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.

> 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.

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 (in this discussion)

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.

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.

How do you verify precision of the simulation?

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.

The problem is not the neural simulation, but that we need to simulate pretty much all of the worm. And we currently can't do that.

The neurons in worm directly interact with muscle cells, for example, via peptide/monoamine signalling. Or with "remote" neurons.

This is all possible because neural network of the worms is directly integrated with everything. And the connectivity is hard-coded in its genome.

It's not true for more complex organisms, simply because you can't encode that kind of complexity in DNA. Instead it encodes the overall connectivity structure, so that emergent behavior provides necessary instincts.

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.

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.

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.