The second part can sometimes be problematic That's exactly where everything goes dark. Applying AI to understand troves of persistent/temporary data archive

The second part can sometimes be problematic That's exactly where everything goes dark. Applying AI to understand troves of persistent/temporary data archive threads in order to learn and reveal patterns and associations in complex datasets of patient encounters can help doctors prevent expensive unnecessary lab tests that need not happen again for another patient. That's conveniently doable for human-computer interaction. The viscosity of natural language seems to be the insurmountable challenge here. Based on what I've experienced, visual perception by computers and translation between natural languages can permanently earn its success in a matter of time's length less than a decade. Not just speech-to-text because we still have to trick Siri for example by speaking slowly. It's probably my angle, but it is not a qualifying success when we all yet have a lot of work underway to do.