In the recent edition of the first of SSEF twice a month newsletter – that spotlight a rapidly evolving frontier: Artificial Intelligence in gemstone testing. AI-powered tools present opportunities in gemstone identification, origin determination, treatment detection and synthetic detection – potentially reshaping laboratory protocols and industry standards. But how far can AI go, and where do its limitations lie? At SSEF, we uphold human expertise and experience as the cornerstone of gemmological analysis, particularly in origin determination.
Unlike typical artificial intelligence (AI) that needs large, simple datasets, gemstone data is more complex because gems form in many different ways underground. Therefore, it is essential to first carefully analyze the dataset to extract meaningful insights. Unsupervised machine learning (ML) methods, as a specialized approach that does not rely on pre-assigned labels, is more effective than traditional AI for helping gemmologists determine a gem’s origin.
By gathering extensive chemical information on a large amount of gemstones, SSEF uses an unsupervised machine learning tool (t-SNE algorithm) to group gemstones based on their chemical similarities. This helps our experts to infer where the gemstones might come from by comparing gemstones with reference samples.
In 2021, SSEF introduced a new machine-learning-assisted chemical data visualization approach for emerald characterisation and origin determination. Read more here. This tool has been expanded further and is regularly supporting our SSEF gemmologists in origin determination of gems such as copper-bearing tourmalines (Paraiba) and spinels, to name a few.
We continue to develop tools and technology in-house to assist gemmologists in their work on treatment detection and origin determination of gemstones. And we look forward to sharing more on this fascinating work in future.
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