Maybe, maybe not. I'm not sure if it's just a matter of scale or of the fundamental nature of the thing. It might be that the computer / neural network metaphor, whilst isomorphic to certain brain procesees, may ultimately not tell us very much about the larger system and what it actually is.

I tend to think that because computers are the current peak of civilisation / engineering, and in some ways, "alive", we think we might be like a computer. In the Victorian machine age they talked of humans as machines. Maybe some, hitherto unthinkable invention is around the corner, and we will say humans / brains are like that too.

No, the difference is we can understand and apply knowledge in different contexts. I'm not saying machines will never mimic that (GPT3 is getting kinda close at times), but as my good friend Dewayne Perry (co-author of the most cited paper in SW engineering and Motorola Regent Chair at UTexas) says, "Artificial intelligence has a long way to go just to catch up to natural stupidity!" Try getting Alexa (famously trained daily by millions) to do anything in anything other than the predefined prescribed way. I'd wait, but I've got a life to live...

> it's nothing more than a ultra-scaled neural network as we currently use them

You state this as if it is an undisputed/obvious fact, but it isn't.

It does not matter for my point. Even if our NNs miss some part of neuron function, it would look comparable to a simple if/a multiplication on the smallest level. My point is that the complexity/intelligence comes from size; just because our algorithms adapt minimally or accept pre-defined scenarios, does not mean that they are fundamentally unintelligent because we're able to fully understand them.