4 ms·

For some more context / concrete links: there are ongoing efforts to simulate the C. elegans worm, e.g. https://openworm.org/ https://openworm.org/ , which has

by Groxx 4y ago

For some more context / concrete links: there are ongoing efforts to simulate the C. elegans worm, e.g. https://openworm.org/ https://openworm.org/ , which has ~300 neurons. The actual precision of this model is: nobody knows, because nobody knows precisely what neurons do / what they react to. We know some of it but definitely not all. But, simulating what we do know, you get quite worm-like behavior, despite whatever flaws exist. To get a more perfect simulation, we'd need more perfect knowledge of the chemistry and physics, and lots and lots more computing power. It's something that's continually improving, but a lot of shortcuts have to be made to make it even remotely calculable. That'll always be the case, physics is simply too complicated to both efficiently and accurately simulate.

cyberax 4y ago
OpenWorm is a bad model. To give a computer analogy, you're simulating highly optimized low-level code for Apple ][. That uses all the little hardware-specific tricks and timings to work. A fruit fly brain is a better model to simulate in this regard. It's much more "generic", so there's a hope that we can recapture high-level behavior from it more easily.
- tsimionescu 4y ago
  > A fruit fly brain is a better model to simulate in this regard. It's much more "generic", so there's a hope that we can recapture high-level behavior from it more easily. This is a completely baseless assumption. It is also moot, since simulating even 300 neurons is beyond our current computational power; simulating 3000 is not going to be possible in the foreseeable future.
  - cutemonster 4y ago
    What makes stimulating 300 that computationally heavy? (Doesn't sound like many?, but I've read that one single neuron needed 100 or so nodes in a deep neural net to simulate. But 300*100 also doesn't sound like many (nodes)?) Edit: some explanations here: https://news.ycombinator.com/item?id=35113498 https://news.ycombinator.com/item?id=35113498 (in this discussion)
    - Groxx 4y ago
      Yep, pretty much that thread. Massively simplified models exist, and yeah - we have artificial neutral networks FAR larger than that and they run just fine. But they're so over-simplified that it's fair to call them something else entirely, not a simulation. You can use them to create similar behavior, but they're just following the basic concept of a brain, not how they actually work. Inspired by a real thing, not actually mimicking a real thing. Kinda like how a door hinge is not your knee, even though they both bend. It's not a knee simulation, it's just something that bends. For some things (doors) that's perfectly fine, and there are billions of them in use. You can't transparently replace your knee with one though, it has biology-juice all over it and needs to handle very different behaviors at times.
      - GoblinSlayer 4y ago
        How do you verify precision of the simulation?
        Groxx 4y ago
        In most cases the vague answer is "how closely can it recreate the end result". Because that's a useful measure. For brains: we have no idea. Every brain is somewhat different, but we only have a couple complete physical layout maps so far. No "weights", no specific chemistry per cell, no knowledge of what may have changed before it could be frozen for imaging. With a lot of work and a lot of extra physical and chemical simulation (a full body in quite a lot of detail), based on that map and what we know of the rest of its body, openworm achieves wormy wriggling. That's honestly pretty good! And it implies that the connection structure is at least coarsely meaningful in an extremely simple mind, which is what many have suspected but have had no way to verify before. But we know next to nothing about how that individual worm's brain behaved compared to the simulation. The fly brain will be similar - we'll have a bigger and more complex brain to run, more complex behaviors to compare against other flies, etc. It'll provide more evidence in favor of or opposed to the importance of that structure, compared to other things that we don't have enough information on yet. Basically it's still extremely early days, so it's sorta like asking Stonehenge astronomers whether or not our supernova-brightness-based measurements of the size of the universe are accurate. They can see supernovas too, but their answer has to be "uh. maybe? give us a few thousand years to research it". We need more data and better tools than currently exist.
yourapostasy 4y ago
An average cell contains an estimated 100 trillion atoms. I’m guessing to “just” simulate elegans’ 302 neurons at the atomic not to speak of quantum precision, is not within our compute capabilities yet. Until then, we’ll have to content ourselves with rather crude approximations of simulations. I’m not entirely convinced on a gut feel level that the current GPT (multimodal or not) AI will necessarily lead to AGI when we don’t even know how the biological equivalent works to the molecular chemistry level. Though I really hope I’m blind to a possibility and we hit the friendly Culture Minds-like AGI jackpot without having to first understand the biophysical mechanics of our own biological sapience.
- flangola7 4y ago
  I think it's likely we will figure out how to make intelligence in silicon before we figure out how it works in brain matter. Just as there's more than one way of generating electricity there may be many ways of generating thought.
  - Someone 4y ago
    I would guess that, if we ever manage to make intelligence in silicon, we will know how every detail works, but not why the entire thing is intelligent.

by Groxx 4y ago

cyberax 4y ago

OpenWorm is a bad model. To give a computer analogy, you're simulating highly optimized low-level code for Apple ][. That uses all the little hardware-specific tricks and timings to work. A fruit fly brain is a better model to simulate in this regard. It's much more "generic", so there's a hope that we can recapture high-level behavior from it more easily.

tsimionescu 4y ago
> A fruit fly brain is a better model to simulate in this regard. It's much more "generic", so there's a hope that we can recapture high-level behavior from it more easily. This is a completely baseless assumption. It is also moot, since simulating even 300 neurons is beyond our current computational power; simulating 3000 is not going to be possible in the foreseeable future.
- cutemonster 4y ago
  What makes stimulating 300 that computationally heavy? (Doesn't sound like many?, but I've read that one single neuron needed 100 or so nodes in a deep neural net to simulate. But 300*100 also doesn't sound like many (nodes)?) Edit: some explanations here: https://news.ycombinator.com/item?id=35113498 https://news.ycombinator.com/item?id=35113498 (in this discussion)
  - Groxx 4y ago
    Yep, pretty much that thread. Massively simplified models exist, and yeah - we have artificial neutral networks FAR larger than that and they run just fine. But they're so over-simplified that it's fair to call them something else entirely, not a simulation. You can use them to create similar behavior, but they're just following the basic concept of a brain, not how they actually work. Inspired by a real thing, not actually mimicking a real thing. Kinda like how a door hinge is not your knee, even though they both bend. It's not a knee simulation, it's just something that bends. For some things (doors) that's perfectly fine, and there are billions of them in use. You can't transparently replace your knee with one though, it has biology-juice all over it and needs to handle very different behaviors at times.
    - GoblinSlayer 4y ago
      How do you verify precision of the simulation?
      - Groxx 4y ago
        In most cases the vague answer is "how closely can it recreate the end result". Because that's a useful measure. For brains: we have no idea. Every brain is somewhat different, but we only have a couple complete physical layout maps so far. No "weights", no specific chemistry per cell, no knowledge of what may have changed before it could be frozen for imaging. With a lot of work and a lot of extra physical and chemical simulation (a full body in quite a lot of detail), based on that map and what we know of the rest of its body, openworm achieves wormy wriggling. That's honestly pretty good! And it implies that the connection structure is at least coarsely meaningful in an extremely simple mind, which is what many have suspected but have had no way to verify before. But we know next to nothing about how that individual worm's brain behaved compared to the simulation. The fly brain will be similar - we'll have a bigger and more complex brain to run, more complex behaviors to compare against other flies, etc. It'll provide more evidence in favor of or opposed to the importance of that structure, compared to other things that we don't have enough information on yet. Basically it's still extremely early days, so it's sorta like asking Stonehenge astronomers whether or not our supernova-brightness-based measurements of the size of the universe are accurate. They can see supernovas too, but their answer has to be "uh. maybe? give us a few thousand years to research it". We need more data and better tools than currently exist.

yourapostasy 4y ago

An average cell contains an estimated 100 trillion atoms. I’m guessing to “just” simulate elegans’ 302 neurons at the atomic not to speak of quantum precision, is not within our compute capabilities yet. Until then, we’ll have to content ourselves with rather crude approximations of simulations. I’m not entirely convinced on a gut feel level that the current GPT (multimodal or not) AI will necessarily lead to AGI when we don’t even know how the biological equivalent works to the molecular chemistry level. Though I really hope I’m blind to a possibility and we hit the friendly Culture Minds-like AGI jackpot without having to first understand the biophysical mechanics of our own biological sapience.

flangola7 4y ago
I think it's likely we will figure out how to make intelligence in silicon before we figure out how it works in brain matter. Just as there's more than one way of generating electricity there may be many ways of generating thought.
- Someone 4y ago
  I would guess that, if we ever manage to make intelligence in silicon, we will know how every detail works, but not why the entire thing is intelligent.