GPT and most modern "AI" approaches suffer from the same problem: they're just brute force algorithmic approaches that don't mimic "intelligence" in any way. Since there is no understanding, they can't learn in the same way humans or other living creatures do (in general terms, by extrapolation).
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.
That's a bold claim to make, given we ourselves don't even understand what understanding, or intelligence is. So how can you support your claim that what GPT-3 does is not intelligence.
Personally I think our common definitions of intelligence are too narrowly defined, we have a bias towards recognizing intelligence similar to our own and a blindspot for intelligent systems unlike our own. I agree with Paul Stamets that mycelia networks may have a form of intelligence quite unlike our own. Large corporations likely have intelligence of their own ('slow AIs') and may in aggregate pursue goals that no individual participant in the organization agrees with (preference falsification demonstrates how such a thing might occur.) Closer to the traditional, I think reptiles may be far more intelligent that we usually give them credit for since we have a mammalian bias against their kind. Mammalians seem to have empathy circuits that fire more often for other mammals, and this inclines us to not consider the possible intelligence of non-mammals.
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.
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.