You can't understand why without considering the environment, the problem is that it's too expensive to train AI agents in reality and simulated environments are too simplistic.
But if such a simulated environment is available, agents can learn general skills.
> Deep mind: Generally capable agents emerge from open-ended play
https://deepmind.com/blog/article/generally-capable-agents-e...
You can't understand why without considering the environment, the problem is that it's too expensive to train AI agents in reality and simulated environments are too simplistic.
But if such a simulated environment is available, agents can learn general skills.
> Deep mind: Generally capable agents emerge from open-ended play
https://deepmind.com/blog/article/generally-capable-agents-e...
In this kind of setup the agents know why they act like they do.
Once this is clear, the discussion becomes healthier: linguists continue to try and understand fundamentals; ML NLP continues to try to get impressive, possibly lucrative results. Logical research can borrow from ML results, or re-use them, and so forth, but I agree with Chomsky that it can't be a shot in the dark always, because once you deploy it and pass it some tough decisions to make, well, what then?
I was pleased to see at least one idea, the recursive structures found in his theories, which is increasingly being accepted by people like Hinton, so that's great.