5 ms·
It's going to be resource intensive, aren't your users going to hate you for draining their battery?
by ndr 9y ago
It's going to be resource intensive, aren't your users going to hate you for draining their battery?
- tdb7893 9y agoIf you found a way to have efficient distributed training you could just have each device do a few training runs and the user probably wouldn't even notice.
- IanCal 9y agoYou may be interested in federated learning: https://research.googleblog.com/2017/04/federated-learning-collaborative.html https://research.googleblog.com/2017/04/federated-learning-c...
- danieldisu 9y agothey show an example of ML when selecting a text in android and how it predicts how much text should it select depending on what it is (a full address, an email etc).. don't know how they made it but it was almost instant, I'm sure the users wont care.
- ndr 9y agoI think that was just using a pre-trained model, i.e. the training didn't happen on the device but offline.
- goda90 9y agoIt'd be interesting to compare the power drain of running the model vs transmitting the data to a server, waiting for the response, and then acting on it. Even if it's still worse, it might be worth the responsiveness and reduction of data usage.
- Klathmon 9y agoNot to mention that training can be done somewhat "out of band". So you can have your phone train on data while charging overnight to get better predictions the next day.
- mtgx 9y agoIt will use accelerators, which should help a bit. You shouldn't use this on the phone's GPU.
- quadrature 9y agoSimple models should run performantly, we're all thinking of CNNs and RNNs, but a simple Logistic Regression will probably the most ubiquitous application.