Thanks for reminding us of openworm. The fact that scientists are still working on simulating an animal with barely a thousand cells surely relativizes claims about simulating a mammalian brain, let alone a human one.

Frankly, it's even kind of discouraging.

Meh, evolution is known to write utterly incomprehensible spaghetti code. It's really not surprising that reverse engineering it is so hard.

Write a very simple genetic algorithm to evolve artificial neural networks or computer code. Often it can solve simple tasks in creative ways. But trying to understand the output is usually a nightmare. It usually comes up with ridiculously convoluted and insane ways of doing things. E.g; https://www.damninteresting.com/on-the-origin-of-circuits/

>The plucky chip was utilizing only thirty-seven of its one hundred logic gates, and most of them were arranged in a curious collection of feedback loops. Five individual logic cells were functionally disconnected from the rest— with no pathways that would allow them to influence the output— yet when the researcher disabled any one of them the chip lost its ability to discriminate the tones. Furthermore, the final program did not work reliably when it was loaded onto other FPGAs of the same type.

Now imagine a system many times larger and more sophisticated than that, that evolution has been hacking on for hundreds of millions of years.

Doesn't mean we aren't close to human level AI though. We didn't completely reverse engineer birds before building airplanes, or horses before building cars.

It's one of the reasons I totally do not believe in https://en.wikipedia.org/wiki/Human_Brain_Project

What I do believe is that once openworm achieves its goals that progress might become very rapid indeed.

As a glimmer of hope, consider this: it may be easier to decode a much larger system with redundancies than the highly evolved and tightly optimized 302 neuron system.

The C. elegans has a small number of heterogenous neurons. Some are quite task specific.

The human brain, on the other hand, has ~100billion. While they do not all have the same structure, within specific regions you will see repeated patterns of homogeneity and redundant encodings.

It may be a harder problem to start with the small system than the large one.

I think it's more important to choose the right level of detail for what you're modelling. This is covered in "The Use and Abuse of Large Scale Brain Models" [1].

[1] https://github.com/arvoelke/delay2017/blob/master/delay2017....