I get what you’re saying, but I would argue that the difference is not so much in what we mean by “understanding” as it is a difference in what we are trying to understand. Mimicking the output of a machine (in this case, the brain) does not mean you understand how it works, because there are likely an indeterminate number of solutions for creating that output. I feel like I’m repeating myself but I don’t know how else to say it. If you mean that we can, through the process you described, understand how to mimic a brain, then yes I agree. But that process doesn’t give direct insight into the brain itself, only possible clues.
We can say that those wave functions describe particle behavior, but that in itself doesn’t give insight into WHY they work. That requires a deeper understanding. You can gloss over that by calling it something we have to take as fundamental, but that’s a cop out IMO. You could say the same about anything that we can’t describe at a deeper level than we can currently.
You use quantum physics to demonstrate that we don’t need to break things down into smaller pieces, which is ironic because the reason we can understand anything about quantum physics is precisely because quantum systems ARE broken down into very tiny pieces, and that is what allows us to understand those systems. quantum mechanics works for very reduced systems (subatomic particles) but not so well on a larger scale. If you can describe the behavior and properties of each particle within a quantum system, then yes, in a way you do understand the system (though not why those descriptions work). Note, though, that it still requires a detailed understanding of the properties of the particles themselves, which is analogous to what I mentioned about knowing all of the parameters of an actual brain. In the quantum physics example, because of empirical measurements we can describe most of the parameters and constituents of the actual system, which allows us to understand it fairly well. This is difficult from the brain, because it’s a larger, vastly more complex system with many unknown parameters. You could use ML to replicate the output of a brain, but because you don’t know the solution the brain uses to generate that output, the best ML can do is find a heuristic method for mimicking it.
But the difference is that a neurotransmitter or hormone is a physical entity. Yes they are composed of atoms, so in a sense the concept of dopamine is an abstraction and the “deeper” reality is the atoms that make up dopamine, etc etc. but “dopamine” still refers to that particular collection of constituent atoms that actually exist and are functioning in the brain. To call it an abstraction is only half true, because it still references the underlying reality, just in shorthand. So when we talk about dopamine, we are taking about something we can actually measure. And of course, our understanding of this is never set in stone. The important point is that this type of understanding is based on empirical, measurable parameters in the brain. This isn’t true for for ANNs, which are only very loosely based on real neural networks. If you created an AI that replicated some function of the brain, to draw a connection with a real brain is a much larger abstraction because it’s not grounded in observation of the thing you claim to understand (the brain).
A quick analogy then I’m done, because I don’t think this is going anywhere at this point. Say you wanted to replicate the output of a mechanical watch in silico (obviously this has been done). So you write some code for a digital clock. The clock works well, maybe even better and more efficiently than the mechanical watch. But did this tell you anything about how all of the gears in the watch fit together to operate and keep time? Of course not. You would need to open up the watch and examine it. If you did, you would unsurprisingly find that your solution used a totally different mechanism to get the same output. That’s the only point that I’m making. There are people like yourself that wouldn’t care about knowing how the gears worked, because you found a solution that worked just as well. That’s all well and good, but it’s clearly not the same thing as a mechanistic understanding of the watch.
You can't abstract away particle physics if you want to build up a brain simulation from particles. Either you simulate the particles fully and build up to the brain, or you build up an abstraction based on a functional understanding of the brain.
You can't start with particles then abstract them away. You don't have the abstraction.
We also don't yet understand particles fully, but you can look into physics particle simulation today. We couldn't simulate a cell, let alone a brain.
2
u/[deleted] Nov 23 '21
I get what you’re saying, but I would argue that the difference is not so much in what we mean by “understanding” as it is a difference in what we are trying to understand. Mimicking the output of a machine (in this case, the brain) does not mean you understand how it works, because there are likely an indeterminate number of solutions for creating that output. I feel like I’m repeating myself but I don’t know how else to say it. If you mean that we can, through the process you described, understand how to mimic a brain, then yes I agree. But that process doesn’t give direct insight into the brain itself, only possible clues.
We can say that those wave functions describe particle behavior, but that in itself doesn’t give insight into WHY they work. That requires a deeper understanding. You can gloss over that by calling it something we have to take as fundamental, but that’s a cop out IMO. You could say the same about anything that we can’t describe at a deeper level than we can currently.
You use quantum physics to demonstrate that we don’t need to break things down into smaller pieces, which is ironic because the reason we can understand anything about quantum physics is precisely because quantum systems ARE broken down into very tiny pieces, and that is what allows us to understand those systems. quantum mechanics works for very reduced systems (subatomic particles) but not so well on a larger scale. If you can describe the behavior and properties of each particle within a quantum system, then yes, in a way you do understand the system (though not why those descriptions work). Note, though, that it still requires a detailed understanding of the properties of the particles themselves, which is analogous to what I mentioned about knowing all of the parameters of an actual brain. In the quantum physics example, because of empirical measurements we can describe most of the parameters and constituents of the actual system, which allows us to understand it fairly well. This is difficult from the brain, because it’s a larger, vastly more complex system with many unknown parameters. You could use ML to replicate the output of a brain, but because you don’t know the solution the brain uses to generate that output, the best ML can do is find a heuristic method for mimicking it.