Why AI Needs Mining as Much as Mining Needs AI
As AI technologies like ChatGPT-4 demand increasingly vast amounts of energy and critical minerals, the role of mining becomes more crucial than ever. Conversely, AI innovations are revolutionising mining operations, addressing workforce challenges, and enhancing safety and efficiency.
Richard Shellam
6/11/20243 min read
Why AI Needs Mining
The advent and proliferation of AI technologies like ChatGPT have significantly increased energy consumption globally. Training large language models such as ChatGPT-4 requires substantial computational power, leading to high energy usage. For instance, training a single large language model can consume up to 10 gigawatt-hours (GWh) of power, which is equivalent to the annual electricity consumption of over 1,000 UK households. Daily operations for handling queries can consume around 1 GWh, comparable to the daily energy consumption of 33,000 UK households (UW Homepage) (TechHQ).
AI hardware, particularly the specialised chips used for these computations, relies heavily on critical minerals. These include silicon, copper, cobalt, and rare earth elements like neodymium and dysprosium, which are essential for manufacturing GPUs and other components crucial for AI data centres (GeekWire). The increasing demand for AI technologies directly correlates to the need for these minerals, underlining the critical role of mining in supporting AI infrastructure.
Critical Minerals for AI Hardware
AI models are powered by GPUs and other high-performance computing hardware, which contain various critical minerals. Silicon is the primary material used in semiconductors, while copper is essential for electrical connections within and between devices. Cobalt is used in the production of lithium-ion batteries, which power many of the portable devices and data centre backup systems. Rare earth elements are crucial for the magnets used in the motors and actuators of server cooling systems (GeekWire).
The Energy and Resource Demands of AI
The energy consumption of AI is poised to grow significantly. By 2027, the AI sector could consume between 85 to 134 terawatt-hours annually, potentially accounting for up to half a per cent of global electricity consumption (Windows Central) (Outlook India). This increasing energy demand emphasises the need for electrifying the grid with renewable energy sources, many of which also rely on mined minerals.
Why Mining Needs AI
The mining industry faces several challenges, including an ageing workforce, the need for increased efficiency, and enhanced safety requirements. AI can address these challenges by providing advanced automation, predictive maintenance, and improved resource management.
Ageing Workforce and Efficiency
The mining industry is experiencing a significant demographic shift, with a substantial portion of the workforce nearing retirement. This creates a gap that AI can fill by automating routine tasks, thus reducing the need for human labour in hazardous environments. AI technologies such as autonomous vehicles and drilling systems can increase efficiency and reduce operational costs (Windows Central).
In the context of geotechnical engineering, tools like Geotech Assist are pivotal. As highlighted in a previous blog post, "The Future of Geotechnical Engineering: Trends and Predictions," the sector is facing a decline in student enrolments and an ageing workforce. Geotech Assist is designed to support geotechnical engineers by automating data management and analysis, thus addressing the efficiency challenge and allowing engineers to focus on higher-level tasks.
Meeting Net-Zero Commitments
To meet global net-zero commitments, a considerable number of new mines need to be developed to supply the necessary minerals for renewable energy technologies and electric vehicles. AI can optimise the exploration and extraction processes, making it feasible to tap into deeper and more challenging deposits that were previously inaccessible. Predictive analytics and machine learning can help in identifying new mineral reserves, thereby ensuring a steady supply of essential materials (GeekWire).
Safety and Zero Harm
One of the most critical aspects of modern mining is ensuring safety and minimising environmental impact. AI-driven monitoring systems can predict and prevent equipment failures, reducing the risk of accidents. Drones and remote sensors can monitor environmental conditions and ensure compliance with safety regulations, thus promoting a zero-harm approach in mining operations (GeekWire).
Geotech Assist plays a crucial role in this area. By providing detailed data capture and intelligent querying, Geotech Assist helps maintain rigorous safety standards and compliance with regulatory requirements. This capability is essential in ensuring that critical inspections and hazard assessments are conducted thoroughly, even with a reduced workforce.
Conclusion
In conclusion, AI and mining are symbiotically linked, each driving the progress and sustainability of the other. The minerals mined are vital for developing and operating advanced AI technologies, while AI innovations are crucial for addressing the challenges faced by the mining industry. This interconnected relationship underscores the necessity of both sectors in driving forward technological and environmental advancements.
By adopting tools like Geotech Assist, the industry can ensure that vital geotechnical data is accurately captured, analysed, and utilised, paving the way for a safer and more efficient future in mining operations. This integration of AI into geotechnical engineering not only enhances efficiency but also addresses workforce challenges, ensuring the industry remains robust and sustainable.
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