Good article, but there is a frequent misconception of Symbolic AI as "manual creation of lots of rules". This was true for early approaches, such as expert systems in the 70s/80s. Symbolic AI just means, well, AI with symbols, and there are many approaches (e.g., in neuro-symbolic AI or using probabilistic inductive logic programming) where symbolic representations are emergent / learned from data using machine learning approaches and can be uncertain/probabilistic.
In the linked talk "From System 1 Deep Learning to System 2 Deep Learning" by Yoshua Bengio, the speaker first criticizes Symbolic AI only to re-invent concepts from Symbolic AI later (e.g., "high level semantic variables", "shared 'rules' across arguments"), which is rather silly given that some Symbolic AI approaches are well capable of learning symbols, rules etc bottom-up - which is not fundamentally different from learning low-dimensional vector representations or "generalizations" in linguistics.
Thanks for the reply. I agree there might be a lot of different approach to to symbolic AI. Probabilistic inductive logic programming sounds interesting but I am not familiar with it. Maybe I should check it out later. I am wondering if it has capability to learning from data.
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[ 6.3 ms ] story [ 23.5 ms ] threadIn the linked talk "From System 1 Deep Learning to System 2 Deep Learning" by Yoshua Bengio, the speaker first criticizes Symbolic AI only to re-invent concepts from Symbolic AI later (e.g., "high level semantic variables", "shared 'rules' across arguments"), which is rather silly given that some Symbolic AI approaches are well capable of learning symbols, rules etc bottom-up - which is not fundamentally different from learning low-dimensional vector representations or "generalizations" in linguistics.