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Machine Learning: Teaching Machines to Predict and Anticipate Will Make Them Interact with Humans More Easily

By Dick Weisinger

Predictive Machine Learning is a type of method used in artificial intelligence that combines statistical techniques, machine learning, predictive modeling and data mining. The goal is to estimate and make a prediction about the likelihood of an event or outcome from occurring. Machine Learning is able to take historical data, and from it, recognize patterns from which it can make decisions.

Predictive Machine Learning (ML) is being applied to a wide variety of problems, like areas in healthcare, sports, and human behavior.

In healthcare, predictive ML can be used to anticipate how patients will respond to therapy or how their condition may change. For example, in Finland, predictive machine learning was applied to patients with tongue cancer. As a tool, it helped enable informed decisions about how to manage the cancer. The algorithm was able to predict with 88 percent accuracy whether or not cancer would recur after treatment.

In sport competitions, coaches are beginning to include predictive machine learning as a tool to help make effective decisions. It can help coaches or managers make decisions that factor in parameters that include things like weather conditions and recent win/loss player statistics.

Predictive ML is also being used to understand the context of video. At Columbia University, researchers devised an algorithm that could examine a short video clip and from that predict the subsequent actions that would occur. For example, the algorithm could anticipate that after two people approach each other that one would offer their hand for a handshake. One application of this kind of research is the application to robotics. Ruoshi Liu, researcher at Columbia, said that “trust comes from the feeling that the robot really understands people. If machines can understand and anticipate our behaviors, computers will be able to seamlessly assist people in daily activity.”

Some researchers think that smarter predictive abilities will make machines easier for humans to relate to. Aude Oliva, researcher at the MIT-IBM Watson AI Lab, said that “prediction is the basis of human intelligence. Machines make mistakes that humans never would because they lack our ability to reason abstractly.”

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