a thing people care about. Sadly, it seems that privacy is something that HN readers care about, but precious few others. Look at the success of Facebook. The covers have been off that stinker for years, yet people still regularly use it; often to the exclusion of more traditional media. I have quite a few friends that I don't get invited to their occasions, because they only advertise them on FB. They invite a bunch of randos they've never met, but not those of us, they see all the time. To be fair, if I sit down, and describe exactly what the ramifications of the "always on, always open" Facebook presence means, people will usually suddenly value privacy, but it seems that no one actually ever does that, at a level most folks can understand. Hysterical rantings (even when well-founded), by geeks, don't get through to most folks. It needs to be done in the vernacular, and via media they actually consume.
- dboreham 1y ago
  There's a commercial version of privacy. E.g. Company A doesn't want to send their data to Company B (a competitor) for processing.

KaiserPro 1y ago

Low latency, low power, portable pick two. well actually you can't really, low latency is pretty hard to do full stop

Teknomadix 1y ago

Not necessarily. There are lots of use cases for on device AI inference. I run YOLO on an Nvidia Jetson powered Lenovo Think Edge, which processes incoming video at full frame rates on four channels with recognition and classification for a bespoke premises security system. No clouds involved other than the Nix package manager etc. Make sure your argument May carry more weight when you're talking about ultra low power devices like an Arduino running AI inference locally that seems like more of a stretch.

webdevver 1y ago
true, true, very true, but i observe you use a nvidia chip. which is perfectly logical. why would you use something that is worse in every single way, right? which is exactly what qcom offerings are...
- cosmicgadget 1y ago
  Is power a single way?
- Teknomadix 1y ago
  Yolo has actually been shown to be run on 15-year-old Canon cameras. They in no way have the power of my Nvidia Jetson. But they can run the model just the same. No cloud necessary. These small devices, well maybe not being able to process four individual video channels at 60 frames per second will still have the capability of processing at lower frame rates and with less channels. Using much cheaper hardware, and using a whole lot last energy. So I don't think you're argument really holds up.

westpfelia 1y ago

SOOOO buy Qualcomm. The second they start talking about AI-IOT stock is gonna sky rocket. We live in a broken world.

vachina 1y ago

> "AI on the edge" and "IoT" nonsense again. I love it when my device stays dumb (or at least connect-local) and not become abadonware 6 months after release because the cloud provider felt it a chore to keep running.

healthymomo 1y ago

tf are you on. just look at meta display glasses. it s all on board compute

webdevver 1y ago
its cool... but thats not gonna last long at all. soon theyre gonna put their own custom soc into it, just like google did. especially for such a specific, space/power/thermal constrained platform. itd be weird if meta didnt put their own custom soc into it. running a big tech company these days, theres enough custom work going around that basically all the big players have internal silicon teams. hell, even fintech shops with ~100 employees are doing tape-outs these days!

deleted 1y ago

[deleted]

leoedin 1y ago

> low latency connectivity That's not exactly easy. I doubt on-device training will become much of a thing. But on-device inference is desirable in all sorts of distributed use cases. We're still a long way off from reliable internet everywhere. Especially when you want to start pushing large quantities of sensor data down the pipe. I can't even get reliable internet on my phone in the centre of London.

cosmicgadget 1y ago

Realtime and offline would like a word.