The Mentalist – Robot version; AI predicting human behaviour

In the show, The Mentalist, the protagonist was able read and predict the behaviour of the people around him and uses it to solve crimes.
By: HyperCharge Technology
 
RAMAT GAN, Israel - Aug. 12, 2021 - PRLog -- Prediction is something humans can do by observing the body language on another human being or animal, but it does not come easily to computers. For example, people have their own way of greeting someone whether it's by saying hello, a handshake or a fist bump, and the other person just responds appropriately.

Columbia engineering researchers created a new study on how computers can gain the same predicting ability. They developed a computer vision technique in order for computers to gain a more intuitive sense for it to be able to "predict" what will happen next through leveraging the associations of objects, animals, and humans.

They claim that their study is the most accurate method that is currently available if we're only talking about prediction through video action events for up to several minute ahead. After the system watched several hours of movies, sports games and TV shows, it managed to learn and predict hundreds of different activities, and even if it came into something it cannot predict, it analyses the situation and determines the higher-level concept that is present, and for the examples earlier, the higher concept would be greeting.

Previous attempts in the same field which is predictive machine learning, including theirs, solely focused on predicting a certain action one at a time. The program and algorithms are set to identify if an action is for a hug, handshake, fist bump, high five, or even the non-action ignore. The problem here is that when the program encounters high uncertainty situation, the previous models were unable to determine the commonalities, hence limiting their response options.

The new program focuses on how the machine organizes the events into categories, and how predictable for it to happen in the near future. For example, running and swimming are different types of exercise. So now, when the machine encounters something similar, instead of a specific prediction of either running or swimming, it will provide a more generic one, like exercise.

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