I agree that the vast majority of people attempting to do targeting advertising do not have sufficient data. But that doesn’t tell us much about whether the big 5 tech companies, or intelligence agencies, have sufficient data to do that, and aren’t being really loud about it.
If any of the big tech companies had the capability for actually-good targeted advertising, they’d use it. The profit motive would be very strong. The fact that targeted ads still “miss” so frequently is strong evidence that nobody has the highly advanced, scalable, personalized manipulation capabilities you describe.
Social media recommendation algorithms aren’t very powerful either. For instance, when I visit YouTube, it’s not unusual for it to completely fail to recommend anything I’m interested in watching. The algorithm doesn’t even seem to have figured out that I’ve never played Skyrim or that I’m not Christian. In the scenario in which social media companies have powerful manipulation capabilities that they hide from the public, the gap between the companies’ public-facing and hidden recommendation systems would be implausibly large.
As for chaotic dynamics, there’s strong experimentalevidence that they occur in the brain, and even if they didn’t, they would still occur in people’s surrounding environments. Even if it weren’t prohibitively expensive to point millions or billions of sensors at one person, that still wouldn’t be enough to predict everything. But tech companies and security agencies don’t have millions or billions of sensors pointed at each person. Compared to the entirety of what a person experiences and thinks, computer use patterns are a very sparse signal even for the most terminally online segment of the population (let alone your very offline grandma). Hence the YouTube algorithm flubbing something as basic as my religion—there’s just too much relevant information they don’t have access to.
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.
If any of the big tech companies had the capability for actually-good targeted advertising, they’d use it. The profit motive would be very strong. The fact that targeted ads still “miss” so frequently is strong evidence that nobody has the highly advanced, scalable, personalized manipulation capabilities you describe.
Social media recommendation algorithms aren’t very powerful either. For instance, when I visit YouTube, it’s not unusual for it to completely fail to recommend anything I’m interested in watching. The algorithm doesn’t even seem to have figured out that I’ve never played Skyrim or that I’m not Christian. In the scenario in which social media companies have powerful manipulation capabilities that they hide from the public, the gap between the companies’ public-facing and hidden recommendation systems would be implausibly large.
As for chaotic dynamics, there’s strong experimental evidence that they occur in the brain, and even if they didn’t, they would still occur in people’s surrounding environments. Even if it weren’t prohibitively expensive to point millions or billions of sensors at one person, that still wouldn’t be enough to predict everything. But tech companies and security agencies don’t have millions or billions of sensors pointed at each person. Compared to the entirety of what a person experiences and thinks, computer use patterns are a very sparse signal even for the most terminally online segment of the population (let alone your very offline grandma). Hence the YouTube algorithm flubbing something as basic as my religion—there’s just too much relevant information they don’t have access to.
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… ;)