It's not that simple because that way you lose too much information. Actually it is more likely the whole system would fail if weights at any layer are reset.
The idea was developed mostly for transfer learning as in learn new stuff on a new domain but do not forget the old stuff as well. For forgetting it could be trained on some old images + all zeros target mask and the MAS to preserve everything else.
Both MAS and (the earlier) EWC facilitate continual learning through passing a bunch of samples through the network and collecting gradients to determine which weights are 'important'. Future weight changes are then regularised by these importance values so that the network retains its ability on past tasks. EWC uses square gradients as importance values, whereas MAS uses absolute gradients... Other than that they're the same lol (I think), how the MAS paper got so many citations I have no idea.
State of the art models can take over a month to train. Ideally you'd have a way to cull out information related to a given input without dramatically altering the model (for example, whenever one of your million users asks to be removed). It'd be very interesting to see how this would be possible. Maybe analyze which nodes contribute the most to an output for that sensitive data? But even then, how do you change them without compromising the model?
As a layman, I'd intuitively want to try to solve this problem with an adversarial approach, i.e. train an adversary network to predict the info we want hidden better than chance given oracle access to the system we're trying to secure. But as I understand it GANs are currently regarded as flaky because training them requires the two component networks' learning be fairly matched. I wish I had a better understanding of the "competitors"/"successors" to GANs and how they would work on this sort of problem.
I think a program synthesis neural network trained on common imperative languages like C will "unlearn" once trained on derivatives of Forth language or say, Lisp
They so need to apply this to Google Maps, in India google maps has gone crazy since past 2 years especially in Mumbai. It seems to give all bad routes always and also all gig workers in Uber, Ola, Swiggy, Zomato are affected by this too.
https://en.wikipedia.org/wiki/Planck%27s_principle "Science progresses one funeral at a time" basically says that people, even very smart people, can't unlearn, so the only way humanity as a whole can unlearn is via people dying off. Maybe it's a problem we should give more attention before tackling aging.
You seem to be assuming that people (as a whole) actually learn from the past and their mistakes. You know the saying, though: "history never repeats itself, but it does often rhyme." We just keep making a lot of the same mistakes as a species, over and over again. :/
I used to be pretty decent in high school math, chemistry and physics... Now I only vaguely can recall that stuff. Show me some problem or something and I might not be sure what to apply or where to start anymore.
It's an old concept...
And yes if people would unlearn, their machines would follow.
Some way or the other.
The question is, wether someone gives a shit and bothers to build it. Or better, is payed to build it in such a way.