ARTIFICIAL INTELLIGENCE DRIVES THE ARCHAEOLOGICAL DISCOVERIES OF STONE AGE Posted on : Oct 27 - 2020

Machine learning model and Neural Networks helps in extracting archaic information about human civilization.

Archaeology is the gateway to our past. It describes events which shaped the world how it is today and the transition that led humans from animal-hunter to a knowledgeable-mosaic. In archaeology, Stone Age holds the key relevance. It establishes the patterns of human behavior and helps in identifying the transitions that hurled humans to the path of development. It was also the era which disembarked human’s acquaintance with tools to sustain their living. Stone Age displayed the creative and clever aspect of human civilization.

Over the years, researchers and scientists are keen to discover the incidents of the Stone Age, which led to the expansion of human civilization. They are inquisitive to learn and discover about how civilizations travel from one place to other and resettled themselves. In a traditional research setting, the information retrieved from many sources is not sufficient to arrive at a substantive conclusion. Henceforth, researchers and scientists are leveraging machine learning model to determine their archaeological findings.

Clarifying the Archaeological Origin

A team of Mexican archaeologists and the University of Marburg are leveraging machine learning model to identify whether the source material required for making Obsidian artifacts, discovered in Xalasco came from local sources or were obtained from other remote areas. Xalasco is a pre-Colombian cite in western Mexico.

In a paper titled“Projection-Based Classification of Chemical Groups for Provenance Analysis of Archaeological Materials”, the researcher states that with the combination of unsupervised and semi-supervised machine learning and chemometric application on the samples of Mesoamerican geological sources and obsidian artifacts collected from the archaeological site of Xalasco in Mexico a preference of Xalasco inhabitants to local obsidian deposits have become evident.

The paper also pointed out that with the combination of XRF spectroscopy, an adequate tool for obsidian provenance studies and which can successfully discriminate between several groups of archaeological artifacts employing quantitative analysis, and machine learning algorithms aided the researchers to implement a procedure that automatically determines the number of groups in obsidian samples according to their chemical characteristics and, consequently, defines their origin.

Moreover, with the machine learning method, which became a flexible and robust approach for cluster analysis consisting of three separate modules that can be optionally combined into the Databionic Swarm (DBS), the non-linear projection displayed the structure of the high-dimensional data into a low-dimensional space preserving the cluster structure of the data. It must be noted that the Databionic Swarm is part of the Swarm-based projection method, which assist intelligent agents to interact with each other and with the environment by displaying intelligent behavior. Swarm intelligence is considered as the most fertile ground for establishing new methodologies of classification. View More