A “river” of Tusayan White Ware sherds, showing the change in type designs from oldest at left to youngest at right. Deep learning allows for accurate and repeatable categorization of these sherd types. Credit: Chris Downum

Archaeologists at Northern Arizona University are hoping a new technology they helped pioneer will change the way scientists study the broken pieces left behind by ancient societies.

The team from NAU’s Department of Anthropology have succeeded in teaching computers to perform a complex task many scientists who study ancient societies have long dreamt of: rapidly and consistently sorting thousands of pottery designs into multiple stylistic categories. By using a form of machine learning known as Convolutional Neural Networks (CNNs), the archaeologists created a computerized method that roughly emulates the thought processes of the human mind in analyzing visual information.

“Now, using digital…

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