>It took about 5 thousands rules to be on par with a junior infectious disease doctor if I remember my studies on this subject.
IIRC, MYCIN had several hundred rules. The researchers in this article had to pre-process 130,000 labeled examples. If you see a misclassification in an expert system you can at least backtrack and identify the individual rules that contributed to the failure. AFAIK, systemic errors in training data are much more difficult to detect and fix.
I think people tend to overstate the practical issues with expert systems and understate the issues with deep learning, partly because we have decades of experience with real-life deployments of the former and relatively little experience with the latter.
Yes, the explainability of expert systems is the only thing going for it.
Yes, there are issues with both, but we are really debating different solutions for different problems. For visual recognition, there is no doubt in my mind that deep learning is king.
IIRC, MYCIN had several hundred rules. The researchers in this article had to pre-process 130,000 labeled examples. If you see a misclassification in an expert system you can at least backtrack and identify the individual rules that contributed to the failure. AFAIK, systemic errors in training data are much more difficult to detect and fix.
I think people tend to overstate the practical issues with expert systems and understate the issues with deep learning, partly because we have decades of experience with real-life deployments of the former and relatively little experience with the latter.