Gary Marcus

The real questions are Q1. Exactly how do we get DL systems to learn to reason? Q2. How do use self-supervised learning to get machines to learn abstract representations of the world (call them symbols if you wish, but really patterns of activity of neural nets, aka vectors)?

Now, whether we actually agree or disagree depends entirely on the details of the answers to these Qs. Hence the pointlessness of the discussion and the necessity to work on answers. I've listed these Qs as the most important ones in AI in all my talks of the last 5 years....

...But they have existed for a very long time: since the early 90s for Q1 and since the early 80s for Q2. Now that the DL machinery works, and that so many people are working on both Qs, we have a shot at making real progress.

I guess the remaining question for your position are: GQ1. Will DL be part of the solution (you said yes) GQ2. Do you agree with "vectors, not symbols; diff functions, not hard logic" GQ3. If not, how do you propose we make reasoning compatible with DL?

GQ1. DL is part of the solution GQ2. "vectors, not symbols” is false dichotomy diff functions: yes, in part Operations a la logic, we do need (contra your view) GQ3. Outputs of deep learning may serve as input to reasoning; symbolic techniques needed for some inferences.

Most humans don't actually do much that resemble your answer to GQ3, except a small number of humans using pen and paper, and only in the last couple of millennia. Right now, we need to get machines to the level of a house cat. Never mind symbolic mathematics and formal logic.

The entire field of generative linguistics would beg to differ. The standard presumption there is that our comprehension of language revolves around manipulations of strings of structured symbols.

Yes, and that's precisely what's wrong with it. That view has been a complete and utter failure in NLP. That was the main point of Hinton's remarks. Noam Chomsky's birthday was yesterday. Geoff Hinton's was the day before yesterday.