Neural artificial intelligence, particularly neural networks, can have significant implications in resource estimation. These AI models can analyze large datasets and recognize complex patterns that might not be apparent to human analysts. In the context of resource estimation, neural AI can help in: 1. **Data Analysis**: Neural networks can process geological data, such as drill hole data, geophysical data, and geochemical data, to identify valuable patterns and correlations that can improve resource estimation accuracy. 2. **Predictive Modeling**: By training neural networks on historical data, they can predict mineral deposits' locations and characteristics more accurately, helping in estimating resources with higher precision. 3. **Risk Assessment**: Neural AI can assess uncertainties and risks associated with resource estimation by considering various factors and scenarios, providing a more comprehensive understanding of the resource potential. 4. **Efficiency**: Automating resource estimation processes using neural AI can significantly reduce the time and effort required for manual calculations, allowing geologists and engineers to focus on higher-level analyses and decision-making. 5. **Continuous Learning**: Neural networks can continuously learn and adapt to new data, improving their accuracy over time and helping in updating resource estimates based on real-time information. Overall, integrating neural artificial intelligence into resource estimation processes can lead to more accurate, efficient, and data-driven decision-making in the mining and geological industries.Barrick BHP Newmont Corporation Datamine Seequent Artificial Intelligence AngloGold Ashanti GOLD FIELDS Yedidia Mgema MAusIMM Exploration Geologist consultant machine learning enthusiast Smart Mining Resources email;yedidiamgema@outlook.com contact;+255694238804 (WhatsApp) 1st, May, 2025