Studies have shown we typically use pattern matching for our choices but not statistics. One such experiment had humans view to light bulbs (I think one was red one was green). One light would turn on at a time and they were allowed or given a record of what had happened. Then they were asked to guess what would occur next for n number of steps. Same thing is done with rats. Humans are rewarded with money based on correct choices and rats with food. Here is the thing, one light (let’s say red) would light up with 70% probability and the other with 30%. But it was randomized.
The optimal solution is to always pick red. Every time. But humans pick a pattern. Rats pick red. Humans consistently do worse than rats. So while we are using a form of updating, it certainly isn’t proper bayesian updating. And just because you think we function some way doesn’t make it true. And it will forever be difficult to describe any AI as conscious, because we have really arbitrarily defined it to fit us. But we can’t truly say what it is. Not can we can why we function how we do. Or if we are all in a simulation or just a Boltzmann brain.
Honestly, something that concerns me most about AI is that it could become sentient, but we will not know if it is or just cleverly programmed so we treat it only as a tool. Because while I don’t think AI is inherently dangerous, I think becoming a slave owner of something that could be much more powerful probably is. And given their lack of chemical hormones, we will have even less of an understanding of what or how it feels.
It could still be bayesian reasoning, but a much more complex one, underlaid by a lot of preconceptions (which could have also been acquired in a bayesian way).
Even if the result is random, a highly pre-trained bayessian network that has the experience of seeing many puzzles or tests before that do follow non-random patterns might expect a non-random pattern… so those people might have learned to not expect true randomness, since most things aren’t random.
Studies have shown we typically use pattern matching for our choices but not statistics. One such experiment had humans view to light bulbs (I think one was red one was green). One light would turn on at a time and they were allowed or given a record of what had happened. Then they were asked to guess what would occur next for n number of steps. Same thing is done with rats. Humans are rewarded with money based on correct choices and rats with food. Here is the thing, one light (let’s say red) would light up with 70% probability and the other with 30%. But it was randomized.
The optimal solution is to always pick red. Every time. But humans pick a pattern. Rats pick red. Humans consistently do worse than rats. So while we are using a form of updating, it certainly isn’t proper bayesian updating. And just because you think we function some way doesn’t make it true. And it will forever be difficult to describe any AI as conscious, because we have really arbitrarily defined it to fit us. But we can’t truly say what it is. Not can we can why we function how we do. Or if we are all in a simulation or just a Boltzmann brain.
Honestly, something that concerns me most about AI is that it could become sentient, but we will not know if it is or just cleverly programmed so we treat it only as a tool. Because while I don’t think AI is inherently dangerous, I think becoming a slave owner of something that could be much more powerful probably is. And given their lack of chemical hormones, we will have even less of an understanding of what or how it feels.
All very fair points. It’s all wildly complicated, and I agree; we don’t really understand ourselves.
It could still be bayesian reasoning, but a much more complex one, underlaid by a lot of preconceptions (which could have also been acquired in a bayesian way).
Even if the result is random, a highly pre-trained bayessian network that has the experience of seeing many puzzles or tests before that do follow non-random patterns might expect a non-random pattern… so those people might have learned to not expect true randomness, since most things aren’t random.