But DL nets can be very fragile solutions, that only curve fit to the training data.
You dont want to recognize the same picture of the puppy, you want to recognize all puppies.
Others who disagree are trying to integrate logical approaches with DL into novel archetectures.
I see it as more of pendulum moving back and forth.
But Pearl's work is in Causal Statistics and is a hard core Bayesian.
https://www.quantamagazine.org/to-build-truly-intelligent-ma...
It's not like his complaints aren't being addressed in the field, they are just not easy problems (transfer learning, world models)
In a neural analogy, you don't haven't built a brain, you have a reflex.
Machine learning is rapidly losing its ability to innovate as a field and the reason is the extreme focus on deep learning for classification- and secondarily the fact that the billions of corporate funding now attract researchers from the sciences who have no background in computer science or AI and haven't got a clue what the fuss is about that people like Perl are making.
Here it is from Hinton himself:
One big challenge the community faces is that if you want to get a paper published in machine learning now it's got to have a table in it, with all these different data sets across the top, and all these different methods along the side, and your method has to look like the best one. If it doesn’t look like that, it’s hard to get published. I don't think that's encouraging people to think about radically new ideas.
Now if you send in a paper that has a radically new idea, there's no chance in hell it will get accepted, because it's going to get some junior reviewer who doesn't understand it. Or it’s going to get a senior reviewer who's trying to review too many papers and doesn't understand it first time round and assumes it must be nonsense. Anything that makes the brain hurt is not going to get accepted. And I think that's really bad.
https://www.wired.com/story/googles-ai-guru-computers-think-...
You'd probably be best served by reading Pearl himself, his recent Book of Why is good, this much earlier paper[0] kind of lays out a few things too as for his reasoning that causal inference needs different tools. There's also some stuff in the blogosphere about an earlier dispute you might find fun, i.e. the methods of using causal inference tools. Example[1].
[0] https://ftp.cs.ucla.edu/pub/stat_ser/r284-reprint.pdf
[1] https://statmodeling.stat.columbia.edu/2009/07/05/disputes_a...