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> the system can produce explanations That is the killer edge that rule-based systems can offer. You can question them about how they reached their conclusions

> the system can produce explanations That is the killer edge that rule-based systems can offer. You can question them about how they reached their conclusions; armed with the response, you can evaluate their reasoning and critique their conclusions. Explanations aren't beyond the reach of ML systems in principle, AFAIAA, but I've yet to hear of any proposal about how one might construct a self-explaining ML system. [Edit] I guess not being able to explain your reasoning might be considered an advantage, in some circumstances; if you can't show that a certain system is using defective reasoning (because it won't show you its reasoning), you can't show the system is defective, so it becomes harder to sue the manufacturer or relying party.

idealmedtech 5y ago
We're a medical device startup using a KB/RBR system for glucose control, and our greatest feature from a traceability/regulatory perspective is the ability to produce a report for each and every decision made; why we made that decision based on what we know, and how that decision affects the output of the system. This can also be applied retroactively; so all we need to know is the version of the system employed and the sensor inputs given and we can fully replay the decision process. This is something that, as you state, you simply cannot do with a deep learning approach. Regulators don't love black boxes, especially when it comes to human lives. Is it highly specific and non-transferable? Yes. Did it take a decade of work to refine? Yes. But does it get the best results, even better than classical MPC/PID approaches? So far, yes. In medicine, it's results that matter, not reusability of the tech employed. Human trials are just around the corner, and we can find out with certainty if our approach gives the results we think it will.

idealmedtech 5y ago

We're a medical device startup using a KB/RBR system for glucose control, and our greatest feature from a traceability/regulatory perspective is the ability to produce a report for each and every decision made; why we made that decision based on what we know, and how that decision affects the output of the system. This can also be applied retroactively; so all we need to know is the version of the system employed and the sensor inputs given and we can fully replay the decision process. This is something that, as you state, you simply cannot do with a deep learning approach. Regulators don't love black boxes, especially when it comes to human lives. Is it highly specific and non-transferable? Yes. Did it take a decade of work to refine? Yes. But does it get the best results, even better than classical MPC/PID approaches? So far, yes. In medicine, it's results that matter, not reusability of the tech employed. Human trials are just around the corner, and we can find out with certainty if our approach gives the results we think it will.