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>the company argues that 85 percent of smartphones today run machine learning workloads with only a CPU or a CPU+GPU combo. Gotta admit I'm not real clear on w

>the company argues that 85 percent of smartphones today run machine learning workloads with only a CPU or a CPU+GPU combo. Gotta admit I'm not real clear on what my phone does that needs on-device machine learning.

PhilippGille 7y ago
Automatic enhancements when taking a photo / video (digital zoom, anti shake, "night mode") for example.
- mceachen 7y ago
  Applying an existing, trained model (like what you described) is computationally cheap. Training new models takes many, many orders of magnitude more time and memory space to complete.
  - jononor 7y ago
    Much cheaper than training for sure. But inference for image classification it is generally in the order of hundreds of milliseconds when using CPU. That wont fly for real-time inference on video, for example. Which is very desirable for even "image" uses, because it allows for live preview in the camera app.
bufferoverflow 7y ago
2019 Google I/O presenters talked about it: much much better latency, no need to send the audio to the server for speech recognition.
- sixothree 7y ago
  Besides that data is no longer useful to them.
bsaul 7y ago
Was about to write the same comment. Plus, i must say every time i hear some function is going to be performed via a NN, i'm thinking that my device is becoming more and more random in the way its behave. Which i really don't like. At least when some classical algorithm fails at performing certain task, people talk of the failure as a bug. Not as something that's "probably going to be solved with more training data".
michaelt 7y ago
So it turns out the hardware needed to run a pretrained model is pretty much the same as the hardware needed to train a model. In both cases, it means lots of matrix multiplication. Of course, training a model takes longer given the same amount of processing power - but for applications like video processing, just applying the model can be pretty demanding.
cheerlessbog 7y ago
I assume they mean inference. Voice assistant or photo facial recognition without network access?
anonuser123456 7y ago
Inference, not training.
TazeTSchnitzel 7y ago
I assume it's what Apple uses to automatically categorise photos by “moment” and recognised face.
antpls 7y ago
If you need actual examples running right now : Recent Snapchat and Instagram selfie filters Google Keyboard's translation and prediction Google Traduction's lens Google Assistant's voice recognition Google Message's instant replies Almost all of them are inference workloads. I believe only Google Keyboard does on-device training in the background when the phone is charging.
- vitorgrs 7y ago
  A few more: - Photos and a few other things on iOS, are all local. Same thing with Windows 10 (Photos app on Windows, Keyboard suggestion too I believe) - Microsoft Edge pages suggestion (really, it's a onnx model) - OCR Text Recognition on Windows
iamnothere 7y ago
Determining whether or not nearby ambient sounds indicate potentially illegal activity. Or, perhaps, mapping the content and emotional valence of conversations to understand whether or not you deserve a lower social credit score.

PhilippGille 7y ago

Automatic enhancements when taking a photo / video (digital zoom, anti shake, "night mode") for example.

mceachen 7y ago
Applying an existing, trained model (like what you described) is computationally cheap. Training new models takes many, many orders of magnitude more time and memory space to complete.
- jononor 7y ago
  Much cheaper than training for sure. But inference for image classification it is generally in the order of hundreds of milliseconds when using CPU. That wont fly for real-time inference on video, for example. Which is very desirable for even "image" uses, because it allows for live preview in the camera app.

bufferoverflow 7y ago

2019 Google I/O presenters talked about it: much much better latency, no need to send the audio to the server for speech recognition.

sixothree 7y ago
Besides that data is no longer useful to them.

bsaul 7y ago

Was about to write the same comment. Plus, i must say every time i hear some function is going to be performed via a NN, i'm thinking that my device is becoming more and more random in the way its behave. Which i really don't like. At least when some classical algorithm fails at performing certain task, people talk of the failure as a bug. Not as something that's "probably going to be solved with more training data".

michaelt 7y ago

So it turns out the hardware needed to run a pretrained model is pretty much the same as the hardware needed to train a model. In both cases, it means lots of matrix multiplication. Of course, training a model takes longer given the same amount of processing power - but for applications like video processing, just applying the model can be pretty demanding.

cheerlessbog 7y ago

I assume they mean inference. Voice assistant or photo facial recognition without network access?

anonuser123456 7y ago

Inference, not training.

TazeTSchnitzel 7y ago

I assume it's what Apple uses to automatically categorise photos by “moment” and recognised face.

antpls 7y ago

If you need actual examples running right now : Recent Snapchat and Instagram selfie filters Google Keyboard's translation and prediction Google Traduction's lens Google Assistant's voice recognition Google Message's instant replies Almost all of them are inference workloads. I believe only Google Keyboard does on-device training in the background when the phone is charging.

vitorgrs 7y ago
A few more: - Photos and a few other things on iOS, are all local. Same thing with Windows 10 (Photos app on Windows, Keyboard suggestion too I believe) - Microsoft Edge pages suggestion (really, it's a onnx model) - OCR Text Recognition on Windows

iamnothere 7y ago

Determining whether or not nearby ambient sounds indicate potentially illegal activity. Or, perhaps, mapping the content and emotional valence of conversations to understand whether or not you deserve a lower social credit score.