It really depends on what level of abstraction you care to simulate. OpenWorm is working at the physics and cellular level, far below the concept level as in most deep learning research looking to apply neuroscience discoveries, for example. It’s likely easier to get the concepts of a functional nematode model working or a functional model of memory, attention, or consciousness than a full cellular model of these.
More specifically, a thousand cells sounds small in comparison to a thousand layer ResNet with millions of functional units but the mechanics of those cells are significantly more complex than a ReLU unit. Yet the simple ReLU units are functionally very useful and can do much more complex things that we still can’t simulate with spiking neurons.
The concepts of receptive fields, cortical columns, local inhibition, winner-take-all, functional modules and how they communicate / are organized may all be relevant and applicable learnings from mapping an organism even if we can’t fully simulate every detail.
Present day ANNs may well be inspired by biological systems but (as you noted) they're not even remotely similar in practice. The reality is that for a biological system the wiring diagram is just the tip of the iceberg - there's lots of other significant chemical things going on under the hood.
I don't mean to detract from the usefulness of present day ML, just to agree with and elaborate on the original point that was raised (ie that "we have a neural wiring diagram" doesn't actually mean that we have a complete schematic).
On the other hand, assuming no errors in implementation then a broken simulation which you had expected to work directly implies that your understanding is flawed.
As I interpret GP, the claim is you can't describe something in sufficient detail to simulate it, then you don't actually understand it. You may have a higher-order model that generally holds, or holds given some constraints, but that's more of a "what" understanding rather than the higher-bar of "why".
It seems that they are saying that a simulation is required for proof. We write proofs for things all the time without exhaustively simulating the variants.
I never claimed that a simulation is required for proof, just that an unexpectedly broken (but correctly implemented) simulation demonstrates that the model is flawed.
Do you ensure this by simulating it?
Take for example cryptographic primitives. We often rely on mathematical proofs of their various properties. Obviously there could be an error in those proofs in which case it is understood that the proof would no longer hold. But we double (and triple, and ...) check, and then we go ahead and use them on the assumption that they're correct.
I'm not the previous poster, but how about the Halting Problem? The defining feature is that you can't just simulate it with a Turing machine. Yet the proof is certainly understandable.
It's a tired analogy but we can understand quite a lot about flight and even build a plane without first birthing a bird.
The problem is we don't know if we're attempting to solve something as "simple" as flight with a rudimentary understanding of airflow and lift, or if we're attempting to achieve stable planetary orbit without fully understanding gravity and with a rudimentary understanding of chemistry.
I think it's still worth trying stuff because it could be closer to the former, and trying more stuff may help us better understand where it is on that spectrum, and because if it is closer to the the harder end, the stuff we're doing is probably so cheap and easy compared to what needs to be done to get to the end that it's a drop in the bucket compared to the eventual output required, even if it adds nothing.
Or fully understanding fluid dynamics and laminar flow. No doubt that the Wright Brothers didn't fully grok it, at least.
Minimally, whatever the complexity inside a Biological neuron maybe, one fundamental propery we need to obtain is thr connection strengths for the entire connectome, which we don't have. Without that we actually don't know the full connectome even of the simplest organisms, and no one to my knowledge has hence actually studied the kind of algorithms that are running in these systems. I would love to be corrected here of xourse.
(Of course we might still learn useful things from such a model, I just want to be clear that it wouldn't in any sense be a complete one.)
It isn't enough to flip switches on and off, and to recognize weights, or even to take a fully formed brain network and simulate it. You have to understand how it developed, what it reacts to, how body shapes mind shapes body, and so on and so forth.
What we're doing now with NN's is mistaking them for the key to making an artificial consciousness, when all we're really playing with is the ML version of one of those TI calculators with the paper roll the accountants and bookkeepers use. They are subunits that may compose together to represent xmcrystalized functional units of expert system logic; but they are no closer to a self-guided, self-aware entity than a toaster.
I mean, I know that I'm conscious. Or at least, that's how it occurs for me.
But there's no way to experience another's consciousness. So behavior is all we have. And that's why we have the Turing test. For other people, though, it's mainly because they resemble us.
The article starts out "At the Allen Institute for Brain Science in Seattle, a large-scale effort is underway to understand how the 86 billion neurons in the human brain are connected. The aim is to produce a map of all the connections: the connectome. Scientists at the Institute are now reconstructing one cubic millimeter of a mouse brain, the most complex ever reconstructed."
So the article is about starting with the wiring diagram and working up. My point is that, even where we already have the wiring diagram for an biological neural system, simulating what it does is just barely starting to work.
and just looking at the way they dance around - they're in motion, they're changing their connections, they're changing shape - is so entirely unlike the software idea of a neural network that it makes me really doubt that we're even remotely on the right track with AI research
I'm definitely excited to start working on this in a couple of years! My hopeful guess is that observing the full nervous system while it's firing full throttle is the only way to start understanding the algorithms that are running there. And from there hopefully we can start finding patterns!
Needless to say I agree with you. The people who say they have a wiring diagram of the mouse brain need to reign in their enthusiasm. We are not anywhere close to start understanding even a fly or zebrafish brain leave alone a mouse or human one. Sydney Brenner himself agreed (though he's arguably biased towards nematodes).
I don't think you understand the depths of what a worm is. Is there any worm created by humans without using previous worm material?
For a concrete example, consider the "modbyte tuning"[1] in the 32byte game of life.
[1]: http://www.sizecoding.org/wiki/Game_of_Life_32b#Modbyte_tuni...
Now here is how life does it: you have a program that outputs prime numbers, you then have to change it into a sorting algorithm, then in a fizzbuzz, and then in a game of life. You are not allowed to refactor or do anything from scratch. If the program become too big, you are allowed to remove random instructions.