Aren't we close to this? Most insects only have a few million neurons in their central nervous system, so we can model their intelligence in real time at least. Maybe we still lack the tools for training such networks into useful configurations?
Neurons communicate with each other with a multitude of neurotransmitters and receptors [1]. As a cell, each neuron is a complex organism of its own that undergoes transcriptomic and metabolic changes. We aren't even close to simulating all protein interactions in a single cell yet, let alone in millions of them.
Of course you could say that full protein simulation of an entire brain is not neccessary if we can build an accurate enough technical model of a single neuron. In fact, already now we have to apply a model of how we believe proteins behave as "properly" simulating interactions of two proteins (or one with itself) with lattice QCD approaches is beyond our computational capabilities. For protein interaction we have pretty good models already. But finding a model of all types of neurons in insect brains is right now an open, unsolved challenge.
[1] https://en.wikipedia.org/wiki/Neurotransmitter#List_of_neuro...
AFAICT this suggests that we have the computational power but wouldn't it also be a significant challenge to create an accurate model for the brain simulation?
I think people think back propagation is the metaphorical lift equation here and we just need a “manufacturing” advancement (ie, more compute and techniques for using it). We’re close to that (I personally feel like with poor evidence) but definitely not there yet (as evidenced by nobody publishing this). We cannot describe what is happening with modern architectures as fully as a lift equation predicts fixed wing flight, and so it is largely an intuition + trial and error, which is a slow unreliable way to make progress.
People think maybe the missing pieces might be in the other things we don’t understand about the brain. It makes sense- it does what we want, so the answer must be in there somehow. I agree we don’t need to perfectly understand it, it just seems like a good place to keep looking for those missing pieces.
Secondarily, just because neurons are complex on technical level, it does not mean that they should be complex on logical level.
For example, in computers if you would look at the CPU structure, on a low level you have quantum effects and tunneling and very insane stuff but on a logical level you are dealing with very trivial boolean logic concepts.
I would be not be surprised in a slightest if copying and reverse engineering neurons per se would not be necessary and defining aspect of anything related to AGI.
OP probably just wanted to downplay the current state of AI.
Now here's a more advanced example to teach a virutal character how to flex in the gym: https://www.youtube.com/watch?v=kie4wjB1MCw
That's a bit more advanced than simple walking.
Here's a deployed AI to a real robot "crab":
https://www.youtube.com/watch?v=UMSNBLAfC7o
How about virtual characters learning to cooperate?
In one of your examples, which are all of narrow AI, we see a mechanical crab powered by ML that has become specialized in walking with a broken limb, which is not even close to what we need if we aim for AGI. For AGI we don't need agents that mimic simple behavior. In my opinion, _mimicking_ behavior will not lead to AGI.
What _will_ lead to AGI? No one knows.
Nowhere I have stated this is the clear path to AGI and you are right, we are missing key building blocks. But I feel like there's too much skepticism agains this field while the advancements are not appreciated enough.
I don't know either what will lead there, but I see more and more examples of different networks being combined to achieve more than they are capable of individually.
No, the complaint was about modelling the behavior of simple organisms.
Certainly we can model some of their behaviors, many of which are highly stereotyped. But the real fly (say) doesn't only walk/fly/scratch/etc, it also decides when to do all of these things. It has ways to decide what search pattern to fly given confusing scents of food nearby. It has ways to judge the fitness of a potential mate, and ways to try to fool potential mates. Our simulations of these things are, I think, really terrible.
Since everything here is loosely defined I feel it's totally pointless to discuss AI, but it's still an intriguing topic. If you look at those insects, they tend to follow brownian motion in 3D, get food and get confused by light, we can get an accurate model of them and more [0].
The key word here is to model, not replication. Simulations are just that, simulations. Given current examples of what's already possible if someone wanted to, could model a detailed 3D environment with physics, scents and food for our little AI fly.
[0] https://www.techradar.com/news/ai-fly-by-artificial-intellig...
Is that a terrible attempt?
I'm sorry and apologize if you feel I was one to kill the discussion you wanted to have around AI.
I'm one of those dreamers who think AGI is or at least should be possible, soon, through means we have not yet discovered but will, soon. I base that on absolutely nothing, I suppose, other than the fact we have lots of "bright/smart/crazy" devs working on it. It's my own personal "believie", as Louis CK would say about things we believe in but cannot or care not prove.
Just like you I'm looking at organisms much simpler than us as a way forward. Many specialized neural network does not make up AGI, is what I think. Is it the organic and human neuron we should model? I don't necessarily think so. Also. robotics + ML is a dead end to me. An amoeba that can evolve into something more complex, is perhaps what we should model.
> they tend to follow brownian motion in 3D
Well, their entire neural system exists to make deviations from Brownian motion. That's the whole point of being an animal not a plant. And doing it well is very very subtle.
First steps towards modelling such behavior can be super-interesting science, not a terrible use of time at all. They can capture a lot of truth about how it works. But like self-driving cars, the thing that kills you is usually a weird edge case, not the basic thing.