>> Does no one remember why expert systems fell apart? There were many reasons. One of them was the "Knowledge Acquisition Bottleneck", but that was not about

>> Does no one remember why expert systems fell apart? There were many reasons. One of them was the "Knowledge Acquisition Bottleneck", but that was not about the cost of paying experts, rather the cost of creating and maintaining a potentially very large knowledge base (i.e. one big mother of a database of production rules). Also, the fact that many experts' knowledge is tacit and not easily formalisable. Modern machine learning began in the 1980's as an effort to overcome the Knowledge Acquisition Bottleneck. Accordingly many early machine learning approaches were designed to learn production rules for expert systems. Decision trees, one of the staple classifiers in data science, come from that era; you can tell, because decision trees are a symbolic, logic-based "model". There were other problems with expert systems, e.g. their infamous "brittleness". But modern, statistical machine learning systems, are also criticised for "brittleness" too. There were also purely political reasons and nothing to do with science or technology considerations. Then there was the 5th Generation Computer Project, and the AI winter, and then there were no more expert systems. The journal of Expert Systems with Applications is still alive and well, on the other hand, although it mostly publishes on machine learning and neural nets these days. With an occasionally cool article, like one about Wolf Colony Optimisation I spotted recently. Too tired to look for links now, sorry.