Biological neurons have many features like active dendritic compartmentalization that perceptrons cannot duplicate.
They are different with different advantages and limitations.
We have also known about the specification and frame problems for a long time also.
Note that part of the reason for the split between the symbolic camp and statistical camp in the 90s was due to more practical models being possible with existential quantification.
There have been several papers on HN talking about a shift to universal quantification to get around limitations lately.
Unfortunately discussions about the limits of first order logic have historical challenges and adding in the limits of fragments of first order logic like grounding are compounded upon those challenges with cognitive dissonance.
While understanding the abilities of multi level perceptrons is challenging, there is a path of realizing the implications of an individual perceptron as a choice function that is useful for me.
The same limits that have been known for decades still hold in the general case for those who can figure a way to control their own cognitive dissonance, but they are just lenses.
As an industry we need to find ways to avoid the traps of the Brouwer–Hilbert controversy and unsettled questions and opaque definitions about the nature of intelligence to fully exploit the advantages.
Hopefully experience will tempor the fear and enthusiasm for AGI that has made it challenging to discuss the power and constraints of ML.
I know that even discussing dropping the a priori assumption of LEM with my brother who has a PhD in complex analysis is challenging.
But the platonic ideals simply don't hold for non-trivial properties, and no matter if we are using ML or BoG Sat, the hard problems are too high in the polynomial hierarchy to make that assumption.