Machine Learning and Archaeology

Silicon Valley Meets Mesopotamia

Archeology is a relatively obscure but deeply impactful discipline. Archaeology is critical to our understanding of human history. Moreover, the discoveries unearthed through archaeological work impact and help shape other intellectual disciplines such as politics, geography, demographics and more. In many ways archaeology is a foundational discipline that creates the base of data and historical context that many different fields rely on.

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Archaeological Site Prediction

One of the most difficult, slow and time-intensive tasks in archaeology is archaeological site prediction. In fact, archaeologists work diligently for years trying to identify new archaeological sites that are worthy of the time and cost to excavate. Traditionally archaeologists would manually pore over data to try identify sites worth of discovery. However, satellite imaging and LIDAR combined with machine learning promise to automate large parts of this discovery process.

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Automated Artifact Classification

Another extremely manual task in archaeology is artifact classification. Every successful archaeological dig results in numerous archaeological artifacts which researchers then have to classify and analyze. Typically this process is done manually via visual inspection. For example, for classification of historical glass artifacts, an expert will search through a reference collection to try classify new glass pieces that are discovered.

Automated Artifact Translation

Archaeologists have also started to use machine learning to understand and recreate ancient Greek texts from broken stone tablets. DeepMind — Google’s famous deep learning unit — built a system called Pythia trained to recreate these texts. As a test PhD students and Pythia were both given a set of texts with artificially removed portions and asked to fill in the gaps. The students completed the text with a poor 43% accuracy. In contrast, Pythia correctly filled in the gaps a more impressive 70% of the time.

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Automated Archaeological Puzzle-Solving

Finally, machine learning can help researchers significantly accelerate the archaeological puzzle-solving process. Initially when most artifacts are discovered they are often broken into multiple pieces. Archaeologists spend countless hours figuring out how to reassemble these pieces. This puzzle-solving process can be incredibly taxing. Researchers at the University of Haifa have trialed a machine learning model that can predict how to reassemble fragmented artifacts for archaeologists. Initial testing has shown the model they built to perform extremely well on a few tests artifacts.

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Archaeological Machine Learning

Overall, machine learning promises to drive a dramatic increase in the pace of archaeological research globally by unlocking a few critical bottlenecks. By automating and speeding up tasks such as archaeological site prediction, artifact classification, translation and puzzle-solving, machine learning will open the floodgates for archaeological research. Our understanding of human history and its complexities will advance rapidly in the decades to come as machine learning helps us discover previously undiscovered sites, classify and translate the artifacts we finds and enable archaeologists to focus on the most high-value and difficult archaeological tasks.

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