Artificial intelligence (AI) is used in many sectors and applications, but
not so much in the mining space. That makes no sense because AI is an
extremely useful tool in dealing with challenges that have tremendous amounts
of data and involve hundreds of variables. However, AI is just a tool, it's
not a silver bullet solution. For mining, the area most in need of what AI
has to offer is exploration for new discoveries.
The costs of exploration are going up. The easy stuff has already been
found, the higher-grade ore already mined. Most exploration programs are
searching in part, or entirely, for mineralized zones that are
"undercover" (under overburden). Overburden is worthless rock or
soil overlying a mineral deposit. It's the enemy of exploration geologists
because it's very difficult to "see through." Various tools are
deployed to understand what's beneath the overburden: geophysics;
geochemistry, etc.
But each property is unique. What works well in one place might not work
at all in another. Exploration (not just for metals) is the perfect
setting—high uncertainty, lots of data, lots of variables, the potential for
a big reward and substantial cost savings—for AI to be a valuable,
cost-effective tool to aid geologists. Albert
Mining's (AIIM:TSX.V) technology will never replace geologists, only
assist them. Income tax software has been around for >20 years, but it has
not replaced tax accountants—not even close.
Albert Mining has been using AI, machine learning and data mining for less
than a decade. Clients benefit from a multidisciplinary team that includes
professionals in geophysics, geology, AI and mathematics. Most of the team
has been together for six to 10 years; they have 30+ proven discoveries.
Management says it has a 70% success rate in identifying new zones of
mineralization. If a gambler could be right just 60% or 65% of the time, he
or she would become quite wealthy. Albert Mining's success rate is not higher
because sometimes there's no additional mineralization to be found. Or,
there's not enough data for the technology to operate at an optimal level, or
the geology is simply too difficult to understand.
Each time the company's CARDS (Computer-Aided Resources Detection System)
finds something, it saves the client considerable exploration time and money,
and frees up managerial resources. The benefits are many, for all
stakeholders, when there's a successful outcome.
Let me throw in this testimonial by Ron Perry, then a director of Metanor
Resources, that I paraphrased from a recently shot video [see 2-minute video clip].
This was an Albert Mining success story from 2009-10 that's going into
production. How many exploration projects make it to production? Not many!
Ron Perry, formerly of Metanor Resources: "I met Michel [Fontaine] of
Albert Mining in 2009, He came into our booth at the Cambridge show, it
sounded like he had some proprietary algorithms. We hit it off. I approached
our VP of Exploration. I told him, 'If we use Albert Mining's technology and
find something, it's your discovery. If not, just blame me, the finance guy'.
. .In 2010 we made a discovery, off the main road, in an area that had no
inkling or smell of gold. At the same time, Michel's team did some mapping
around our tailings pond and there was another discovery, and that discovery
is now going into production. A pretty amazing technology. . ."
What's Albert Mining's Secret Weapon?
Albert Mining owns 100% of a proprietary software package called CARDS, a
state-of-the-art computer system used to identify areas with a high
statistical probability of containing mineral deposits. High statistical
probability is a key phrase: CARDS does not provide certainty; no system of
any kind can. The backbone of CARDS is a knowledge extraction data mining
engine that uses pattern recognition algorithms to learn the signatures of
positive and negative data points to create a model that makes predictions on
the positive or negative nature of new data points. CARDS uses powerful
algorithms to analyze digitally compiled exploration data and identify zones
with a high potential for discovery.
How Does All of This Work?
Data is entered into CARDS in the form of a geo-referenced database. Each
data point, or "cell," in the database is linked to its own set of
criteria extracted from geophysical surveys, drill and rock sample assays,
geological maps, etc. It's critical that as much data as possible is captured
and logged. Importantly, the system can take many different forms of data,
(geophysics, geochemistry, topography, satellite, geology, faults, spatial
data, etc.) which is why it's such a robust predictive tool. The data is
divided into two databases.
The first database includes cells with known assay results (drill
hole/rock samples data, etc.) and is used to develop—to learn—a model of the
geological target being sought. The model could be set to identify targets
containing >5 g/t gold. The second database, equally important, includes
cells with no assay results. Complex algorithms are used to identify those
cells that have a high similarity to the signatures of positive mineral
deposits. In addition, in the analysis of each cell, the characteristics of
cells within a specified distance, in the same neighborhood, are weighed into
the evaluation of that cell.
Therefore, even cells lacking data can be effectively evaluated if the
combination of their limited characteristics, and their proximity to cells
with other significant characteristics, is similar to that of cells with
known positive results. Unlike rule-based computer models, CARDS is not
biased by the rules of any particular geologic model. In fact, because of
CARDS' ability to learn and make predictions based on the signatures of
multiple positive data points, it can make predictions on any geological
deposit type.
Once the data has been crunched, which typically takes several weeks,
prospective targets generated by CARDS are evaluated with the client, and
both parties discuss and outline potential exploration and drill targets.
New Business Model, New Chairman, New Sectors: A New Albert Mining
In the past, this is where Albert Mining's work ended. They would provide a
valuable service and move on. However, all of that is about to change. The
company, with the support of large shareholders, is taking ownership stakes
in companies it does work for. In addition, a new chairman should be named
shortly. He or she will be well versed in this new operating model. The
company's objective is to develop new revenue streams by participating in the
exploration success of its AI-assisted exploration.
Albert Mining is currently running a program in Norway for Playfair Mining
(PLY:TSX.V). Management recently invested CA$100,000 in Playfair at CA$0.05
(2 million shares) and signed a CA$75,000 service agreement. The RKV project
covers two past-producing volcanogenic massive sulfide (VMS) copper mines
(Kvikne and Rostvangen), a magmatic nickel-copper deposit (Vakkerlien) and
>20 additional mineral occurrences. Management will use its proprietary
technology to analyze a large amount of geophysical, geochemical and
geological data to uncover patterns hidden in Playfair's data. It will then
run those machine-learned patterns through its algorithms to identify
prospective targets.
This technology has been successful in assisting geologists in the
identification of a number of major mineral discoveries, especially in the
context of VMS mining districts. As management continually tweak CARDS, they
will now benefit more directly, and more substantially, from their high
success rate.
CEO Michel Fontaine tells me his company will look a lot different in three
or four months. A new chairman, a new business model, perhaps programs
outside of just mining. Readers may recall that in a recent interview with Michel he mentioned searching for
abandoned underground land mines and exploring for water as possible new
business segments. Any high-value field or application where there's a lot
of complex data is ideally suited for CARDS.
As CEO Michel Fontaine said in our interview last month: "We feel
this is just the beginning of something potentially much bigger. No other
company has the breadth and& depth, the vast experience in AI technology
for mining (and new sectors) that we do. No one."
Peter Epstein is the founder of Epstein Research. His
background is in company and financial analysis. He holds an MBA degree in
financial analysis from New York University's Stern School of Business.
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understand and agree that nothing contained herein, written by Peter Epstein
of Epstein
Research [ER], (together, [ER]) about Albert
Mining, including but not limited to, commentary, opinions, views,
assumptions, reported facts, calculations, etc. is not to be considered
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