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In the early years of the 20th century, American author L. Frank Baum created the Land of Oz \u2013 and with it, two contrasting examples of living, intelligent machines. The first and most famous of these is the Tin Man: a human woodsmith whose biological body had slowly been replaced with a metal one until he was \u201csome sort of man yet made entirely of hollow tin.\u201d<\/p>\n\n\n\n
The second of Baum\u2019s androids is Tik-Tok, who first appears in the third Oz novel, Ozma of Oz. Unlike Tin Man, Tik-Tok is an original machine \u2013 a \u201cpatent double-action, extra-responsive, thought-creating, perfect-talking mechanical man,\u201d as the manufacturer\u2019s label reads \u2013 and an early example of artificial intelligence (AI) in fiction.<\/p>\n\n\n\n
By the middle of the century, science had picked up where literature had led. Alan Turing explored the theoretical possibility of AI in his seminal paper Computing Machinery and Intelligence, published in 1950.<\/p>\n\n\n\n
In a section entitled \u201cLearning Machines,\u201d Turing compares the mind to a pile of atomic material, ideas to neutrons that are injected into that pile, and the reaction caused by that injection to the process of learning. According to Turing, most human minds are \u201csub-critical’ [\u2026] An idea presented to such a mind will on average give rise to less than one idea in reply. A smallish proportion are super-critical. An idea presented to such a mind may give rise to a whole \u2018theory’ consisting of secondary, tertiary and more remote ideas [\u2026] Adhering to this analogy we ask, \u2018Can a machine be made to be super-critical?\u2019\u201d<\/p>\n\n\n\n
At the time he wrote this, the answer to Turing\u2019s question was very firmly theoretical. Seven decades later, times have changed. We are living in the age of Tik-Tok. But unlike in Oz, where that perfect-talking mechanical man was a one-off creation, AI today is reaching into almost every part of human life. <\/p>\n\n\n\n
AI and mining: the basics<\/strong> \u201cThe fundamental thing that miners need to understand with anything to do with AI is that it all starts with data and its real-time usage (or not),\u201d explained Phillip McBride, lead at mining Industry 4.0 specialists, I4 Mining, and chief sales officer at Rayven. \u201cPoor quality or incomplete data sets coming in equals bad insights and performance coming out.\u201d<\/p><\/blockquote>\n\n\n\n Machine learning is the algorithmic analysis of these (potentially very large) data sets to identify hidden trends or relationships within them. \u201cYou could, for example, use a machine learning algorithm to spot that productivity goes up when the external temperature is below 33\u00b0C,\u201d continued McBride. This can be taken a step further by programming a machine learning algorithm to predict how a plant or equipment will perform, based on what has happened in the past. Predicting a catastrophic failure based on real-time vibration measurement would be an example of this, but \u201cthis alone doesn\u2019t really meet the criteria to be classed as AI,\u201d the Rayven executive noted. <\/p>\n\n\n\n What is AI, then? According to McBride, AI is \u201cwhen a machine learning algorithm is working on real-time data and is enabled to take action based on business logic and automation.\u201d In the plant vibration scenario, this would mean the machine learning algorithm is enabled to instruct either the responsible operators, or the machinery itself, to take action to rectify the situation or to shutdown to prevent failure.<\/p>\n\n\n\n \u201cTo facilitate this, you need an AI-plus-Internet of Things (IoT) platform that is interconnected to all of the relevant data sources in real time and that\u2019s capable of not just analysis, but execution too, through any number of interconnected technologies and devices,\u201d McBride concluded. <\/p>\n\n\n\n Machine learning<\/strong> Machine learning \u201cequips miners with superhuman senses,\u201d explained Ravi Sahu, founder and CEO of mining AI specialists Strayos. \u201cThe algorithms can comb through mountains of data in a fraction of a second, detecting nuances in the data that would be missed by people.\u201d <\/p>\n\n\n\n This is particularly useful when repeated analysis of large data sets is required to identify specific operational gains. \u201cSay you are working with an increasingly depleted deposit and trying to eke out whatever you can from what remains. Access to the \u2018superhuman senses\u2019 of machine learning can provide additional data that can be used to refine processes further,\u201d Sahu continued.<\/p>\n\n\n\n Take blast design as an example. By analyzing a range of data sources \u2013 from drill data to post-blast fragmentation, vibration prediction and hyperspectral imaging \u2013 machine learning allows blasters to:<\/p>\n\n\n\n Other applications might include using a drone and machine learning to provide daily accurate stockpile measurements, something that companies may previously only have been able to afford annually. \u201cThe same is true of slope failure prediction,\u201d said Sahu. \u201cWhereas previously, companies would have needed outside experts to come in and conduct the analysis, this can now be done quickly and affordably in-house.\u201d <\/p>\n\n\n Identify your objectives<\/strong> \u201cThere are obvious individual use cases when it comes to plant, safety devices, environmental remediation, etc., but where mining companies will find true value is in being able to use AI across the entire mining lifecycle and its lifetime,\u201d continued McBride. <\/p><\/blockquote>\n\n\n\n \u201cWith complete data sets plugged into highly interoperable platforms, mining companies can use their data to get complete real-time visibility of everything and anything within their organization and beyond, get accurate predictive insights that improve over time with growing data sets, and get the ability to optimize, on an ongoing basis, based on changing operational and business needs.\u201d<\/p>\n\n\n\n \u201cWhen you focus on improving a single piece of the puzzle, you could be inadvertently causing increased expense or waste elsewhere,\u201d agreed Ravi Sahu of Strayos. \u201cA holistic view of mining is therefore key to true performance improvement. For example, drilling data informs blast design, blast design affects vibration, vibration affects slope stability and, thus, pit design. So ultimately, data pulled from drills can assist with resource and geological modelling, pit design and compliance.\u201d<\/p>\n\n\n\n When considering AI implementation, McBride suggests asking the following questions: <\/p>\n\n\n\n A bit more about data<\/strong> This makes a company\u2019s IoT strategy critical, as it\u2019s this that will ensure the right data is collected, as it is being generated (i.e., in real time), and then cleansed, standardized and maintained in a way that makes it accessible to be leveraged by machine learning algorithms. Only then can machine learning become true AI as it results in \u201creal-time interventions, executed through augmentation or automation, before optimization opportunities are lost or before unwanted events occur,\u201d continued McBride. <\/p>\n\n\n\n A particular challenge comes with equipment data that are not interoperable. \u201cThe onus is on mining companies to make sure that their data stays qualitative and meaningful to use in other systems when it\u2019s transferred,\u201d continued Sahu. To do so requires investment in \u201cdata quality, data management and data governance frameworks on a centralized system, such as the cloud, and the definition of interoperable models with OEMs and system integrators.\u201d<\/p>\n\n\n\n Get the data right, and machine learning is the gift that keeps giving. \u201cOver time, and with growing data sets, machine learning algorithms will find new and more effective ways to operate (they get better \u2013 another key point),\u201d said McBride. \u201cMultiple algorithms can also be applied to the same data to experiment with new use cases or to test alternative approaches (via digital twins).\u201d<\/p>\n\n\n\n What about the people? <\/strong> The \u201cbiggest prerequisite\u201d for adopting AI is buy-in from the team, said Sahu. \u201cAI is flexible and can be applied at any level to an operation. But you need to have people willing to implement it. AI can be sabotaged by workers who refuse to use it, refuse to learn it or intentionally provide it bad data. There will be many people who are reluctant to change for any number of reasons, and thanks to Hollywood, AI often has a bad reputation that needs to be overcome.\u201d<\/p>\n\n\n\n Added McBride: \u201cWe\u2019re not going to wake up in a dystopian future where AI is running all of our operations and we\u2019re not needed \u2013 not in my lifetime, anyway.\u201d To return to Oz for a moment, this was the fear behind Tin Man: the human worker slowly replaced by machine \u2013 of industry that had lost its heart. It\u2019s an image that has cast a long shadow; the reality is much more nuanced, however. <\/p>\n\n\n\n \u201cThere will probably be fewer jobs in some fields, due to labor-replacing innovations such as automation, drones and the IoT,\u201d said a 2021 report into the use of digital technologies in mining from the International Institute for Sustainable Development. \u201cAt the same time, there will be new higher-paying jobs created in high-skilled occupations, such as information technology and engineering.\u201d<\/p><\/blockquote>\n\n\n\n This will place the mining industry in direct competition with other sectors, notably the technology sector. As a result, a portion of the mining workforce is likely to migrate away from the mine site to centralized hubs where such skills are more freely available (something that\u2019s already seen in Western Australia\u2019s autonomous truck haulage operations). This could be problematic when it has a negative economic impact on local communities in mining regions, who are seeing mining employment levels decline. Mining companies may therefore need to invest in skills and training policies that help locals compete for these new jobs in order to support continuing social license to operate. <\/p>\n\n\n\n It’s also important to remember the workforce and wider social benefits of AI. \u201cIt\u2019s going to help free up time for workers so that they will be able to focus their attention on more important matters,\u201d said Sahu. \u201cThis will reduce the number of unfillable vacancies. Additionally, adopting technology and creating new kinds of jobs could be the lure to pull young people, who wouldn\u2019t otherwise have been interested, into the industry. Finally, the improvement of the industry, as enabled by AI \u2013 smarter, safer, more sustainable \u2013 could help to begin a perception shift among the general population that finally retires some of the outdated stereotypes.\u201d<\/p>\n\n\n\n The future is AI: It has to be<\/strong> The reason for such certainty is simple: mining cannot do without it. As McBride said, \u201cAI is going to be key in achieving the incredibly complicated, multivariable things that are now being asked of the mining sector, i.e., ensuring profitability, whilst supplying the material needed for energy transition, all whilst slashing emissions, improving workplace safety and being better world citizens. Fundamentally, the path to 2050 is wholly reliant on the mining sector embracing the technology.\u201d <\/p>\n\n\n Artificial intelligence has long appeared in science fiction. But it\u2019s now coming to a mine near you (if it\u2019s not already there). By Jonathan Rowland In the early years of the 20th century, American author L. Frank Baum created the Land of Oz \u2013 and with it, two contrasting examples of living, intelligent machines. 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As with all complex concepts, AI can be difficult to define. There are also many associated terms thrown around, notably (and going back to Turing for a moment) the idea of machine learning, which is often used interchangeably with AI. What then are the keys ideas to understand? <\/p>\n\n\n\n
Much of what is labelled AI in the mining industry today is variations on machine learning. Not that this should downplay the benefits of this technology, which are significant (and for most, the semantics are less important than the results). <\/p>\n\n\n\n![\"\"](\"https:\/\/northamericanmining.com\/wp-content\/uploads\/2022\/04\/NAM-MA2022-F1-1.png\")
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There is then very broad scope to where machine learning and AI can be implemented in the mining industry. To reap most reward, however, it is important to look beyond individual steps and consider the complete mining process, tying implementation to specific business targets. <\/p>\n\n\n\n
\u201cMost people think the challenge with AI relates to the complexity of the algorithm,\u201d said McBride. \u201cHowever, that\u2019s just a piece of the jigsaw, and normally the algorithms are not actually that complex; there are many tools and methodologies in place now for solving that problem. The bigger challenge for most businesses comes down to the data.\u201d <\/p>\n\n\n\n
People are a key consideration during the implementation of any new technology. Balancing human intelligence with machine intelligence is recurring theme within Industry 4.0 and is particularly true when talking about machine learning and AI, as they can be seen to threaten \u2013 even replace \u2013 the human operators. But AI still works better in partnership with human operators. <\/p>\n\n\n\n
AI will \u201crevolutionize the mining industry,\u201d concluded Sahu. \u201cIt\u2019s only a matter of time before the advantages of AI become so clear that it will be impossible not to fully implement. It\u2019s actually already being implemented \u2013 you just don\u2019t notice it because it\u2019s not the flashy, in-your-face AIs, like autonomous vehicles. But one day, it will be ubiquitous.\u201d <\/p>\n\n\n\n![\"\"](\"https:\/\/northamericanmining.com\/wp-content\/uploads\/2022\/04\/Mar22_NAM_AI_and_the_Mining_Industry-image2-Muckpile_Fragmentation_3D.png\")
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