I read an interesting article on the use of machine learning to solve problems and it’s use in predictions.
When I was in college I spent a lot of time researching AI. At that time the focus was on developing expert systems. Processing power was expensive so to help machines be it in machine vision, speech or in computable problems much of the effort was in developing short cuts.
Today machine learning and AI is changing that.
Machine learning will replace the “human” element of defining the algorithm to define and solve the problems in more and more domains.
we have moved from trying to solve problems using “expert systems” as this was the focus previously. We would try to develop systems to replicate the heuristics used by “experts” but these failed not only because of the declining cost of computing but also because the models used to try to replicate human experts were faulty. We didn’t understand enough about neuroscience to understand how we developed expertise.
Allowing machines to learn and find the patterns is more efficient than asking people in many cases now.
It takes time for the human mind to find the patterns. The challenge will still be to determine the data set to feed the machine.
The interesting point is in solving creative problems, the human mind takes feeds from many more sources intuitively. What we learn to accept or ignore is interesting. Think of Archimedes and his Eureka moment. Would an AI have learnt to solve that problem…
For example we learn intuitively that when it rains, traffic will be slower and so we “learn” these patterns. For ML/AI who will define the data sets or parameters in novel and new situations or develop creative solutions to problems.
E.g. How would an AI system design a mars lander… or is imagination an prediction algorithm.
Another element is that we as moving to solve things at a smaller level, e.g. Gene therapy rather than surgery.
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