• Wed. Jan 19th, 2022


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Archaeologists vs. Computer: New Study Fuels Sorting Competition

The neural network tied two of the human analysts for accuracy and beat the other two, the researchers found.

The machine was also far more efficient. Because the task was dull, none of the human analysts wanted to go through all 3,000 photographs without stopping, Dr. Pawlowicz said. So even though they probably could have completed the task in three hours, each conducted the analysis through several sessions over three to four months.

The neural network whipped through thousands of images in a few minutes.

Not only was the computer program more efficient and as accurate as the archaeologists, it was also able to better articulate why it had categorized shards a certain way compared with its living, breathing competitors. In one case, the computer offered up a smart sorting observation that was new to the researchers: It pointed out that two similar types of pottery with barbed line design elements could be distinguished by whether the lines connected at right angles or were parallel, said Leszek Pawlowicz, an adjunct faculty member at Northern Arizona University and another author of the study.

Machine also outshined humans in offering only one answer for each classification; the participating archaeologists often disagreed on how items were categorized, a known issue that often slows archaeological projects, the authors said.

Phillip Isola, an electrical engineering and computer science professor at M.I.T. who was not involved in the study, said he was not surprised that the neural network performed as well as — or sometimes better than — the archaeologists.

“It’s the same story we’ve heard a few times now,” Dr. Isola said. In the field of medical imaging, for example, researchers have found that neural networks rival radiologists at identifying tumors. Academics are also using similar tools to categorize plant and bird types.

This is also far from the first time archaeologists have turned to artificial intelligence. In 2015, researchers in France applied machine learning to classifying medieval French ceramics. A group of archaeologists and computer scientists from five countries is also developing a digital tool to categorize pottery shards. Neither of these projects explicitly pits human against machine, however.