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Status: Unchecked ×Topic: Geochemistry and Geologic Mapping ×Clear all

3 claims from 1 paper

1. Computer Science › Geochemistry and Geologic Mapping

   Exploring neuro-symbolic AI applications in geoscience: implications and future directions for mineral prediction

   Chen, Ma, Wang et al. · Earth Science Informatics · 2024

   A literature review of machine learning, deep learning and neuro-symbolic AI in geoscience, focused on mineral prediction, argues that combining neural methods with symbolic reasoning could improve interpretability.

   Unchecked3 claims
   Show 3 claims

   1. UncheckedA literature analysis finds AI-focused geoscience research rose sharply from 2018, with a predictive orientation and steady attention to key computational terms.“A thorough analysis of the literature indicates a marked escalation in AI-centric geoscience research starting in 2018, characterized by a predictive research orientation and a persistent focus on key computational terms.”
   2. UncheckedA literature analysis of AI in geoscience finds "deep learning" and "machine learning" remain central themes, with "transfer learning" and "big data" growing.“The thematic network and evolution analyses underscore the enduring prominence of “deep learning” and “machine learning” as pivotal themes, alongside progressive developments in “transfer learning” and “big data”.”
   3. UncheckedThe paper says neuro-symbolic AI overcomes limits of standard machine learning by using expert knowledge and explaining its predictions, which suits mineral prediction.“NSAI distinguishes itself by surmounting traditional ML constraints through the incorporation of expert insights and delivering explanatory power behind its predictive prowess, rendering it particularly advantageous for mineral prediction tasks.”