Opportunities and challenges in applying AI to evolutionary morphology

Y He, JM Mulqueeney, EC Watt… - Integrative …, 2024 - academic.oup.com
Artificial intelligence (AI) is poised to revolutionise many aspects of science, including the
study of evolutionary morphology. While classical AI methods such as principal component …

The Paradox of Predictability Provides a Bridge Between Micro-and Macroevolution

M Tsuboi, J Sztepanacz, S De Lisle… - Journal of …, 2024 - academic.oup.com
The relationship between the evolutionary dynamics observed in contemporary populations
(microevolution) and evolution on timescales of millions of years (macroevolution) has been …

Accelerating segmentation of fossil CT scans through Deep Learning

EM Knutsen, DA Konovalov - Scientific Reports, 2024 - nature.com
Abstract Recent developments in Deep Learning have opened the possibility for automated
segmentation of large and highly detailed CT scan datasets of fossil material. However …

Artificial Intelligence-powered fossil shark tooth identification: Unleashing the potential of Convolutional Neural Networks

A Barucci, G Ciacci, P Liò, T Azevedo… - arXiv preprint arXiv …, 2024 - arxiv.org
All fields of knowledge are being impacted by Artificial Intelligence. In particular, the Deep
Learning paradigm enables the development of data analysis tools that support subject …

Automatic Segmentation of Early Triassic Vertebrate Fossil CT Scans: Reducing Human Annotation Time through Deep Learning

EM Knutsen, DA Konovalov - 2024 - researchsquare.com
Abstract Recent developments in Deep Learning have opened the possibility for automated
segmentation of large and highly detailed CT scan datasets of fossil material. However …