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
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.
We’re having our particular technological revolutions as well. In little more than a century we’ve managed to construct computing devices with capabilities that may have taken thousands of years to be achieved by nature.
Because we don’t understand it.
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.)
To understand the complexity of the human brain, you need a brain more complex than the human brain.
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.
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
Trial and error.
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.
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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.
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 :)
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
There’s actually a Robert Miles video on this very question.
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
A programmer’s pet peeve is someone who says “why can’t you just…”.
But the fundamental problem with your plan, assuming it’s possible at all - it’s been said that if the brain were simple enough for us to understand then we’d be too simple to understand it - is that you’re going to want to make your AI at least as smart as someone who’s 30-40 years old, which by definition would take 30-40 years.
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.
You can’t raise it like a human because is not a human. Are you going to put it the size of baby? Gonna pump it with hormones that change its structure when it becomes a teen?
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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
ETA: not to mention the brain is a heck of a lot more than a collection of neurons. Other commenters pointed out how we just discovered a new kind of brain cell - the brain is filled with so many different types of neurons (e.g. pyramidal, Purkinje, dopamine-based, myelinated, unmyelinated, internet Ron’s, etc.). Then there’s an entire class of “neuron support” cells called neuralgia. This includes oligodendrocytes (and Schwann cells), microglia, satellite cells, and most importantly, astrocytes. These star-shaped cells can have a huge impact on how neurons communicate by uptaking neurotransmitters and other mechanisms.
Here’s more info: https://en.wikipedia.org/wiki/Tripartite_synapse?wprov=sfla1
We didn’t know which things mechanisms in a nuron are important, and we don’t have anywhere near the computing power to model all of them. We have guesses as to what’s important, and that’s what a lot of modern AI is built on. But because computers have different strengths and weaknesses, we can’t simulate a whole human brain yet.
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.
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?
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.
The one if the big reason that people are brushing over is latency. You can have a billion super computers simulator something but the latency between them will prevent you from simulating at a reasonable speed an interconnected system like a bunch of neurons.
