Just running some data through the resulting model is still somewhat expensive since they have so many parameters. And of course for a lot of things, you want to train the model on new data you're putting through it anyways.
In their defense, I'm sure there are tons of actually useful machine learning models that don't use that much power once trained.
I have an iPhone with Face ID and I think the way they did that was to train a model on lots of people's faces, and they just ship that expensive-to-train model with the operating system and then it trains a little bit more when you use face ID. I can't imagine it uses that much power since you're running the algorithm every time you open the phone.
I'm sure any model worth anything probably does require a lot of training and energy usage. I guess it really depends on the eventual utility whether it's worth it.
Just running some data through the resulting model is still somewhat expensive since they have so many parameters. And of course for a lot of things, you want to train the model on new data you're putting through it anyways.
The proselytizer treated it as a gotcha, so I appreciate the additional information.
In their defense, I'm sure there are tons of actually useful machine learning models that don't use that much power once trained.
I have an iPhone with Face ID and I think the way they did that was to train a model on lots of people's faces, and they just ship that expensive-to-train model with the operating system and then it trains a little bit more when you use face ID. I can't imagine it uses that much power since you're running the algorithm every time you open the phone.
I'm sure any model worth anything probably does require a lot of training and energy usage. I guess it really depends on the eventual utility whether it's worth it.