An artificial intelligence program developed by researchers at the University of Southern California uses machine learning algorithms to predict where poaching is likely to occur. Researchers at the University of Southern California have developed Protection Assistant for Wildlife Security (PAWS), an artificial intelligence program that uses machine learning algorithms to analyze data from past animal patrols to predict where poaching is likely to occur. Meanwhile, a game theory model helps generate randomized, unpredictable patrol routes. PAWS has produced good results during field tests in Uganda and Malaysia, and in the coming year its use will expand to China and Cambodia. The PAWS system also could be integrated into an existing tracking tool called SMART, which wildlife conservation agencies have deployed at most sites around the world to collect and manage patrol data. The researchers say the next step for PAWS is to make it available to other non-governmental organizations by integrating the algorithm into existing tools.

Every year, poachers kill about 27,000 African elephants--an astounding 8 percent of the population. If current trends continue, these magnificent animals could be gone within a decade. The solution, of course, is to stop poachers before they strike, but how to do that has long confounded authorities. In protected areas like wildlife preserves, elephants and other endangered animals may roam far and wide, while rangers can patrol only a small area at any time. "It's a two-part problem," explains Milind Tambe, a computer scientist at the University of Southern California, in Los Angeles.