Artificial Intelligence

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.

About the working group

The AI working group aims to facilitate a greater understanding of AI and its applications in mining to enable companies to adopt AI solutions safely and effectively. It also aims to identify the current challenges within the industry, challenges associated with applying AI in mining operations and, define long-term collaborative and innovative solutions in a rapidly advancing technological landscape.

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.

Active Projects

The AI working group aims to facilitate a greater understanding of AI

Our objectives

The AI working group aims to facilitate a greater understanding of AI

Develop high-quality material about AI and related emerging technologies for their safe, effective, and efficient application in the mining industry

Define AI in a clear and accessible way and support its implementation and long-term sustainability

Facilitate a global network of AI experts to share knowledge, best practices, and practical use cases and case studies and examples

Educate the industry on concepts related to AI

Enable valuable and open conversations about solving key challenges and advance collaboration about the benefits and challenges associated with adopting AI solutions in mining operations to enable informed decision-making around AI adoption

2023 Focuses

Industry Education & Alignment

Having industry alignment and education that is widely accepted and understood by all stakeholders of the technology (from OEMs to operators) is key to understanding and enabling the full benefits of AI. Additionally, for industry-wide change, the realization that it won’t be one single technology making the difference, but a series of foundational technologies that will be implemented, magnifies the challenge.

Industry education and alignment around data is crucial to the success of AI technologies because these technologies are data reliant; therefore, industry-wide standardization (on the OEM side) is a requirement.

Additionally, alignment on cybersecurity is needed because it should be viewed from the same lens as safety, not from a competitive aspect, because whenever a new AI technology is implemented, there are risks to the operation.

Increasing industry education about where exactly AI can be beneficial in the mine is needed. This can also be a component to helping with industry alignment by sharing the knowledge of what and where the technology can fit (e.g., ore processing, smart ventilation).

We will deliver this workstream in support with the following focus areas:

How can existing AI tools be used to solve common problems (e.g., data standardization)?
Roadmap – define end state, start with current actions.
As questions get more complicated when determining how a technology can benefit an operation, it can be beneficial to know what emerging technologies exist that can allow a team of humans to ask these questions and to be supported by tools for the questions that increase with complexity. A guideline that can help should:
Be flexible and resilient enough to allow growth and integration.
Consider cost vs value.
Include alignment on adoption to better understand the value of what the cost is, where it can be used, and what it can be used for.
Consider the level of maturity required to adopt those types of implementations.

Technical Knowledge - Implementation and Operation Knowledge

Having industry alignment and education that is widely accepted and understood by all stakeholders of the technology (from OEMs to operators) is key to understanding and enabling the full benefits of AI. Additionally, for industry-wide change, the realization that it won’t be one single technology making the difference, but a series of foundational technologies that will be implemented, magnifies the challenge.

Industry education and alignment around data is crucial to the success of AI technologies because these technologies are data reliant; therefore, industry-wide standardization (on the OEM side) is a requirement.

Additionally, alignment on cybersecurity is needed because it should be viewed from the same lens as safety, not from a competitive aspect, because whenever a new AI technology is implemented, there are risks to the operation.

Increasing industry education about where exactly AI can be beneficial in the mine is needed. This can also be a component to helping with industry alignment by sharing the knowledge of what and where the technology can fit (e.g., ore processing, smart ventilation).

We will deliver this workstream in support with the following focus areas:

How can existing AI tools be used to solve common problems (e.g., data standardization)?
Roadmap – define end state, start with current actions.
As questions get more complicated when determining how a technology can benefit an operation, it can be beneficial to know what emerging technologies exist that can allow a team of humans to ask these questions and to be supported by tools for the questions that increase with complexity. A guideline that can help should:
Be flexible and resilient enough to allow growth and integration.
Consider cost vs value.
Include alignment on adoption to better understand the value of what the cost is, where it can be used, and what it can be used for.
Consider the level of maturity required to adopt those types of implementations.

Radiness - Strategy, Builindg Business Cases, Etc.

Having industry alignment and education that is widely accepted and understood by all stakeholders of the technology (from OEMs to operators) is key to understanding and enabling the full benefits of AI. Additionally, for industry-wide change, the realization that it won’t be one single technology making the difference, but a series of foundational technologies that will be implemented, magnifies the challenge.

Industry education and alignment around data is crucial to the success of AI technologies because these technologies are data reliant; therefore, industry-wide standardization (on the OEM side) is a requirement.

Additionally, alignment on cybersecurity is needed because it should be viewed from the same lens as safety, not from a competitive aspect, because whenever a new AI technology is implemented, there are risks to the operation.

Increasing industry education about where exactly AI can be beneficial in the mine is needed. This can also be a component to helping with industry alignment by sharing the knowledge of what and where the technology can fit (e.g., ore processing, smart ventilation).

We will deliver this workstream in support with the following focus areas:

How can existing AI tools be used to solve common problems (e.g., data standardization)?
Roadmap – define end state, start with current actions.
As questions get more complicated when determining how a technology can benefit an operation, it can be beneficial to know what emerging technologies exist that can allow a team of humans to ask these questions and to be supported by tools for the questions that increase with complexity. A guideline that can help should:
Be flexible and resilient enough to allow growth and integration.
Consider cost vs value.
Include alignment on adoption to better understand the value of what the cost is, where it can be used, and what it can be used for.
Consider the level of maturity required to adopt those types of implementations.

Autonomous Mining Updates

So, how can the industry respond to these challenges and stay on top of future innovations?

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