5

First of all, I would like to point out the main differences between knowledge base and (Deep) machine learning, specially when the main focus is on "AI" not "Data Science": NNs are like a black box; Even if they learn a dataset and gain the power of generalization over the problem domain, you'd never know how they are working. if you scrutinize the details ...


3

Sure! There's the whole Semantic Web scene! OWL is derived from DLs and Frames, arguably has a lot in common with semantic networks too. Expert-driven decision support systems are still being developed (and researched) in industries where the human is required to take responsibility or getting data is not going to happen. As the ideas evolve so do the names. ...


3

Oh yeah, definitely. Just to pick one example, you have Douglas Hofstader's group at Indiana. I think most of what they do would fall under the rubric of GOFAI (or at least closer to that than the statistical machine learning stuff). Beyond that, just go to the CORR and browse around the AI category. You'll see plenty of neural networks and ...


3

Although asked over 3 years ago, the question is still interesting and while I agree with the original answer, a lot can be added to it. First, I'd like to point out that the term "knowledge base" is very ambiguous and it means different things to different people. For example, there is no sharp distinction between knowledge base and neural network....


2

Sure! This is a somewhat hot area right now. There are lots of ways to do it. Probably the main line of research is with Bayesian Networks (1980's) and Casual Networks (1990's). These are basically rule-based systems for reasoning probabilistically. They rely on a user-designed model, which corresponds well to rules (e.g. when blood pressure is high, then ...


1

Tariq's comment hints at this, but this is still in some sense a very mainstream idea. Check out the transcripts for this year's Loebner Prize. The winner (called Tutor) is again making use of Eliza-like deflections. Some of the other candidates try to use seduction (Columbia), or speaking in a flighty or insistent way (like 2017's Rose). In all cases, ...


1

A neural net with even a single hidden layer is capable of Universal function approximation - it can approximate any continuous function 'as closely as you like'. Hence, one option would be to look for GOFAI applications that would benefit from this property - for example, in state-space search approaches where the utility of a state is not readily defined ...


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