Note that you also want the "correctly" heard commands, because some of them will have been incorrect. It's frustrating when an assistant gives the "I don't know how to do that", but it's even more frustrating to get "OK, doing (the wrong thing)".
Also, another alternative: provide an actual bug reporting channel. "Hey Google, report that as a bug" "Would you like to attach a transcript of the recent interaction? Here's what the transcript looks like." "Yes."
I'm not sure if humans will then review that feedback of if it goes through a speech to text algorithm first but the mechanism for feedback is there.
I tried to voice my way back to directions, unsuccessfully. I said "Fuck you Google."
"I see that you're upset," followed by some instructions on how to give feedback. While I was driving. It sounded almost exactly like "I'm sorry Dave, I can't help you."
While I like how Ubuntu does it, I actually like better how Fedora does it. Not only do they ask to submit core dumps but gives you the ability to annotate and inspect what gets sent as well as gives you a bug report ID which you can use to follow up on.
This not only happens, it's my job (though I'm not a doctor). Of course, it's tightly controlled on my end. I work for the government, but health systems have their own analysts. As part of my job, I have access to sensitive and identifying information.
This isn't to be contrairian. There are existing systems using very personal data in bulk for analysis. The wheel doesn't need reinvented.
My car has a little blurb that explains that they collect data to use for training and gives me the choice to participate or not. Opting out doesn’t affect any functionality. Why can’t Google do the same thing?
I can just as easily say that the best way to maximize shareholder value is to minimize public scandal, scrutiny, and potential for legislature.
Nearly every single decision, including contradictory ones, made by every single company, everywhere, can be retroactively justified to have been done in the name of shareholder value.
Scandals can get free marketing, for example, Nike and Colin Kaepernick. Attention is always better than no attention at all for a business. Every single decision is made to increase profit but there might be many things that need to be accomplished first so its hard to see the big picture. For example, a developer might want to improve a feature because they want more people to use their product. A manager gets approval to pay that developer because the investment is deemed a profitable one. What does the person who gave them that money care about the number of users. It's not their invention and they don't even use the service? They give the money because they know that More users = more market share = more ads to sell = a return greater than the initial investment. Until a business can run with people working for free, the person paying for things always dictates what is bought and thus the direction the company is headed.
Let's say that direction is contrary to the direction of another prominent member of the business wants it to go. Whether you want to believe it or not, the same calculus goes on in every person's mind: Is this the potential payoff of Option A greater than the potential loss of Option B given the risk?
This is the business equivalent of saying “because physics.” It’s not wrong, it’s just not useful.
Google has many primary concerns it needs to manage. That’s how you get big - by managing lots of concerns successfully.
If they drop one too long, they start going backwards very quickly.
Alphabet is still spending billions from Google into "other bets" like Loon, so I don't see how this explains the change.
The difference between 3 and 4, noble as it is, can be caused by feasability concerns that push people into 3, not just ignorance of the privacy impact. Human labelling of training data sets is a big thing in supervised learning. Methods that dispense with this would be valuable for purely economic reasons beyond privacy - the cost of human labelling of data samples. Yet we don't have them!
Techniques like federated learning or differential privacy can train models on opaque (encrypted or unavailable) data. This is nice, but they assume too much: that the data is already validated and analyzed. In real life modelling problems, one starts with an exploratory data analysis, the first step being looking at data samples. Opaque encrypted datasets also stop ML engineers from doing error analysis (look at your errors to better target model/dataset improvements) which is an even bigger issue, IMO, as error analysis is crucial when iterating on a model.
Even for an already productivized model, one has to do maintenance work like checking for concept drift, which I can't see how to do on an opaque dataset.