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How far are we from fully modeling the human brain? I mean besides an easy way to identify all the neuronal connections... This makes me feel like we're close
by iaw 3y ago
How far are we from fully modeling the human brain? I mean besides an easy way to identify all the neuronal connections...
This makes me feel like we're close to that one terrifying short-story.
- neurostimulant 3y agoIs human consciousness just a product of neural networks, or is there other additional mechanisms we're not aware of?
- ChatGTP 3y agoWouldn't modelling the human brain mean we'd be using less power? We're using brute force to try get similar results to what the brain does.
- TheAlchemist 3y agoYou would need to define what you mean by 'modeling the human brain'. If it means AGI or anything similar, then we're very far. To paraphrase an analogy I've heard somewhere (in similar context) - We're building better and better ladders, maybe even lifts with this last push in ML field. But the brain is on the moon - even the best lifts won't get us there.
- tbenst 3y agoWe are massively far away from modeling the human brain. First of all, no one can agree what level is necessary to model the brain, and that varies tremendously by scientific question. Personally, my lower limit would be something like the computational package Neuron which models voltages across axon compartments and distribution of ion channels, My upper limit confidence bound is we don’t care about anything subatomic. At the upper bound: In molecular dynamics, which is used extensively in modern day neuroscience to understand the function of ion channels and GPCRs, a single H100 can model 70ns/day of compute for 1M atoms. There are 8.64e+13 nanoseconds per day. There are ~10^26 atoms in a human brain. Therefore, an upper limit back of envelope is you need fewer than 10e+26 atoms / 10e+9 atoms * 8.64e+13 ns / 70 ns = 1.23e+29 H100 GPUs. Calculating the lower bound is more difficult, but let’s start by saying you can get away with a fp16 for each synapse. Storing the weights of that model for 100 trillion synapses is 200 Terabytes, and if you figure weight size * 4 or so to do anything useful then this is in spitting distance. Note that this example lower bound is massively less complex than the Neuron model I suggested, as the entire field of neuromodulators, homeostatic mechanisms, glia, and more are thrown out, which are all important for modeling how the brain works under certain computational regimes.
- Jedd 3y agoThat is a spectacular response! My bio knowledge is very basic, so forgive naiviety in these two questions. First, I'm not asking you to go through the math on the spot, but I'm guessing that lower-bound capability is well understood in 'the field', but is it documented against various species? Perhaps mapping against current / projected GPU/compute systems capabilities? (I know there's a project to model a worm's brain, IIRC down to molecular level. But I'm picturing a 'we are 3 years away from being able to emulate a basset hound, 4 years for a border collie' - that kind of roadmap.) Second, you said upper bound is to ignore sub-atomic. I thought we had proton and electron gradients, at least in metabolism. I believe proton there is a synonym for Hydrogen (atom), but electron would imply some potential need to emulate at sub-atomic? Have I misunderstood the bounding / chemistry involved?
- DoctorOetker 3y agoThe electron and proton are considered parts of the realm in atomic physics still. When physicists speak of sub-atomic they tend to mean any physics below the Aufbau model or electrons and nuclei.
- ftxbro 3y agoFor the lower bound there is a dark horse factor that has spooked Geoffrey Hinton. He thinks that biological brains aren't able to do backpropagation effectively through multiple layers, and so differentiable programming frameworks are much more powerful than what the brain has, at an algorithmic level. In other words, he thinks that computers are able to learn more effectively than any neuron-based biological brain. Of course right now there are caveats. The brain appears to have more 'statistical efficiency' meaning it appears to learn more from less data, and the brain is obviously more energy-efficient. There is also the possibility that Geoffrey Hinton is just wrong.
- DoctorOetker 3y agoCan you cite a reference for Geoffrey Hinton (and or others) on this line of thought on this "dark horse factor"? I think this resonates with a lot of people, having a focal point Schelling point to refer to in discussions would be handy. I believe the situation is a lot more extreme than the absurd efficiency of differentiable programming. I have been meaning to write up (but been too busy to do so) an insight where I believe training can be made ridiculously cheap computationally speaking (in a way that combines with differentiable programming, not replaces it). I am agnostic if this is what the brain does, but wouldn't be surprised at all if the brain does in fact do back-propagation (or uses the insight that I've been meaning to write up).
- photochemsyn 3y agoI think a long ways away, if I understand this article about the difficulties involved in accurately modeling even a single biological neuron: https://www.quantamagazine.org/how-computationally-complex-is-a-single-neuron-20210902/ https://www.quantamagazine.org/how-computationally-complex-i... > "If each biological neuron is like a five-layer artificial neural network, then perhaps an image classification network with 50 layers is equivalent to 10 real neurons in a biological network." The complexity explodes quickly because each biological neuron's behavior is modulated by a large number of biochemical neurotransmitters, on top of all the dendritic connections (up to 15,000 each, apparently).
- predictabl3 3y agoSorry if I'm missing an obvious reference, but what short story do you mean? (edit: thanks for both replies already, and any others that might fit; I understand now the reference was likely to Asimov)
- mensetmanusman 3y agohttps://users.ece.cmu.edu/~gamvrosi/thelastq.html https://users.ece.cmu.edu/~gamvrosi/thelastq.html
- iaw 3y agohttps://qntm.org/mmacevedo https://qntm.org/mmacevedo
- itissid 3y agoWhen AI becomes good enough we will maybe we will stop thinking about trying to imitate human brains. If we viewed our brain's decision making power objectively we can find several flaws, for example our heuristics to make quick decisions for mundane things is also our greatest weakness(short sightedness). We are poor at incorporating data to make good decisions and constantly bias due to some external stimuli. Why would you want to make anything close to the brain? What real scientific or engineering or humanitarian uses does doing that even have? AI is already and going forward should strive to be a groundup of redesign of intelligence.
- notamy 3y ago> Why would you want to make anything close to the brain? What real scientific or engineering or humanitarian uses does doing that even have? To have models of the human brain that we can poke at and change and tinker with and etc., so that we can get better ideas of how therapy techniques, medications, ... will impact the actual real people that might benefit from them.
- deleted 3y ago[deleted]
- leriksen 3y agoThe brain is analog and chemical, AI will be digital and silicon. We have no idea how the map from one to the other.
- alchemist1e9 3y agoIs the Quantum computer hypothesis dead?
- Filligree 3y agoI don't see how quantum computers are relevant? We can't build them, and there certainly isn't any interesting quantum computation in the brain.
- svnt 3y agoWhat do you mean we can’t build them? To your second point, we have little to no ability to understand yet what quantum effects may or may not be active in brain/consciousness function. We certainly can’t exclude the possibility.
- Filligree 3y agoI mean lots of people have tried to build quantum computers, and so far no-one has succeeded in anything that's describable as a "computer", instead of "half a dozen logic gates". Perhaps in the future. We can fairly well exclude the possibility of interesting quantum effects in human consciousness, because the human brain is a hot, dense environment that might as well have been literally designed to eliminate the possibility. It's the exact opposite of how you want a quantum computer to be built. Which doesn't mean there aren't plenty of quantum effects involved in the molecular physics, but that isn't what is normally meant by 'quantum computer'. Transistors would also meet that definition.
- svnt 3y agoI do not agree with either of your assertions: 1) That 433 qubits does not make a computer and is instead “a half dozen logic gates.” I agree a half dozen logic gates is not a computer. 433 qubits is not comparable in terms of information capacity or processing capacity to a half dozen logic gates. This number is also publicly doubling annually now — I would bet the systems we don’t know about are more complex. Importantly, a computer in this context is not something you would attach a monitor to — it is just an electronic device for storing and processing data. 2) That we have any good idea of the limits of how biological systems might be influenced by quantum effects within specific temperature ranges. You certainly wouldn’t construct a human brain to interface with quantum effects given the present state of our knowledge in constructing these kinds of systems. But then we can’t even construct a self-replicating cell yet, nevermind a brain. It’s hard to imagine we understand the limits at work there.
- dsign 3y agoThe question that interests me is how far are we from modelling a human cell, neuron or not? Because that's how we cure cancer.
- renonce 3y agoFor many applications there is no need to fully model the human brain. An approximation of a particular aspect would be sufficient in most cases. We didn’t build aeroplanes by fully modeling a bird, we just need aerodynamics.