Emerging technologies such as artificial intelligence (AI) and machine learning are rapidly proving their value across many industries. Today's infographic comes from GoldSpot Discoveries, and it shows that when this tech is applied to massive geological data sets, that there is growing potential to unlock the next wave of mineral discoveries. Discovering new sources of minerals, such as copper, gold, or even cobalt, can be notoriously difficult but also very rewarding. According to Goldspot, the chance of finding a new deposit is around 0.5%, with odds improving to 5% if exploration takes place near a known resource. On the whole, mineral exploration has not been a winning prospect if you compare the total dollar spend and the actual value of the resulting discoveries.
Brazil's mining major Vale is set to start applying machine learning to identify new drilling targets at its Coleman nickel mine. Coleman Mine, which is the flagship asset of Vale in Ontario, Canada, is part of the company's base metals operations. Vale has selected technology company GoldSpot Discoveries to examine and analyse the vast amount of data acquired by it over decades of mining at Coleman. GoldSpot Discoveries' team of geologists and data scientists will also discover previously unrecognised data trends, which may point to unknown areas of in-depth mineralisation. By using its geoscience and machine science expertise, GoldSpot Discoveries' team will clean, unify and analyse exploration data from Vale's Coleman Mine.
Machine learning and artificial intelligence are becoming key components of mineral exploration programs as companies set exploration targets. Machine learning and artificial intelligence (AI) have the ability to solve two of the mining industry's biggest challenges: rising exploration costs and a lack of new discoveries. After a heavy downturn in the past few years, the mining and mineral exploration sector is finally starting to recover, but deep challenges remain. In an industry that thrives on new discoveries, today's resource companies are finding it harder and more expensive to locate new deposits. Gold provides one of the greatest examples of this dearth of new discoveries in the face of rising exploration costs.
STOCKHOLM – It is serendipity: from America to Viagra, history is full of great discoveries helped along by chance, as more than a century of Nobel prizes can attest. Among the chance discoveries that have been honored with the prestigious prize are X-rays (physics, 1901), penicillin (medicine, 1945), fullerenes that paved the way for nanotechnology (chemistry, 1996), conductive polymers (chemistry, 2000), and the bacteria responsible for ulcers (medicine, 2005). But, as the father of pasteurization Louis Pasteur noted in 1854, "In the fields of observation, chance only favors the prepared mind" -- a remark made in reference to the discovery of the link between electricity and magnetism by Danish scientist Hans Christian Orsted. Orsted happened to notice that a compass needle deflected from magnetic north when an electric current from a battery was switched on and off -- a pioneering discovery in electromagnetism. Like Pasteur, Dutch scientist Pek Van Andel also believes in the unexpected.
Rice University statistician Genevera Allen knew she was raising an important issue when she spoke earlier this month at the American Association for the Advancement of Science (AAAS) annual meeting in Washington, but she was surprised by the magnitude of the response. Allen, associate professor of statistics and founding director of Rice's Center for Transforming Data to Knowledge (D2K Lab), used the forum to raise awareness about the potential lack of reproducibility of data-driven discoveries produced by machine learning (ML). She cautioned her audience not to assume that today's scientific discoveries made via ML are accurate or reproducible. She said that many commonly used ML techniques are designed to always make a prediction and are not designed to report on the uncertainty of the finding. Her comments garnered worldwide media attention, with some commentators questioning the value of ML in data science.