> Our main goal is to build the world's first virtual organism - an in-silico implementation of a living creature - for the purpose of achieving an understanding of the events and mechanisms of living cells.
This blog post is interesting: "Whole Brain Emulation: No Progress on C. elegans After 10 Years" https://www.lesswrong.com/posts/mHqQxwKuzZS69CXX5/whole-brai... As other mentioned in the comments of the post, it is certainly also a matter of funding, but still I think it is very interesting how we still struggle to simulate a 302 cell worm, while some people expect an artificial superintelligence in the next ten years.
Edit: I am surprised at the downvotes. In general, we learn from the nature, but aping it usually proved too difficult and often unpractical at the same time. Do we really want to replicate worm intelligence for practical purposes, or do we want something different?
I would say that a machine which can, say, analyze chemical compounds for their potential biological functions, is a very practical form of "intelligence" and yet very far from any biological intelligence that was ever produced in vivo. Worms cannot do that and even humans struggle with such tasks.
This is not true for how this 302 cell organism works. We don't know and struggle to understand. That's actually the reason why the project exists. To find out how everything works with an bottom-up approach.
While we may find shortcuts or even superior forms of intelligence without understanding how intelligence works in biological creatures, it is still curious how we struggle even with a "simple" organism like C.elegans.
And most of the details of bird flight were not exactly discovered till well after commercial air travel was commonplace.
We still don't know how cells in C. elegans work together. It's neither visible nor explainable on a satisfying level.
The Wright flyer didn't flap, and the wings only superficially look like anything a bird has.
That is true and it shows even more how important observabilty is for science and engineering. That's also why a simulation that actually provides an accurate enough model of reality might help us so much. The problem with AI right now is, that we try or even claim to understand Unix by mimicking the functionality of transistors.
> The Wright flyer didn't flap, and the wings only superficially look like anything a bird has.
They tried to mimick bird wings when coming up with flight control mechanisms.
https://youtube.com/watch?v=eaYIU6YXr3w?t=106
I’m not sure the timestamp works but it’s at 1:46
When it comes to brains, I don't know if anyone knows what might be the simplest sufficient model that would usefully replicate them, even if you specify "usefully" well enough to know if this is about fundamentals of intelligence or about the impact of drugs on cognition, which are two completely different standards.
For example, perceptrons are a toy model, but modern AI can do more in (breadth XOR single-skill performance in various domains) than any single human, even with much smaller parameter counts than we have synapses; but the broad-skilled ones also mess up in inhuman ways, like being equally good at advanced calculus as basic arithmetic, or being a poet at the level of stereotypical teenager but in every language simultaneously.
If anyone's made a neutral network that can get high on simulated caffeine — and I'm not saying it hasn't been done — it's not reached any of the places I follow discussions on this kind of thing. (Google didn't help, results were about software named Caffeine and non-artificial neurones).
Cells duplicate, but can you make a cell without splitting one in 2?
We're physics too, but we don't know how it all fits together.
If a sub-part of us that knows less than we do can make a copy of us, despite not knowing how it all works, that's an existence proof that we don't need to understand how it all works to make a copy of us.
But strictly speaking, as we understand it, it's not possible to replicate something exactly without recapitulating the exact laws and running a deterministic simulation, which is not practical.
I don't think anybody is really attempting to exactly replicate things, but rather to create a physical model which can be calcualted and contains enough similarity or transferrability to be able to make accurate generalized predictions about the behavior of the simulated system. How and why that works with modern math methods is still somewhat mysterious. The most useful thing written about that so far is https://en.wikipedia.org/wiki/The_Unreasonable_Effectiveness...