Neural networks are generalizing things as part of their optimization scheme. The current approach is just to dump many layered neural networks (at the core) as in "deep learning" to solve the problems, but the networks are too regular, too "primitive" of sorts. What is needed are network topologies that strongly support this generalization, the creation of "meta abstraction levels", otherwise it will get nowhere.

Biological networks of more intelligent species contain a few billion neurons and upwards from that while even the big LLMs are somewhere in the millions of "equivalent" at best. So, bad topology + much less "neurons" and the resulting capabilities shouldn't be too surprising. Plus it is clear that AGI is nowhere close, because one result of AGI is a proper understanding of "I". Crows have an understanding of "I", for example.

And that is where these "meta abstraction levels" come in: There are many needed to eventually reach the stage of "I". This can also be used to test how well neural networks perform, how far they can abstract things for real, how many levels of generalization are handled by it. But therein lies a problem: Let 2 persons specify abstraction levels and the results will be all across the board. This is also why ARC-AGI, while dives into that, cannot really solve the problem of testing "AI", let alone "AGI": We as humans are currently unable to properly test intelligence in any meaningful way. All the tests we have are mere glimpses into it and often (complex) multivariable + multi(abstraction)layered tests and dealing with the results, consequently, a total mess, even if we throw big formulas at it.