Machine Learning in Archaeology
Differences in interpreting pottery shards among archaeologists stem from subjective opinions and historical limitations in typology. With advancements in machine learning, particularly convolutional neural networks, researchers are now able to analyze vast collections of pottery more effectively, allowing for a more nuanced understanding of design variations that traditional methods struggled to accommodate.In this clip
From this podcast
The AI Podcast
Researchers Chris Downum and Leszek Pawlowicz Use Deep Learning to Accelerate Archaeology - Ep. 154
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