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GMG Workshop: Open Data Sets for AI in Mining

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GMG Workshop: Open Data Sets for AI in Mining

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The GMG Artificial Intelligence Working Group recently launched the Open Data Sets for AI in Mining guideline project, and your participation and input are invaluable to drive the project forward.

Volunteer experts are needed to contribute to the content development of the guideline. Over the coming months the guideline will be drafted through contribution and collaboration of peers around the globe. The project kicked off with a virtual workshop in April. Online collaboration will continue to enable the development of the guideline. Learn more here.

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AI research and progress in many spheres has benefitted hugely from having a set of public and curated datasets. This has allowed developers and researchers to have suitable data to test and train their models on a variety of applications. More importantly, it has provided data which can be used to benchmark various solutions and allow for effective and fair comparison, as well as allowing for research to be repeated and validated.

The GMG AI Working Group believes there is a strong case to work collaboratively with its stakeholders to build open data sets specific to the mining sector for AI research and development. The project 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. Without these large open data sets which can be used by all, research and development will be limited to proprietary data which is likely to inhibit the progress of AI research and development specific to the mining sector. The project should seek to leverage what has been learned over time by the wider AI community and ensure that the approaches used in the mining domain are consistent with best practice, as well as providing guiding principles to underpin the collection, cleaning, labelling and curation of appropriate data.

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