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I disagree. Data isn't magic fairy dust, and romaniv is correct -- plenty of expert systems-based approaches did perform well in practice. Many of the barriers

I disagree. Data isn't magic fairy dust, and romaniv is correct -- plenty of expert systems-based approaches did perform well in practice. Many of the barriers standing in the way of wide-spread use diagnostic software are not technological in nature. Ignore those barriers at your own risk.

lucidrains 10y ago
I am assuming you are referring to the expert system used for infectious diseases. They performed well, but could not adapt to the constantly changing guidelines, medications. Data input was inefficient back in the days of terminals. It took about 5 thousands rules to be on par with a junior infectious disease doctor if I remember my studies on this subject. The deep neural net visual diagnostics are different. They are learned on pure pixels as we would from photons striking our retina, signals traveling all the way back to our visual cortex for learning. There is no assembling of thousands of rules here and therefore the system is less brittle. These new systems get more powerful with more data given to them. Expert systems required humans to craft rules from the data, and therefore required constant maintenance and is fallible to human error. So yes, some of the barriers are definitely technological in nature.
- romaniv 10y ago
  >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.
  - lucidrains 10y ago
    Oh yea, I'm confusing it with INTERNIST. https://en.wikipedia.org/wiki/Internist-I https://en.wikipedia.org/wiki/Internist-I 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.

lucidrains 10y ago

I am assuming you are referring to the expert system used for infectious diseases. They performed well, but could not adapt to the constantly changing guidelines, medications. Data input was inefficient back in the days of terminals. It took about 5 thousands rules to be on par with a junior infectious disease doctor if I remember my studies on this subject. The deep neural net visual diagnostics are different. They are learned on pure pixels as we would from photons striking our retina, signals traveling all the way back to our visual cortex for learning. There is no assembling of thousands of rules here and therefore the system is less brittle. These new systems get more powerful with more data given to them. Expert systems required humans to craft rules from the data, and therefore required constant maintenance and is fallible to human error. So yes, some of the barriers are definitely technological in nature.

romaniv 10y ago
>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.
- lucidrains 10y ago
  Oh yea, I'm confusing it with INTERNIST. https://en.wikipedia.org/wiki/Internist-I https://en.wikipedia.org/wiki/Internist-I 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.