Mempercayai AI dalam Penambangan dan Sepuluh Pilar Transformasi – Cryptopolitan
The mining industry is poised for a technological revolution as it navigates the challenges of integrating artificial intelligence (AI) into its operations. With fluctuating ore properties, changing conditions, and low data quality, mining requires a unique approach to ensure AI adoption is not just successful but also builds trust among users. Here are the ten pillars of trust in AI for mining. Mining is a dynamic field, and its AI solutions must reflect that. Industry-specific AI approaches must consider real-time data, varying ore feeds, low data quality, and the physical properties of mining processes and equipment. These tailored solutions are crucial for optimal mineral recovery. In mining, immediate predictions are essential. Real-time digital twins of unit processes and equipment offer hidden insights for better operational decision-making. Hindsight is not an option in an industry that demands quick adaptability. The most effective AI in mining combines mechanistic (first principle/physics) models with machine learning techniques. This fusion delivers target outputs within the constraints of real-world conditions and the laws of physics, ensuring practical and reliable results. Building trust with users requires transparency and explainability in AI decision-making. Miners should have the power to influence AI outputs by choosing “value drivers” that align with their specific goals,