I think you're overlooking a lot with that aside. It's just one example but with tensorflow.js you can run your model on a video feed in the browser, frame-by-frame, in a way that wouldn't just increase server costs but would hobble performance as well.

Any company that attempts to do targeting/advertising based on the user's video camera feed without explicit consent will be shamed/sued out of existence, even if there technically isn't a privacy leak.

It seems I made my comment before I saw your edit, that you were writing about only advertising and targeting. I'm not sure what your definition of consent is, regarding video streaming on the web, but it seems to me that every implementation of the getUserMedia API on modern browsers asks the user for permission to access the device.

So yes, anyone surreptitiously gaining access to your camera or mic feed through the browser would get into heaps of trouble and invite bad press, especially if they were able to find a way around the consent dialog.

Some benefits: for the user - the data stays private as all processing is done on device. You can get much faster inference times and create applications with real-time interactions. The applications can work offline once the models are downloaded. The browser comes with tons of sensors (microphone/camera/accelerometer/etc) and the data from these sensors can be fed into the models in real time without needing to do a round trip to a server. Yes if the model is proprietary it maybe wouldn’t make sense to have the user download the weights as it could be reverse engineered, but for open source models it’s not an issue. And anyways the value these days is in the dataset and training of the models. Check out this great article on edge computing: https://techcrunch.com/2017/08/03/edge-computing-could-push-...

Those are benefits for TensorFlow.js, sure, but none of those help tracking/advertising use cases.