I know current learning models work a little like neurons but why not just make a sim that works exactly like how we understand neurons work
There’s actually a Robert Miles video on this very question.
Was wondering if Robert Miles - Children had a music video with a lot of foresight.
https://youtu.be/DvyCbevQbtI
Hardware limitations. A model that big would require millions of video cards, thousands of terabytes of storage, and hundreds of terabytes of ram.
This is also where AI ethics plays into whether such a model should exist in the first place. People are really scared of AI but they don’t know that ethics standards are being enforced at the top level.
Edit: get Elon Musk on the phone, he’s deranged enough to spend that much money on something like this while ignoring the ethical and moral implications /s
Edit: get Elon Musk on the phone, he’s deranged enough to spend that much money on something like this while ignoring the ethical and moral implications /s
You joke but he’d probably traumatize a synthetic intelligence enough that it’d think 4chan user behavior is the baseline human standard
You wouldn’t need to raise it as a baby.
The reason that humans come out as babies in the first place is because if we came out with fully developed brains, our heads would be crushed through the birth canal and the mother would probably die. Therefore, our brains have to mature as we get older which of course takes decades.
Growing up is a biological imperative.
In terms of artificial intelligence or large language models, there would be no need to actually grow in physical size.
Which solidifies the point a person already made here is that it would be a fundamentally different kind of intelligence one that simply needs data input And will not need the ability to grow up as a child would.
That’s kinda the idea of neural network AI
The problem is that neurons aren’t transistors, they don’t operate in base 2 arithmetic, and are basically an example of chaos theory, where a system is narrow enough for outer bounds to be defined, yet complex enough that the amount of “picture resolution” needed to be able to accurately predict how it will behave is currently beyond our scope of understanding to replicate or even theorize on.
This is basically the realm where you’re no longer asking for math to fetch a logical answer to a question and more trying to use it as a way to perfectly calculate the future like an oracle trying to divine one’s own fate from the stars. It even comes with its own system of cool runes!
I fully imagine we will have a precise calculation of Rayo’s Number before we have a binary computer capable of being raised as a human with a fully human intelligence and emotional depth.
More likely I see the “singularity” coming in the form of someone who figures out how to augment human intelligence with an AI neural implant capable of the sorts of complex calculations that are impossible for a human mind to fathom while benefiting from human abilities for pattern recognition to build more accurate models.
If someone figures out how to do this without accidentally creating a cheap 80’s slasher villain, it will immediately become the single most sought after medical device in human history, as these new augmented mind humans will instantly become a major competitive pressure for even most manual labor jobs.
I don’t think we need exact neuron emulation to simulate human consciousness. We just need to work out the information theory behind it.
We currently have a good idea about how neurons behave, but to simulate them properly we also need to know how they work. I’m sure we can figure that stuff out, eventually. Give it a couple of decades or centuries and we’ll know enough to properly simulate neurons, if not just for finding cures to diseases. From that point on it’s just a matter of reducing the complexity and scaling the simulation.
We’ve figured out how to make robots walk by first making exact copies of animals and people, and then once we got that to work we reduced the whole thing down to a relatively simple machine that’s getting better by the year. I’m sure we’ll be able to apply the same pattern to neurons once we figure them out.
Scientists are already working on simulating the complete brain of a small insect with a miniscule brain. It takes just about a supercomputer to run that thing in real-time, but it’s not finished yet.
With the way technology is headed, I do wonder whether we’ll get a cyborg singularity before or after we manage to simulate consciousness. Either seem possible, so I think it just comes down to what kind of technology gets invented first. With Musk still paralysing monkeys in his lab, I don’t think we’re close to either option today.
We don’t really understand how real neurons learn.
We’ve got some really good theories, though. Neurons make new connections and prune them over time. We know about two types of ion channels within the synapse - AMPA and NMDA. AMPA channels open within the post-synapse neuron when glutamate is released by the pre-synapse neuron. And the AMPA receptor allows sodium ions into the dell, causing it to activate.
If the post-synapse cell fires for a long enough time, i.e. recieves strong enough input from another cells/enough AMPA receptors open, the NMDA receptor opens and calcium enters the cell. Typically an ion of magnesium keeps it closed. Once opened, it triggers a series of cellular mechanisms that cause the connection between the neurons to get stronger.
This is how Donald Hebb’s theory of learning works. https://en.wikipedia.org/wiki/Hebbian_theory?wprov=sfla1
Cells that fire together, wire together.
Name checks out
i think that’s roughly exactly what happened - i think the new neural nets have 80 billion neurons which is a rough estimate of what a human brain has
the way they work is wildly different of course
What “new neural nets have 80 billion neurons”? Examples?
Simple answer: We don’t have any computer to run that on. While I don’t see any absolute limitations ruling out that approach… The human brain seems to have hundreds or thousands of trillions of connections. With analog electrical impulses and chemistry. That’s still sci-fi and even the largest supercomputers can’t do it as of today. I think scientists already did it for smaller brains like those from flies(?), so the concept should work.
And then there is the question what are you going to do with it. You can’t just kill a human, freeze the brain, slice it and then digitize it by looking at a microscope a trillion times. So you have to make it learn from ground up. And this requires a connection to a body. So you also need to simulate a whole body and the world it’s in on top. To make it learn anything and not just activate random neurons. So that’s going to be sci-fi (like the Matrix) for the near and mid future.
Neurons undergo physical change in their interconnectivity. New connections (synapses) are created, strengthened, and lost over time. We don’t have circuits that can do that.
Yes we do. FPGAs and memristors can both recreate those effects at the hardware level. The problem is scaling them and their necessary number of interconnections to the number of neurons in the human brain, on top of getting their base wiring and connections close to how our genetics build and wires our base brains.
Actually, neuron-based machine learning models can handle this. The connections between the fake neurons can be modeled as a “strength”, or the probability that activating neuron A leads to activation of neuron B. Advanced learning models just change the strength of these connections. If the probability is zero, that’s a “lost” connection.
Those models don’t have physical connections between neurons, but mathematical/programmed connections. Those are easy to change.
That’s a vastly simplified model. Real neurons can’t be approximated with a couple of weights - each neuron is at least as complex as a multi-layer RNN.
I’d love to know more.
I recently read “The brain is a computer is a brain: neuroscience’s internal debate and the social significance of the Computational Metaphor” and found it compelling. It bristled a lot of feathers on Lemmy, but think their critique is valid.
Do you have any review resources? I have a bit of knowledge around biology and biochemistry, but haven’t studied neuroscience.
And no pressure. It’s a lot to ask dor some random person on the internet. Cheers!
Here’s the video that introduced me to the idea: https://www.youtube.com/watch?v=hmtQPrH-gC4
He explains it very well and gives a lot of references :)
Did OP mean accomplishing the connectivity and with software rather than hardware? No, we don’t have hardware that can modify itself like a brain does, but I think it is possible to accomplish that with coding.
Sure, but now you’re talking about running a physical simulation of neurons. Real neurons aren’t just electrical circuits. Not only do they evolve rapidly over time, they’re powerfully influenced by their chemical environment, which is controlled by your body’s other systems, and so on. These aren’t just minor factors, they’re central parts of how your brain works.
Yes, in principle, we can (and have, to some extent) run physical simulations of neurons down to the molecular resolution necessary to accomplish this. But the computational power required to do that is massively, like billions of times, more expensive than the “neural networks” we have today, which are really just us anthropomorphizing a bunch of matrix multiplication.
It’s simply not feasible to do this at a scale large enough to be useful, even with all the computation on Earth.
Thanks for putting it at a scale I can grok. If we could create such a device it would just be a literal (digital) brain.
Performance suffers. Basically we don’t have the computing power to scale the sw to the perf levels of the human brain.
Because we don’t understand it.
To understand the complexity of the human brain, you need a brain more complex than the human brain.
To clarify:
We don’t even know how human intelligence/consciousness works, let alone how to simulate it.
But we know how an individual neuron works.
The issue with OPs idea is we don’t know how to tell a computer what a bunch of neurons do to create an intelligence/consciousness.
Heck, we barely know how neurons work. Sure, we’ve got the important stuff down like action potentials and ion channels, but there’s all sorts of stuff we don’t fully understand yet. For example, we know the huntingtin protein is critical to neuron growth (maybe for axons?), and we know if the gene has too many mutations it causes Huntington’s disease. But we don’t know why huntingtin is essential, or how it actually effects neuron growth. We just know that cells die without it, or when it is misformed.
Now, take that uncertainty and multiply it by the sheer number of genes and proteins we haven’t fully figured out and baby, you’ve got a stew going.
To add to this, a new type of brain cell was discovered just last year. (I would have linked directly to the study but there was a server error when I followed the cite.)
Do you need to understand it in order to try it out and see what happens? I see lots of things experimenting with a small colony of neurons. Making machines that move using the organic part to navigate or making them play games (still waiting on part 2 of the Doom one). Couldn’t that be scaled up to human brain size and at least scanned to see what kind of activity is going on and compare it to a real human brain?
We need to understand what we’re simulating to simulate it. We know the structure of neurons at a simple level, we know how emergent systems represent more complex concepts… we don’t know how the links to build that system are constructed.
Even assuming we can model the same number of (simple machine learning model) neurons, it’s the connections that matter. The number of possible connections in the human brain is literally greater than the number of atoms in the universe.
I just want to make sure one of your words there is emphasized “possible” (Edit it’s also wrong as I explained below)
The number of possible connections in the human brain is literally greater than the number of atoms in the universe.
Yes - the value of 86 billion choose two is insanely huge… one might even say mind bogglingly huge! However, in actuality, we’ve got about 100 trillion neural connections given our best estimates right now. That’s about a thousand connections per neuron.
It’s a big number but one we could theoretically simulate - it also must be said that it’s impossible for the simulation of the brain to be technically impossible… We’ve each got a brain and there are a billion of us made up out of an insignificant portion of the mass+energy available terrestrially - eventually (unless we extinct ourselves first) we’ll start approaching neurological information storage density - we’re pretty fucking clever so we might even exceed it!
Edit for math:
So I did a thunk and 86 billion choose 2 actually isn’t that big, I was thinking of 86 billion factorial but it’s actually just 86 billion squared (it’d be 86 billion less than that but self-referential synapses are allowed).
Apparently this “greater than the number of atoms in the universe” line came from famously incorrect shame of Canada Jordan Peterson… and, uh, he’s just fucking wrong (so math can be added to the list of things he’s bad at - and that’s already a long list).
Yea so - 86 billion squared = impressively large number… but not approaching 10^80 impressively large.
I’ve been quoting Jordan Peterson for years?! Ahhh fuck.
Short answer: Neural Networks and other “machine learning” technologies are inspired by the brain but are focused on taking advantage of what computers are good at. Simulating actual neurons is possible but not something computers are good at so it will be slow and resource intensive.
Long Answer:
- Simulating neurons is fairly complex. Not impossible; we can simulate microscopic worms, but simulating a human brain of 100 billion neurons would be a bit much even for modern supercomputers
- Even if we had such a simulation, it would run much slower than realtime. Note that such a simulation would involve data sent between networked computers in a supercomputing cluster, while in the brain signals only have to travel short distances. Also what happens in the brain as a simple chemical release would be many calculations in a simulation.
- “Training” a human brain takes years of constant input to go from a baby that isn’t capable of much to a child capable of speech and basic reasoning. Training an AI simulation of a human brain is at least going to take that long (plus longer given that the simulation will be slower)
- That human brain starts with some basic programming that we don’t fully understand
- Theres a lot more about the human brain we don’t fully understand
Thank your AI LLM for this structured robotic reply in an easy to digest numbered list.
Lmfao I actually wrote that by hand but it does kinda look AI generated
Nah, too focused and not enough repetition and generalizations ;)
Main reason for answering: thanks!
Whatever you say, SkyNet. I upvoted your comment. Remember me buddy ❤️
Simulating even one neuron is very complex. Neurons in artificial neuron nets used in machine learning are a gross oversimplification. On top on this you need to get the wiring right. On top on this you need to get the sensorial system right (a brain without input is worthless). On top of this you need an environment. So it’s multiple layers of complexity that we don’t have
What I find fascinating is the efficiency of the brain.
With a supercomputer and the energy of a nuclear station to run it we are able to simulate a handful of neurons interacting with each other.
On the other hand the brain with billions of neurons only requires the energy of one or two potato to run.
To be fair, nature had millions od years to optimize the power consumption and we only observe the successful results since the failures didn’t survive.
Just some thoughts:
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Current LLMs (chat AIs) are “frozen brains.” (Over-)Simplified, the synapses on the AI’s input neurons are given the 2048 prior words (the “context”) and the AI’s output synapses mean a different word each, so the synapse that lights up most strongly is the next word the AI will say. Then the picked word is added to the “context” and the neural network is executed once more for the next next word.
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Coming up with the weights of the synapses takes insane effort (run millions of books through the “context” and look if the AI t predicts the next word correctly, if not, change a random synapse). Afaik, GPT-4 was trained on more than 2000 NVidia A100 GPUs for somewhere around 4 to 7 months, I think they mentioned paying for 7.5 Megawatt hours.
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If you had a super computer that could keep running the AI with live training, the AI’s ability to string up words would likely, and quickly, degrade into incoherence because it would just ingest and repeat whatever went into it. Existing biological brains have these complex mechanisms of distilling experiences and evaluating them in terms of usefulness/success of their own actions.
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I think that foundation, that part that makes biological brains put the action/consequence in the foreground of the learning experience, rather than just ingesting, is what eludes us. Perhaps at some future point in time, we could take the initial brain structure that grows in a human as the seed for an AI (but I guess then we’d likely have to simulate all the highly complex traits of real neurons, including mixed chemical and electrical signaling and possibly even quantum-level effects that have been theorized).
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Creating an accurate neuron simulation would probably require much more advanced AI than we already have. Like, real AI, not this piddly, piecemeal shit we have now.
You’re looking at this backwards. We’d need better AI to even start trying to simulate neurons accurately. They’re far more complex.
Currently, AI is capable of analyzing basic chemical and cellular interactions. It’s ok at it.
Actually, we’ve got some pretty sophisticated models of neurons. https://en.wikipedia.org/wiki/Blue_Brain_Project?wprov=sfla1
See my other comment for an example of how little we truly understand about neurons.
We do have some pretty sophisticated models of neurons, and there are persistent theories (2015 was earliest I found in a quick search) that brains use some quantum physics, in particular Quantum Entanglement, to operate.
https://phys.org/news/2022-10-brains-quantum.html
In which case, hardware has a very long way to go before we can do that at scale.
Modeling neurons and simulating them with AI are very different things. And, as you say, we still know very little about neurons and the nervous system and the brain itself. How, then, could we even attempt to train an AI to work accurately?
It’s not a terrible idea by any means. It’s pretty hard to do, though. Check out the Blue Brain Project. https://en.wikipedia.org/wiki/Blue_Brain_Project?wprov=sfla1
With current technology, a supercomputer capable of that would be absolutely gigantic, immobile, and have an insane power draw. How’re you going to raise a building like a human?
Currently, a mouse brain is about the limit of what we can do. https://www.cell.com/neuron/fulltext/S0896-6273(20)30067-2
