Artificial Intelligence is changing the mining industry

Examples of successful applications

Maria Linca

Maria Nanescu

Mining is a global industry that is fundamental to every product we use. A vital component of the mining industry is efficiency because most of the production revolves around transforming matter into different forms. It is often the case that small improvements in execution speed, process efficiency, or reduced downtimes separate a profitable operation from a complete failure. Nowadays, artificial intelligence is readily available in many of the products and services we use. Furthermore, cloud computing matured, hardware prices decreased, and machine to machine communication improved leading to unexpected advances in mining and industrial technology. Add the latest advances in analytics and artificial intelligence to the mix, and you get the perfect environment for improving efficiency in all areas of a mining operation.

In short, systems powered by artificial intelligence use different algorithms to organise and understand vast amounts of data with the purpose of creating of making optimal decisions. One immediate application of AI in mining is during the prospecting phase, especially for discovering deposits. For example, Goldspot Discoveries Inc. uses artificial intelligence for improving mineral exploration. The current practice of finding gold deposits is more an art than a science, thus Goldspot Discoveries Inc. intends to change that by developing AI systems capable of ingesting different data from which to discover potential gold deposit locations. AI is also used to understand better the environment and the terrain where new development will take place. In this space, Drone Deploy uses drones and computer vision to understand better the environment and the terrain where exploitation is to begin.

Artificial intelligence is not limited to systems capable of going through vast amounts of data. Some companies aware building intelligent systems for other phases of the operation. For example, the diamond mine Renard, in Quebec. There we see a smart system for waste sorting and disposal. This system is primarily used to improve the quality and quantity of the diamond recovery process. The algorithms use data from sensors and X-rays to increase the diamond recovery rate which helps recovering at least 96% of the weight of all diamonds larger than 1mm. Within the same space, Tomra, a Norwegian mining company, also developed a smart sorting system for minerals and ores. They are using computer vision and other AI algorithms powered by colour sensors and X-rays data. Finally, another successful example is Rio Tinto who now uses a fleet of autonomous vehicles inside the mine. These vehicles can be remotely operated and managed.

All these examples suggest that there are many areas where artificial intelligence is used in the mining industry. Furthermore, they also help shed some light on an old paradigm that investments in natural resource development and investments in technology are mutually exclusive. As technology evolves, investments will have to merge if mining companies are to improve their operational efficiency and productivity. As it stands now, the sector has seen pockets of investments in various AI technologies, with some mining companies being more aggressive than others.

The years to come will be exciting for the mining sector. We are now seeing many companies trying to implement digital systems and artificial intelligence as part of their operations. In the future, as data from all the processes are centralised, we will see many new intelligent systems for easing regulatory and compliance requirements, improve operational efficiency, or guide production based on real-time predictions of the demand. Finally, AI will also be used to predict accidents and natural disasters, and it will also become central for creating simulations that will improve reclamations decisions.

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Enterprise-wide AI business systems

Enterterprises should create networks of specific AI solutions and intelligent agents. These agents adapt to their environment and are autonomous. In other words, they continually learn from situations and each other about how to best perform their tasks. Soon, intelligent agents augment the whole enterprise and support employees. As they grow and evolve, these systems become ubiquitous, from internal systems to monitor and control processes to client facing applications and services.

AI Facilitates New Forms Of Customer Interactions: Voice Interfaces And Visual Content

voice interfaces and video content will soon become the primary way in which customers interact with businesses. Customers will interact with a brand in real time about their products and services. Voice interfaces like Alexa, Siri, or Cortana will enable customers to talk to a brand. Video and speech processing algorithms will further support that interaction either via intelligent chatbots or other interfaces that understand consumers’ pains, wants, and needs. As far-fetched as it sounds, this is not a sci-fi scenario. It will happen in the next 3-5 (maybe ten years). Thus, for brands to maintain and establish new long-lasting relationships, they need to support audio and video conversations with their clients as soon as possible.

A Pinch Of Artificial Intelligence

AI is a collection of methods that initiated a new paradigm in business, one where we solve problems without knowing the steps to take in advance. Using AI, we develop systems that mimic natural intelligence and in some cases even surpass it. Thus, AI creates systems capable of reasoning, solve problems, acquire and use knowledge, make decisions, and communicate in natural language.

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