Archaeologists vs. computers: A study tests who's best at sifting the past

The First Art Newspaper on the Net    Established in 1996 Thursday, March 28, 2024


Archaeologists vs. computers: A study tests who's best at sifting the past
Shards of Tusayan White Ware, a type of painted hand-formed pottery used in northeastern Arizona between 825 and 1300, with distinctive design elements that made the various types identifiable. Leszek Pawlowicz and Christian Downum/ Northern Arizona University via The New York Times.

by Heather Murphy



NEW YORK (NYT NEWS SERVICE).- A key piece of an archaeologist’s job involves the tedious process of categorizing shards of pottery into subtypes. Ask archaeologists why they have put a fragment into a particular category and it’s often difficult for them to say what exactly had led them to that conclusion.

“It’s kind of like looking at a photograph of Elvis Presley and looking at a photo of an impersonator,” said Christian Downum, an anthropology professor at Northern Arizona University. “You know something is off with the impersonator, but it’s hard to specify why it’s not Elvis.”

But archaeologists have now demonstrated that it is possible to program a computer to do this critical part of their job as well as they can. In a study published in the June issue of The Journal of Archaeological Science, researchers reported that a deep-learning model sorted images of decorated shards as accurately — and occasionally more precisely — as four expert archaeologists did.

“It doesn’t hurt my feelings,” Downum, one of the study’s authors, said. Rather, he said, it should improve the field by freeing up time and replacing “the subjective and difficult-to-describe process of classification with a system that gives the same result every time.”

The study focused on Tusayan White Ware, a type of painted hand-formed pottery used for serving food and storing water in the canyons and mesas of northeastern Arizona between 825 and 1300. In the 1920s, archaeologists figured out that Tusayan White Ware pieces have consistent patterns depending on the time period in which they were created.

The researchers recruited four of the most experienced analysts of this particular type of pottery. Each had spent 30 or more years analyzing ceramics and had previously classified tens of thousands of Tusayan White Ware fragments.

They also spent about four hours training a neural network, a complex mathematical system that can learn specific tasks by analyzing vast amounts of data, to sort photographs of Tusayan White Ware.

Human and machine were each tasked with categorizing thousands of images into one of nine known types and evaluated on the accuracy of their answers.

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, said Leszek Pawlowicz, an adjunct faculty member at Northern Arizona University and another author of the study. 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, Pawlowicz said.

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 Massachusetts Institute of Technology 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,” 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.

Since the study began to circulate, some archaeologists have shared concerns with the authors that they will be replaced by machines. Downum and Pawlowicz said they were not worried about such a thing happening.

“We’re the ones that decide what’s important to study,” Downum said.

© 2021 The New York Times Company










Today's News

May 27, 2021

A self-styled 'troublemaker' creates a different Paris museum

Laurence des Cars to head Louvre, first woman boss in its history

Archaeologists vs. computers: A study tests who's best at sifting the past

Asia Week New York 'LIVE' zooms-in on The Art of Installation and Display on May 27th

Hindman Auctions appoints Caroline Mujica-Parodi as Director of Museum Services

Biden seeks to replace several Trump appointees on arts commission

Stephen Hawking's office and archive saved for the nation

Fritz Scholder skyrockets to $225k, and more from Los Angeles Modern Auctions Spring Auction

Ten-year Panza Collection initiative concludes with publication and digital archive

Germany unveils 2.5 billion euro fund to reboot cultural events

Sculpture International Rotterdam enriched with new sculpture by Gavin Turk

Artsy to auction work by Julie Mehretu with proceeds going to Art for Justice

Hindman's Spring Modern Design auction surpasses $865,000

Anna Halprin, choreographer committed to experimentation, dies at 100

Lost Ravilious work last seen in 1939 unveiled at Hastings Contemporary's summer show

Major gift for the Canada Pavilion, Venice and gallery re-named to honour the legacy of Dr. Shirley L. Thomson

Jack Shainman Gallery opens an exhibition of new work by Leslie Wayne

'Myths and Hymns,' a theater cult favorite, changes shape again

Robbie McCauley, stage artist who explored race, dies at 78

Woaw Gallery opens a new group exhibition curated by Sasha Bogojev

TarraWarra Museum of Art announces appointment of Léuli Eshrāghi as Curator for TarraWarra Biennial 2023

Exhibition of works of small dimensions created by Agostino Bonalumi opens at The Cardi Gallery

Greece approves Dior shoot at key ancient sites

Vienna's musicians find their voice after months of silence

Enjoy the surprising things to do in the UK

Super Mario Bros

Your Ultimate Guide to Glass Bottle Printing

Mushrooms: Know the Simple Steps to Grow Them

Style over substance: why the art is the driving point of games

Seitaro Yamazaki Showcases New Art in 2021 YICCA Exhibition

Private Yacht Charters: Enjoy the Breath taking Views of Nature




Museums, Exhibits, Artists, Milestones, Digital Art, Architecture, Photography,
Photographers, Special Photos, Special Reports, Featured Stories, Auctions, Art Fairs,
Anecdotes, Art Quiz, Education, Mythology, 3D Images, Last Week, .

 



Founder:
Ignacio Villarreal
(1941 - 2019)
Editor & Publisher: Jose Villarreal
Art Director: Juan José Sepúlveda Ramírez

Royalville Communications, Inc
produces:

ignaciovillarreal.org juncodelavega.com facundocabral-elfinal.org
Founder's Site. Hommage
to a Mexican poet.
Hommage
       

The First Art Newspaper on the Net. The Best Versions Of Ave Maria Song Junco de la Vega Site Ignacio Villarreal Site
Tell a Friend
Dear User, please complete the form below in order to recommend the Artdaily newsletter to someone you know.
Please complete all fields marked *.
Sending Mail
Sending Successful