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Digital Humanities Benelux Conference 2017

Excavations 2.0: how aincient uses artificial intelligence to unlock the past

Heleen Wilbrink: Egyptologist, marketer at ING and owner of aincient

 

An archaeologist digging in dusty soil, exhausted by heat and battling many challenges and disappointments, finally finding something unexpected and extraordinary. We cherish this romantic idea of archaeology, fed by books and movies. But reality is different.

 

Today’s biggest challenge is not to find something new, but to work our way in the ever-increasing body of already excavated and digitized objects. Unfortunately it is extremely difficult to search and compare single items from different collections. This is not only due to the use of various data-structures and lack of standardization but also relates to the core of the archaeological discipline; categorization. Much of the discoverability depends on the quality of tagging. There are multiple metatags to use – think of the material, morphology, function and period – and the definitions of the assemblies depend largely on the choices made when the objects were categorized in the early stages of the excavation.

 

Could computers achieve what humans could not?  Would artificial intelligence be able to solve this long-standing problem? Wanting to discover its potential, I turned to Google for help. Together with their partner, software company SynerScope, we devised a tool which aims to recognize, sort and analyze the images of archaeological artifacts in minutes instead of days or even months.

 

To put the tool to the test, we used the open-data set of the Dutch National Museum of Antiquities (RMO for short in Dutch). The results of the prototype are amazing. With very little work and by maximizing Google’s Cloud Vision API the collection was categorized using image recognition. This gives a researcher the possibility to rediscover objects with similar shape and form in the existing body of digital material. Wim Weijland, director of the RMO, explains: “The available data in the tool can be used as a source of inspiration for new exposition topics, but also serves the public and researchers. In the future, we will be able to link well-organized databases to those of other national and international museums, which will increase the knowledge level. This way, the current and future technology will give us a better look into the past.”

 

The prototype of the tool, using the RMO data set, can be tested during the DH Benelux conference. The next steps will be linking the dataset to other collections and launching the full release version of the tool, which we expect to be able to do before the end of this year. The more databases aincient will be able to link up, the better the results. A comprehensive search process will than be able to be completed in minutes instead of months, and will allow for a shift in focus towards in-depth analysis. Due to its ability to find correlations between objects and collections that were previously hidden under different metatags and keywords, aincient will also increase the likelihood of new discoveries in material that was excavated and digitized years ago.