The job of an archaeologist is one that is filled with tedious hours searching for that ultimate discovery that may never come. For archaeologist Gino Caspari, those feelings ring true as he looks for and studies the ancient Scythian civilization across the vast area that they inhabited, which is now known as Mongolia, Russia, and China’s Xinjiang province.

In a recent article written by Zach Zorich for the New York Times online site, he discusses the steps that Caspari has taken to cover the fast landscape as he searches for the civilization’s royal tombs. Current methods used to locate and map the crypts have taken away valuable time for Caspari and his team and, according to Caspari, is not something that a well-educated scholar should be doing.

Luckily for Caspari, there is a technology that is available that is changing the face of conducting archaeological site identification and potential digs. The technology called a convolutional neural network (C.N.N.) is a form of artificial intelligence designed to analyze information processed as a grid. Caspari and Dr. Crespo (designer of the convolutional neural network that they are using) after, training the C.N.N., are hoping that through its use, they will be able to locate more sites. Scouring Google maps as Caspari had previously been doing took time away from searching sites found. He also hopes that it will also aid other archaeologists to identify new tombs and other important sites with hopes to protect them from looters.

Convolutional neural networks can be used for more than just identifying tombs and sites, however. Archaeologists Dr. Gattiglia and Dr. Anichini at the University of Pisa are hoping to use it to identify pottery sherds against thousands of images of pottery to pinpoint the kind of pottery it is and potentially the area that it originated. Through systems like convolutional neural networks, the time frames that archaeologists spend photographing and analyzing discovered sherds or sites are cut in half, and they can spend more time excavating different sites.

The time-saving aspect of convolutional neural networks is endless across all fields of archaeology from, marine to human remains. Many archaeologists already using forms of C.N.N.’s have found success, and continued success is hoped for and expected.