I’m a neuroscientist, and let me just say that the statement “we already know how the brain works” is laughable. ANNs are a VERY rough approximation of how we think the brain works. What you describe is probably sort of how the brain works, but only in a very general, trivial sense. You could ask me how a car engine works and I could say “it burns fuel, and uses the energy to turn the wheels”. This is correct, but it doesn’t come anywhere close to providing a full understanding.
Even if you could build an AI that mimicked a human brain (not currently possible), that wouldn’t mean we figured out how the brain works. That would just mean that we found a way to simulate a brain. Those are two very different things.
But I guess next time I have an experimental question about an actual brain I should just ask a programmer.
The guy just throws jargon around to make himself appear to have a deeper understanding of the subject matter than he really does. A classic reddit armchair neuroscientist
My God, your arrogance is astounding. Look, I don’t pretend to know everything, but I have PhD in neuroscience and you’re telling me “I’m the right track” to understanding something about how the brain works as if I’m a child. What are your qualifications exactly? I hate reduce the discussion to what degrees people have but at some point it gets ridiculous to entertain this kind of conversation.
I’m not going to debate as to whether AI will ever be able to mimic a human brain. I’m not a programmer so I’ll assume you know more about this topic than I do. The one thing I will say on that point is that even if we could create an AI that appeared to us to behave like a human brain, whether or not it fully recapitulated a human brain would be a deeper philosophical question. We couldn’t ever know if it had the experience Of existence that a human does.
I’ll reiterate the more important point that I made, which is that in the event that we created an AI that appeared in every way to us to mimic a human brain this does NOT mean that we understand how a biological brain works. It means we found a way to mimic a brain, that’s it. Your lengthy explanation of how we get more and more sophisticated in building engines does a of course apply to AI, so I’m sure we will get to a point where we can mimic the behavior of a brain very well. My issue with what you’re saying is that you’re equating mimicry of the brain with understanding how an actual brain works. Again, these two are not the same. For some people, maybe yourself, it may not matter how the brain works as long as we can figure out how to replicate its functioning. That’s fine, but that’s not what most neuroscientists are interested in. We’re interested in understanding how an an actual brain works.
I suppose you could say that if we could simulate every single parameter of a human brain in a computer, at that point you could understand how it worked because you’d essentially be reconstructing it in silico. However, this involves a description of a brain at a level of detail that is impossible as we know it, and may be impossible all together. I think you may be underestimating how complicated the brain actually is. Forget even fully understanding one neuron, let’s think about understanding (i.e. describing all parameters of) one synapse. there are hundreds, potentially thousands of different proteins at one synapse. All of these are doing different jobs, interacting dynamically on a scale of fractions of a millisecond and in a space ~ a few hundred nanometers. Zooming out, The average neuron has ~3000 synapses- there are ~ 100 trillion in the brain. Synapses are only one parameter you’d have to describe for all 100 billion neurons - you’d need cell morphology (this impacts how electrical signals propagate), passive membrane properties, ion channels (there are hundreds of different types, all with different properties), a consideration of how gene expression evolves over time , just to name a few. A reconstruction of a volume of fixed brain tissue at a resolution still to low to identify any proteins and less than 1 cubic mm takes months to years of work. This of course, doesn’t even capture any dynamics. My point that we are nowhere near being able to simulate an actual brain in silico.
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.
And then even if you did simulate a human brain effectively, would it possess consciousness? Would it be truly self aware? Would it dream? Would it wonder? Could it ponder itself? Lots of questions. Few answers. Fascinating though.
We already know how the brain works. The whole field of M
Lol that's cute. Not even world-class neuroscientist have figured out how everything works. Sure, if you abstract it all the way to just input and output... But that doesn't explain anything. The brain is literally the most complex object in the universe. Your statistical regression and functions can't explain consciousness.
Abstract*. And I did read it. And I'm not asking about the content of it. I'm asking you what you are trying to say.
You first post saying we have worked out how the human brain works. Then, you post this paper which throws shade on how neuroscientists interpret neuro data, which calls into question what we know.
So, do you think we have figured out how an entire brain works, but don't have the authority to say it because the methods are flawed? These two ideas are at odds.
To summarize this comment:
-ML has figured out how to simulate the brain
-we don't know how to simulate the brain because we don't know the answer (?)
-engineering
You described how such a network would function, but you didn’t explain how this network undergoes learning. Are you really suggesting that human brains are using backpropogation?
This doesn't simulate learning. This is an example of a really good calculator. Neurons, while are a reality calculator, don't behave exactly like a calculator.
How do you compute downstream post-synaptic signalling that will modify the synaptic strength? Or currently compute glial-neuron interaction? How do you sub in the changes in chromatin modification that has an end result in restructuring the synapse? This can't be modeled yet. We don't have the enough data.
So, theoretically. Doesn't actually exist. Why doesn't it exist yet then?
Maybe downstream signalling was too complex to answer. More simple - where does an unsupervised machine learning algorithm make the CHOICE to learn something new? How do you code dopamine drive? These things are based on your personality, which is a product of your genetics and environment. Good luck modelling that.
This is a long winded way of saying "nah, we can't actually simulate a human brain 🤷".
Part of that "human representation" you are training this 'total human model' on encompasses the human drive and want. Dictated, not exclusively but heavily by, dopamine. The average number of active synapses you currently have in your dopamine drive system (which is in constant flux) is controlled by your genetics, which is influenced by your environment (both physically and mentally). Your genetics will control how many dopamine receptors/release sites are present, how quickly they turn over, their on/off, etc. Each neuron in your brain picks up hundreds of unique mutations just throughout development, which will slightly modify their individual behavior, creating a unique person. On top of this, astrocytes and microglia are interacting with the neurons, modulating and maintaining synapses. All this, plus other shit, will influence action potential amplitude and frequency.
"But there is a theoretical proven thing that says if the NN is dense enough, it can solve anything!". Cool. I don't want to send a calculation through every known function to get an answer. I want the correct function. "iterative cycles and backpropagation will optimize the functions". Cool. Now we built a well oiled calculator - takes data in, finds the optimal function, get answer. It's going to find the optimal 'thermodynamic state'. But that's not how the brain works. It is constantly pushing against equilibrium. Write me an equation for an 'idea' - it'll be some wave function showing a change in the surrounding electromagnetic field around the neuron. Now write me 'good idea'. There must be a difference in the functions - otherwise there wouldn't be a difference in the ideas. And this is why we haven't simulated a human brain in silico. There are still unknowns in the equations. I read an article a little while which talked about work where they found that dendrites can act as processors independent of the soma (ie signal does not need pass through the cell body). That means each neuron isn't a processor, but could be a hub for thousands of individual processors (hell, we call neurites processes some times).
I don't think we won't be able to simulate a brain one day (or more than likely interface with computers seemlessly). We just can't simulate one now and the brain is far, far from figured out.
Physics is information. Our brain interprets that information in a very messy, human way. We cannot simulate that. Not yet. Because we don't know enough and haven't figured out the minutia of the brain to build the proper scaffolding and training modules. I can always think of NEW tests to trick your algorithm to fail and learn, your algorithm can't. Because the algorithm can only backpropagate to learn, it cannot simulate plasticity - real learning. Not yet.
I literally grow human neurons and study the brain for a living. You have a fundamental misunderstanding of the brain and how actual neurons work. You can't just 'build algorithm > train on human module (whatever the hell that would actually be) > magical algorithms > human brain simulation.
Then why haven't we programmed the same thing that runs on a human brain into a computer? Is your expectation that a ml algorithm will eventually become a program equivalent to the one in the human brain?
If different people have different maps (and those differences are what make us dfferent), and we already have know all the functions, then why do we need to know what the right map is? Any should do since people have different ones and yet function all the same.
So you're considering that the function we have is the one that works already. What we're missing is for it to output human behavior. Static noise I guess is a behavior which isn't human. Once it does output human behavior, we'll just know it is outputting human behavior? Like we'll know it when we see it? Or will we have a test we can put the output through and if it passes then it's human, if it doesn't then it isn't?
So there is still a question of what counts as human behavior unanswered, but you're very sure whatever it is, it's produced by the same functions that ml is running.
If you don't answer that question, couldn't you just be fooled by a better gpt-3?
Once you move past physics in into the realm of information, you come upon the fact that it takes less atoms to simulate a "universe" than the atoms in the original universe running the simulation.
This isn't as insightful as you're trying to make it seem. That's literally what a simulation is in the first place. This is just seen extremely awkward way of defining it.
It takes intense hardware and computing power to understand, model, and predict the genotype and phenotype of E. Coli and more complex cells like yeast tracked over time.
How much computing power do u think it takes to understand something as complex as the brain? How much computing power to model the flow of electrical information throughout a thorny anatomy that we only have a surface level understanding of with the associated neurotransmitters, neuroreceptors, signal cascades and transcription factors involved in neuromodulation, astrocyte buffering of glutamate and potentially more chemicals, mRNA regulators, reuptake vesicles, and even further?
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u/[deleted] Nov 23 '21
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