The ironic thing is that ML became popular because we didn't have the technology back then to properly model actual neuronal activity. Now we do, but ML is so established that modeling "true" intelligence is no longer de jure.

So, GPT? I think that's probably a form of intelligence too, but not one that's particularly similar to our own. I also doubt that we'll have much luck getting it to ever perform at near-human levels, when you consider the data and power requirements. I think it's not simply a matter of silicon vs wetware; I think the GPT approach is substantially more different from our own than the hardware it runs on. It's a form of intelligence but that doesn't mean it is like our own.

https://www.technologyreview.com/2020/08/22/1007539/gpt3-ope...

That's not a fair comparison. The human neural network is not trained _from scratch_. There is a very good fundamental structure we inherited through millions of years of evolution.

It is worth noting that the training data used for training humans has been undergoing it's own optimization process for a while too (arguably for about 40ky, since the Upper Paleolithic Revolution, which is roughly when cultural evolution started taking over). In AI adjusting the training data is called Curriculum Learning. Right now most curriculum learning is done just by adjusting the order of training samples, rather than creating samples specifically optimized to facilitate learning (although GANs might be considered a Socratic approach to the latter, if you squint at it).

Instead, as you pointed out, it's just proof of how efficient our wetware is.