(I worked on this)
I don't see any mention in the article of what algorithms Apple is using, or a link to the code. In the area of privacy, the code should really be open source, for obvious reasons.
which has some nice slides and discussion on the "reusable holdout" ("thresholdout") which is a technique to allow one to use all of the training data to fit a lot of models, but also offers guarantees against overfitting.
Ironic, considering I'm trying to read an article about privacy, and the entire reason I use an adblocker is to try and protect that privacy.
Built into uBlock Origin with a checkbox to enable it in the settings.
I actually only use adblockers on my tablet, since I have misgivings about the effect of the end of web advertising on the availability of sites to poor people (since ads effectively act as a redistribution mechanism), but I don't blame anyone for not being willing to subject themselves to that crap.
Seriously, at the end of the day, why do you feel entitled to their content, instead of simply not going to their site?
The new standard was Doctor_Fegg's idea that people should announce in their user-agent that they are using an adblocker. In any case, nothing is being "taken". The entitlement comes from where it has always come from - one makes a request, they reply with whatever they want to serve. Payment is only a moral obligation when we have agreed to do so, otherwise we'd have to pay for every musician playing on the street, regardless of whether we enjoy their content or not. People trying to push moral obligations to prop up their flawed business models don't get my sympathy.
You still haven't explained why you're entitled to their content for free. How do you expect them to actually pay their bills? And if you don't care about that, then why are you reading their content?
There is probably some specifics I'm missing though, and it raises the question about how they identify others faces. I've not used the feature so I can't say. I'm guessing it's a manual tagging when the device can't find a match and then on device processing afterwards once the algorithm has learned.
The data set to learn from probably can be gathered from stock photos or other open source images, but can that learning be saved down to phone and used to compare against, or would it be too big?
If too big, does that mean characteristics of an image would be computed on the phone sent to the cloud compared and the results sent back?
So they're really saying that no data leaves the device for either object or face recognition at classification time. Now, whether they are using iCloud-stored photos (which the user has already agreed to share) for training, I don't know, and any privacy issues would still be important at that point.
You can certainly argue that this is "exploiting" the user's data, but any computer program whose purpose is to summarize or make inferences from a provided data set is, by definition, exploiting provided data, right? Privacy questions mostly revolve around consent and tracking. Is it potentially a privacy concern for Google or Apple to deduce that the email that I have from American Airlines and Hilton relates to travel plans, add that trip information to my calendar, add the airline ticket to my phone's wallet, let me know about transit options to and from the airport (possibly calling an Uber for me), and so on? Sure. But it's also really useful.
That Apple is trying to be able to provide ever more Google-esque assistance without leaving a whole bunch of trackable and user-identifiable information on their servers, instead keeping as much as possible on my personal devices, is something I find laudable, not overly concerning. My biggest question is more whether or not they're actually going to be able to make it work as well as Google does -- or at least close enouugh to Google's quality level that it's worth using.