Automated identification and deep classification of cut marks on bones and its paleoanthropological implications.

• The identification of cut marks on bones created by stone tools is a crucial aspect of human evolution. • Until now the identification of cut marks was exclusively based on the analyst expertise and experience. • Here, we present a machine learning methodology that overcomes human subjective interpretations of marks using artificial intelligence tools. • A computer vision and machine learning method provides a success rate of cut mark identification that is 50% higher tan the frequencies of correct identifications by human experts. • Our method enables to correctly identify marks and assess when key behavioral features emerged in human evolution, such as meat-eating and hunting. The identification of cut marks and other bone surface modifications (BSM) provides evidence for the emergence of meat-eating in human evolution. This most crucial part of taphonomic analysis of the archaeological human record has been controversial due to highly subjective interpretations of BSM. Here, we use a sample of 79 trampling and cut marks to compare the accuracy in mark identification on bones by human experts and computer trained algorithms. We demonstrate that deep convolutional neural networks (DCNN) and support vector machines (SVM) can recognize marks with accuracy that far exceeds that of human experts. Automated recognition and analysis of BSM using DCNN can achieve an accuracy of 91% of correct identification of cut and trampling marks versus a much lower accuracy rate (63%) obtained by trained human experts. This success underscores the capability of machine learning algorithms to help resolve controversies in taphonomic research and, more specifically, in the study of bone surface modifications. We envision that the proposed methods can help resolve on-going controversies on the earliest human meat-eating behaviors in Africa and other issues such as the earliest occupation of America. [ABSTRACT FROM AUTHOR]