Vertebrate paleontology has a numbers problem. Computer vision can help
Phys.org
February 25, 2026
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A new study co-authored by Bruce MacFadden, UF Distinguished Professor Emeritus and retired curator of vertebrate paleontology at the Florida Museum of Natural History, reveals that only around 250 fossils are needed to accurately train an image-based AI algorithm. This finding challenges previous assumptions that a much larger dataset was necessary for effective training, potentially opening new avenues for paleontological research.
The study, published in the journal *Paleobiology*, highlights a significant "numbers problem" in vertebrate paleontology. Traditionally, scientists believed that large collections of fossils were required to train AI algorithms for accurate classification and analysis. However, this new research demonstrates that a more manageable number—250 fossils—can achieve comparable or even superior results. This breakthrough could streamline the process of fossil analysis, making it faster and more accessible for researchers.
The implications of this discovery are profound. By reducing the reliance on vast datasets, AI tools can now be applied to classify and study fossils with greater efficiency, even in cases where resources are limited. This shift could accelerate the pace of paleontological research, enabling scientists to better understand ancient species and their evolutionary histories. The integration of computer vision into paleontology not only addresses a long-standing challenge but also enhances the field's ability to process and interpret large amounts of data.
This advancement is particularly relevant in an era where technology
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Originally published on Phys.org on 2/25/2026