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Decision analytics: What is AI?

Blog: Decision Management Community

Prof. Warren B Powell from Princeton University: “The most common types of AI come in three flavors: rules, machine learning, and making decisions (“optimization”). Rules are used for guiding computers to identify patterns or make recommendations, and have to be designed by people. This is how AI was done in the 1970’s. Learning involves using an external dataset to fit a statistical model.” LinkThere are three approaches computers can use to exhibit intelligence:

1) Rule-based logic – This was the original form of “artificial intelligence” in the 1960’s and 70’s, but it is still used today. “If furniture has four legs and a seat, it is a chair” or “if the credit score is over 600, grant the loan” are simple examples. In a health context, the attributes of a patient are more complicated (“if patient is male, and if a smoker, and if over 50, and if … and if … then order treatment X”). If the input data (for the “if” statements) has more than three or four dimensions, the rule becomes quickly intractable. This is the reason that “rule-based AI” failed, but we note that simple business rules remain widely used today in virtually all systems.

2) Making estimates using data from the environment – This is the domain of machine learning, also known as statistics, statistical learning and, more broadly, data sciences. It helps to divide this into two broad classes:

3) Making decisions that interact with the environment – Decisions involve a controllable quantity, and a metric that evaluates the performance of the decision. These arise in both static and sequential settings. Algorithmic strategies for making decisions are quite rich, but for this discussion it is useful to identify the following classes of methods:

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