Making people feel unsafe is a great way to lose users, and people during the mid 2010s did notice advertisements that predicted what they wanted before they gave any indication that they want it, and they did react in an extraordinary measurable way to that (quitting the platform). Even the most mundane systems you could possibly expect would notice a dynamic like that and default to playing conservatively and reducing risk e.g. randomly selecting ads. People who use LW or are unusually strongly involved with AI in other ways probably stopped getting truly targeted ads years ago (or even people with more than statistics 101), because with sample sizes in the millions you can anticipate that kind of problem from a mile away.
Likewise, if a social media platform makes you feel safe, that is basically not an indicator at all at how effective gradient descent is at creating user experiences that prevent quitting.
Can you go into more detail about your chaotic dynamics point? Chaotic dynamics don’t seem to prevent clown attacks from succeeding at a sufficiently high rate; and the whole point of the multi-armed bandit algorithms I’ve described is that any manipulation technique will have a decent failure rate; that redundancy is why using social media for an hour a day is dangerous but looking at a computer screen one time is safe. These systems require massive amounts of data on massive numbers of people in order to work, and that is what they’re getting (although I’m not saying that a gait camera couldn’t make a ton of probabilistic inferences about a person from only 10 seconds of footage).
If I had read your comment before writing the post, I wouldn’t have used the word “zero days” nearly so frequently in this post, or even at all, because you’re absolutely right that the exploits I’ve described here are very squishy and unreliable in a way that is very different from how zero days are generally understood.
Making people feel unsafe is a great way to lose users, and people during the mid 2010s did notice advertisements that predicted what they wanted before they gave any indication that they want it, and they did react in an extraordinary measurable way to that (quitting the platform). Even the most mundane systems you could possibly expect would notice a dynamic like that and default to playing conservatively and reducing risk e.g. randomly selecting ads. People who use LW or are unusually strongly involved with AI in other ways probably stopped getting truly targeted ads years ago (or even people with more than statistics 101), because with sample sizes in the millions you can anticipate that kind of problem from a mile away.
Likewise, if a social media platform makes you feel safe, that is basically not an indicator at all at how effective gradient descent is at creating user experiences that prevent quitting.
Can you go into more detail about your chaotic dynamics point? Chaotic dynamics don’t seem to prevent clown attacks from succeeding at a sufficiently high rate; and the whole point of the multi-armed bandit algorithms I’ve described is that any manipulation technique will have a decent failure rate; that redundancy is why using social media for an hour a day is dangerous but looking at a computer screen one time is safe. These systems require massive amounts of data on massive numbers of people in order to work, and that is what they’re getting (although I’m not saying that a gait camera couldn’t make a ton of probabilistic inferences about a person from only 10 seconds of footage).
If I had read your comment before writing the post, I wouldn’t have used the word “zero days” nearly so frequently in this post, or even at all, because you’re absolutely right that the exploits I’ve described here are very squishy and unreliable in a way that is very different from how zero days are generally understood.
There was that story about the girl that got ads for baby stuff before her parents knew about the pregnancy… ;)