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The future is electric.
未來是電動的。
That means it will need a lot of batteries, motors and wires.
這意味著我們將需要大量的電池、馬達和電線。
That, in turn, means a lot of cobalt, copper, lithium and nickel with which to build them.
反過來，這也意味著我們需要大量的鈷、銅、鋰和鎳來制造它們。
Great times, then, for prospectors, and particularly for any who think they can increase the efficiency of their profession.
因此，對探礦者來說，尤其是對那些認為自己可以提高行業效率的人來說，這是一個大好時期。
Several firms are applying artificial intelligence (AI) to the process, both to improve the odds of surface strikes and to detect underground ore bodies that are invisible to current techniques.
幾家公司正在將人工智能(AI)應用于這一過程，既提高了發現地表礦物的幾率，又能探測到現有技術無法發現的地下礦體。
KoBold Metals in Berkeley, California, Earth AI in San Francisco and Verai in Boston are tiddlers at the moment, as are SensOre, in Melbourne and OreFox, in Brisbane.
目前，加州伯克利的KoBold Metals、舊金山的Earth AI和波士頓的Verai等公司都還處于起步階段，墨爾本的SensOre和布里斯班的OreFox也是如此。
But at least one bigger fish—Rio Tinto, an Australian-British firm—is also keen.
但至少有一條大魚——英澳合資的力拓公司——也很熱衷于這件事。
They are garnering reams of geological, geochemical and geophysical data to feed to software models.
他們正在收集大量的地質、地球化學和地球物理數據，以提供給軟件模型。
These, they hope, will spot patterns and draw inferences about where to sink new mines.
他們希望能夠找到固定模式，并推斷出在哪里挖新礦。
Some of the data are new.
其中一些是新的數據。
But a lot once mouldered in the archives of national geological surveys, journals of geology and other historical repositories—or, in the case of Rio Tinto, which has been operating for 150 years, sat in the form of rock cores in various sheds around the world.
但是很多都是曾在國家地質調查檔案、地質學期刊和其他歷史儲存庫中存放太久的數據，或者，就像里約廷托的情況一樣，它已經運作了150年，以巖芯的形式在世界各地的各種倉庫中存放。
Kobolds were mythical underground sprites that bedevilled miners in medieval Germany.
Kobold是一種神秘的地下精靈，在中世紀的德國一直困擾著礦工。
(They also gave their name to cobalt.)
(他們也給鈷取了名字。)
Kurt House, KoBold’s boss, hopes some of their magic will rub off.
KoBold的老板庫爾特·豪斯希望他們的一些神奇手段能夠奏效。
His firm has reformatted archive data from around the world, many of which are on paper and some of which go back to the 19th century, into machine-useable form.
他的公司以機器可用的形式重新編排了來自世界各地檔案數據的格式，其中許多是紙上的，還有一些可以追溯到19世紀。
That has permitted it to build maps of areas of interest all over Earth’s surface.
這使得該公司能夠繪制出地球表面所有有意思區域的地圖。
Some of those maps are used to train the company’s AI models.
其中一些地圖被用來訓練該公司的人工智能模型。
Others are used to test that software’s effectiveness by checking how good it is at predicting known ore deposits on maps it has not previously seen.
另一些則用來測試該軟件的有效性，通過檢查它在預測以前未見過的地圖上已知礦藏的準確性。
If it passes, it can be let loose on under-explored places of interest, generating leads for KoBold’s geologists.
如果它通過了測試，就可以讓其在未開發的有意思的地方自由發揮，為KoBold的地質學家提供線索。
