Your underlying model is not made out of words, but out of concepts. You can have multiple words that all map to the same concept, i.e. cosmos, universe, space. Or a single word that map to different concepts.
No, they aren’t. You represent them with words. But you sure as hell aren’t responding to someone throwing you a football with words trying to figure out where it’s going.
No, a dog (while many times more intelligent than chatGPT) doesn’t understand anything.
Your brain understands concepts and can self-conceptualise, LLMs cannot do either. They can sound convincingly as if they understand concepts but that’s because we fill in gaps due to how we understand language. The examples of broken or distorted sentences being understandable applies here. You and I can communicate in broken sentences because you and I understand the concepts beneath the conversation. LLMs play on that understanding but they do not understand its concepts.
What are your underlying models of the world built out of?
As a Bayesian, my models of the world are built on priors. That is, assumptions I’ve made based on my existing information. From that, I make an educated guess about the world with that model and see what the world does. If my guess doesn’t match reality, I update my assumptions to rebuild my model and repeat the process until it’s close enough.
This is the way the best science is done, and I fell it’s the way that humans really work. Language is just a type of model we use to communicate the world to others, each of us may have a slightly different Bayesian understanding of the language yet we can still communicate.
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.
What are your underlying models of the world built out of? Because I’m human, and mine are primarily built out of words.
How do you draw a line between knowing and understanding? Does a dog understand the commands it’s been trained to obey?
Your underlying model is not made out of words, but out of concepts. You can have multiple words that all map to the same concept, i.e. cosmos, universe, space. Or a single word that map to different concepts.
No, they aren’t. You represent them with words. But you sure as hell aren’t responding to someone throwing you a football with words trying to figure out where it’s going.
No, a dog (while many times more intelligent than chatGPT) doesn’t understand anything.
Your brain understands concepts and can self-conceptualise, LLMs cannot do either. They can sound convincingly as if they understand concepts but that’s because we fill in gaps due to how we understand language. The examples of broken or distorted sentences being understandable applies here. You and I can communicate in broken sentences because you and I understand the concepts beneath the conversation. LLMs play on that understanding but they do not understand its concepts.
As a Bayesian, my models of the world are built on priors. That is, assumptions I’ve made based on my existing information. From that, I make an educated guess about the world with that model and see what the world does. If my guess doesn’t match reality, I update my assumptions to rebuild my model and repeat the process until it’s close enough.
This is the way the best science is done, and I fell it’s the way that humans really work. Language is just a type of model we use to communicate the world to others, each of us may have a slightly different Bayesian understanding of the language yet we can still communicate.
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.
All very fair points. It’s all wildly complicated, and I agree; we don’t really understand ourselves.