IMAGE: This is Taghi M. Khoshgoftaar, Ph.D., co-author and Motorola Professor in FAU's Department of Computer and Electrical Engineering and Computer Science. Using a highly sophisticated form of pattern matching, researchers from Florida Atlantic University's College of Engineering and Computer Science are teaching "machines" to detect Medicare fraud. About $19 billion to $65 billion is lost every year because of Medicare fraud, waste or abuse. Like the proverbial "needle in a haystack," human auditors or investigators have the painstaking task of manually checking thousands of Medicare claims for specific patterns that could indicate foul play or fraudulent behaviors. Furthermore, according to the U.S. Department of Justice, right now fraud enforcement efforts rely heavily on health care professionals coming forward with information about Medicare fraud.

In addition to penning 37 plays, William Shakespeare was a prolific composer of sonnets -- crafting 154 of them during his life. Now, more than 400 years after his death, the Bard's words are influencing a new generation of poets. It's just that these writers do so with silicon imaginations and digital quills. A consortium of researchers from the University of Toronto, the University of Melbourne and IBM's Australia division have managed to teach a neural network to craft sonnets just as the Bard did in the 16th century, using his own words to teach the machine. They published their results at the 2018 ACL conference, and you can play around with the network itself over at GitHub.

While this animated fellow looks like something out of NBA 2K18, it's really an AI that's learning how to dribble in real time. The AI starts out fumbling the ball a bit and by cycle 95 it is able to do some real Harlem Globetrotters stuff. In short, what you're watching is a human-like avatar learning a very specialized human movement. To do this researchers at Carnegie Mellon and DeepMotion, Inc. created a "physics-based, real-time method for controlling animated characters that can learn dribbling skills from experience." The system, which uses "deep reinforcement learning," can use motion capture date to learn basic movements.