Sure. It is rather complex to be honest but I'll do my best to give a high level abstract. I should first off explain that there's no clear definition of intelligence. You might see some of my other comments use the term HLI, which is Human Level Intelligence. It is a term gaining in popularity due to this difficult to define definition and allows for more a porn like definition -- I know it when I see it.
Philosophers, psychologists, neuroscientists, and many others have been contemplating the definition of intelligence for centuries and we're still not quite there. But we do have some good ideas. One of the important aspects is the ability to draw causal relationships. This is something you'll find state of the art (SOTA) text to image (T2I) generators fail at. The problem is that these models learn through a frequency based relationship with the data. While we can often get the desired outcome through significant prompt engineering it still demonstrates a lack of the algorithm really understanding language. The famous example is "horse riding an astronaut." Without prompt engineering the T2I models will always produce an astronaut riding a horse. This is likely due to the frequency of that relationship (people are always on horses and not the other way around).
Of course, there are others in the field that argue that the algorithms can learn this through enough data, model scale, and the right architecture. But this also isn't how humans learn and so we do know that if this does create intelligence it might look very different ("If a lion could talk I wouldn't be able to understand it"). Obviously I'm in the other camp, but let's recognize that this camp does exist (we have no facts, so there's opinions). I think Chomsky makes a good point: our large language models have not taught us anything meaningful about language. Gary Marcus might be another person to look into as he writes a lot on his blogs and there's a podcast I like called Machine Learning Street Talk (they interview both Marcus and Chomsky but also interview people that have the opposite opinion).
So another thing, is that machines haven't shown really good generalization, abstraction, or really inference. You can tell a human a pretty fictitious scenario and we're really good at producing those images. Think of all the fake scenarios you go through while in the shower or waiting to fall asleep. This is an important aspect of human intelligence as it helps train us. But also, we have a lot of instincts and a lot of knowledge embedded in our genetics (as does every other animal). So we're learning a lot more than what we can view within our limited lifetimes (of course our intelligence also has problems). Maybe a good example of abstraction is "what is a chair." There's a whole discussion about embodiment (having a body) and how language like this may not be able to be captured in the abstract manner without a body since a chair is essentially anything that you can sit on.
I feel like I've ranted and may have written a mess of a lot of different things haha. But I hope this helps. I'll try to answer more and I'm sure others will lay out their opinions. Hopefully we can see a good breadth of opinions and discussion here. Honestly, no one knows the answers.