ARTIFICIAL INTELLIGENCE WORKING GROUP

Artificial intelligence (AI) has great potential to make mining operations safer and more productive. This working group aims to identify the current challenges, educate the industry, keep up with the rapid pace of change in this field and define collaborative solutions.

To join the working group and any of its projects, click here.

WHY IS THIS TOPIC IMPORTANT FOR THE MINING INDUSTRY?

The mining industry is increasingly using AI as a tool to optimize processes, enhance decision-making, derive value from data and improve safety. Nevertheless, the transition to an AI-enabled mine will look different for every organization and an inadequate implementation may result in:

  • Revenue loss
  • Accidental system failures
  • Resistance to change
  • Potential hazards and labour risks

While AI has many benefits for the industry, it is still very new and there is a lot of work that needs to go into successfully applying it. It therefore requires a robust foundation of planning, research, communication and change management. 

ABOUT THE WORKING GROUP

The AI Working Group aims to identify the current challenges within the industry, define long-term collaborative solutions, drive innovation, educate on AI-related concepts and technologies like machine learning, keep up with the rapid advancement of technologies, and share best practices and knowledge. The group is a global network of subject-matter experts, operators, suppliers, leaders from inside and outside the mining industry, and those interested in learning more about applying AI in their operations. 

Objectives

  • Develop high-quality material about AI and related emerging technologies for their safe, effective and efficient application in the mining industry
  • Facilitate a global network of AI professionals to share real use case examples, drive valuable conversations about solving key challenges and advance collaboration for enabling AI adoption 
  • Define AI in a clear and accessible way and support its implementation and long-term sustainability

ACTIVE PROJECTS / SUB-COMMITTEES

Open Data Sets for AI in Mining

In the transition to a data driven world, data can provide opportunities to solve problems in various areas, for example, accelerated research, increased transparency, and identification of novel solutions to problems.

The objective of this guideline is to provide best practices for data sharing based on existing initiatives for those within the mining industry so they can benefit from the opportunities of open data.

Click here to learn more

Implementation of AI in Mining

This project will act as a roadmap for the industry and provide guidance on scaling AI applications across mine operations by leveraging lessons learned and case study examples.

Click here to learn more.

PROPOSED PROJECTS / SUB-COMMITTEES

Case Studies Library

The Case Studies Library is a project dedicated to house a collection of user stories related to the implementation of AI from across the mining industry. This hub will be a vital resource that can be used by different companies eager to learn from others who have attempted a similar project and can help provide information on what some of their roadblocks, outcomes and successes from the undertaking looked like.

Interpretability & Explainability

This project aims to explore two different areas of AI that the working group have identified as priority topics.

Interpretability – This concept refers to the predictability of an AI system based on a series of inputs.

Explainability – This concept refers to the understanding of how an AI system comes up with a particular result.

KNOWLEDGE SHARING PRESENTATIONS

Foundations of Artificial Intelligence in Mining (Video)

The mining industry is increasingly using artificial intelligence (AI) as a tool to optimize processes, enhance decision-making, derive value from data, and improve safety. The white paper aims to build a foundation for mining companies that are planning and implementing AI solutions. Click here to watch.

From idea to product: The application of AI algorithms in mining (Video)

With the mining sector embracing more and more the principles of the industry 4.0, we expect artificial intelligence to be the next revolution in mining. But in data science, between ideas and products there is a long and tortuous road. This presentation will describe how Newtrax has managed to move from ideas to reality for the application of AI technology in mining. Best practices and common traps to avoid will be discussed as well. Click here to watch.

Developing Open Data Sets for AI in the Mining Sector (Video)

The Global Mining Guideline’s AI in Mining Working Group has begun to work collaboratively with its stakeholders to build the case for open data sets specific to the mining sector for AI development. GMG believes this will enhance the ability to build meaningful solutions for the industry by providing typical data relating to assets or operations for training and testing of models, and allowing all parties to have the ability to benchmark solutions and research more effectively. Click here to watch.

Learnings from Staging Petabytes of Data for Analysis in AWS (Video)

AWS hosts a variety of public datasets that anyone can access for free. Previously, large datasets such as satellite imagery or genomic data have required hours or days to locate, download, customize, and analyze. When data is made publicly available in the cloud, anyone can analyze any volume of data without needing to download or store it themselves. We will look at patterns and anti-patterns to watch out for when looking to work with open data across any domain. Click here to watch.

Implementation of Artificial Intelligence in Mining (Video)

The GMG Artificial Intelligence Working Group is currently developing an implementation of AI guideline. Its aim is to leverage lessons learned and case study examples from experience applying AI in mining to help overcome the challenges and barriers to entry and to improve adoption of AI, and as a result, boost efficiency in the mining value chain. Click here to watch.

Open Data Sets for Artificial Intelligence in Mining (Video)

The Industry participants are currently developing a guideline for the collection, cleaning, labelling, and curating of open data sets to help the industry test and train their models for a variety of AI applications.  This guideline is the first phase of a broader project to build open data sets relevant to the mining sector. Click here to watch.

Uncertainty Management Strategies for Industrial Applications (Video)

Danielle Zyngier, Hatch, presented about uncertainty management strategies for industrial applications. We typically talk about AI focusing on data-driven technologies, but AI isn’t only about data-driven methods. There is a lot to be said about AI based on fundamental models and optimizing them, and that can be combined. What is decision-making under uncertainty? Literature talks about many different methods, but they are used for different things.  Click here to watch

Value and Opportunities of IoT and Location Interoperability (Video)

Data and AI are better when they are together. Unfortunately most of today’s Internet of Things (IoT), including location tracking, sensing, and industrial control systems, are disparate systems designed and deployed for one-off applications. That means it is very challenging to aggregate and correlate the heterogeneous data from silo-ed IoT systems for real-time situational awareness and early warnings. This webinar will present the geospatial and IoT standards from the Open Geospatial Consortium (OGC). Click here to watch.

X