While this post seems aimed more at compliance or sensitive data issues, “unlearning” aka forgetting itself may be essential for better or more human like AI agents. You are as much defined by what you have forgotten as what you have learned.
Gradient descent obviously has none of that, it has not feelings or goals, and so there would be no preferential remembering or forgetting other than to do better next word prediction or rlhf or whatever. So it would be interesting to think about what a model should remember or forget in order to align it with our goals (because it doesn't have any of it's own).
Also, don't volunteer for Google. They have lots of money, they can pay for this stuff and if you know how to do it you have lots of options.
My totally amateur pet theory is that paranoia is threat pattern recognition gone bonkers.
Actually, most of what we do with brains is pattern recognition, and if there isn't enough good input they will make shit up.
Supposedly OCD is your brain’s cause-effect loop being too potent. As in, you have a random fear I.e. “stove is on, fire will burn down house,” you go to check the stove, and the act of checking (regardless of its being on) creates the feeling that you saved your house from burning down, so now you feel compelled to check every time. Paraphrased from the Huberman podcast.
He was also quite unassuming and very "normal". No trace of mental dysbalance.