[DennisP]

By "experimental data" do you mean something like "this had a pattern of neural activity that looks like what we've observed in biology," or do you mean "we tried driving a self-driving car with this method and it worked really well." The latter is more what I'm talking about, whether it's robotics, image classification, game playing, etc.

[FakeRemore]

You do realize you can't just jump straight into "Let's see if this thing can drive a car", right? There's years of research and development that's going to happen before anything like that. And it's not like they're designing this to get a car-driving AI. They're trying to find an accurate model of the brain. Maybe, if results are promising, this can be turned into something like that in 10-20 years, but I wouldn't count on it. Maybe sooner if it turns out to be particularly promising. Chances are this is going to radically evolve in different directions. They might hit dead-ends. They might make valuable insights about how some parts of the brain work, but can't generalize it or go from there to a general problem solving intelligence. There are all manner of problems, and I don't think you realize how complex the brain actually is when you're starting from first principles like this. They're at the level of simulating what individual neuron clusters do. That's like looking at electrons interacting and expecting to build a bridge by manipulating them individually.

[DennisP]

I do realize, but I'm not saying let's try to jump right into general intelligence. Deep neural networks do all sorts of reasonably smart things right now, including all the things I mentioned. Seems to me we are at a point where we can test neural models to see whether they actually perform functions useful to an organism.

[FakeRemore]

The first neural networks appeared in the 50s (with significant limitations), and proper research into them and appropriate funding first started in the 80s. NN's didn't become a thing overnight, and neither will this. I'm not sure why you're expecting this thing to be proven now. Science is slow. It takes time to build evidence and "prove" things and figure out how useful a model is.

[DennisP]

Perceptrons were applied to image recognition tasks right from the beginning: https://en.wikipedia.org/wiki/Perceptron#History

Applying them to real tasks revealed their capabilities and limitations, and when computers got better people took it from there.