2
where rules will take the base set of data in a database
and define new data that’s well defined by that base data,
recursively. Tasks here generally focused on understanding
the relationships between certain “things,” such as under-
standing that the brother of your parent is your uncle. This
sort of framing worked well for certain well-defined cases,
including diseases and categorizing species.
While this type of AI struggles with uncertainty, it can
be very effective in cases where things are well-known, and
some of these systems are still used today. The OWL frame-
work has been used to organize data on the web. Ontologies
and knowledge graphs are created as part of a mapping step
before doing potentially costly tasks like creating data ware-
houses[2] because it forces a rigorous understanding of an
area before investing time and resources. It hasn’t had as
many direct use cases in mining, though it can still be valu-
able in organizing and understanding complex mining data
and its relationships.
Deterministic Optimization
Operations Research (OR) is the discipline of using analyti-
cal methods to improve decision-making. The boundary of
this and AI is often blurred, as methods that can generically
be used to make smart decisions can be thought of as “arti-
ficial intelligence.” In mining, optimization methods from
OR are used widely, though not often considered to be AI.
Importantly, these also can solve these problems optimally,
finding the mathematically best result.
One example of this is scheduling problems. Starting
with the Simplex algorithm, which was invented by Dantzig
in the 1940s, these sorts of problems can be solved with
exact solutions using tools like mixed-integer linear pro-
gramming. With a well-defined objective and constraints,
optimal answers can be found. Work in this area has con-
tinued up to today, being able to handle large, complex
cases like optimized direct block scheduling.
A related area is the Pseudoflow algorithm [3], an
algorithm which can solve for optimal flows in a network,
which is equivalent to some other optimization tasks. This
algorithm is often used for pit optimization for example,
but it’s useful across a range of tasks that require picking
an optimal set given explicit constraints. It can be thought
of as a binary integer programming method in this sense.
This can be used to optimize underground workings, for
example, where each piece of construction has a cost, but
it unblocks a set of further things that can be built. Using
Pseudoflow, you can find the optimal set of things to build
where you’d make the most money.
Like the symbolic AI, these methods work great where
there’s a clear objective and no uncertainty. It’s important
to realize that these methods are only as “optimal” as how
correctly they model reality. Using an optimal method on
estimations where there can also be unknown effects in
operations means the result may not be the optimal method
to execute.
Deep Learning
Machine learning has expanded quite a bit in recent years,
especially in deep learning. Deep Learning refers to neu-
ral networks with multiple layers, allowing them to learn
more complex relationships and patterns from data.
Advancements like convolutional neural networks have
allowed images to be learned from efficiently, expanding
neural networks from just working with numbers.
Different combinations of neural network layers are
called architectures. For example, the transformers architec-
ture[4], which is used in ChatGPT, has made it possible for
AI to understand and respond in natural language. There
are other architectures that allow for many other useful
things, such as autoencoders, which, among other things,
can help understand the similarity between abstract con-
cepts by learning the most important patterns that appear
in data sets.
At this point, audio, pictures, and video can also all be
used. Neural networks can now efficiently do translation,
transcription, image recognition, and more. In mining, this
can be used for things like estimating fragmentation from
blast face images or analyzing material on conveyor belts
with just simple imaging.
Reinforcement Learning and Stochastic Optimization
Stochastic optimization is optimization that works even
with uncertainty, unknowns, and randomness. This makes
it very useful for mining, handling the real-world complica-
tions that come with mining operations.
Reinforcement learning (RL) is a key tool for doing
stochastic optimization. It involves an agent which learns
by taking actions and learning from their results, like how
a person learns from experience. Over time, an agent can
gradually learn how to handle complex problems, achieving
superhuman performance.[5]
This approach is used in sequential decision problems,
which are problems where each choice affects future results.
A famous example is the AlphaGo AI [6], which plays the
game Go at a superhuman level. Go is a complex board
game, with more possible board combinations than atoms
in the observable universe. Reinforcement learning allowed
AlphaGo to defeat the best players in the world, whereas
previously, it was difficult to make an AI that played the
game well at all. In mining, this same approach can be
where rules will take the base set of data in a database
and define new data that’s well defined by that base data,
recursively. Tasks here generally focused on understanding
the relationships between certain “things,” such as under-
standing that the brother of your parent is your uncle. This
sort of framing worked well for certain well-defined cases,
including diseases and categorizing species.
While this type of AI struggles with uncertainty, it can
be very effective in cases where things are well-known, and
some of these systems are still used today. The OWL frame-
work has been used to organize data on the web. Ontologies
and knowledge graphs are created as part of a mapping step
before doing potentially costly tasks like creating data ware-
houses[2] because it forces a rigorous understanding of an
area before investing time and resources. It hasn’t had as
many direct use cases in mining, though it can still be valu-
able in organizing and understanding complex mining data
and its relationships.
Deterministic Optimization
Operations Research (OR) is the discipline of using analyti-
cal methods to improve decision-making. The boundary of
this and AI is often blurred, as methods that can generically
be used to make smart decisions can be thought of as “arti-
ficial intelligence.” In mining, optimization methods from
OR are used widely, though not often considered to be AI.
Importantly, these also can solve these problems optimally,
finding the mathematically best result.
One example of this is scheduling problems. Starting
with the Simplex algorithm, which was invented by Dantzig
in the 1940s, these sorts of problems can be solved with
exact solutions using tools like mixed-integer linear pro-
gramming. With a well-defined objective and constraints,
optimal answers can be found. Work in this area has con-
tinued up to today, being able to handle large, complex
cases like optimized direct block scheduling.
A related area is the Pseudoflow algorithm [3], an
algorithm which can solve for optimal flows in a network,
which is equivalent to some other optimization tasks. This
algorithm is often used for pit optimization for example,
but it’s useful across a range of tasks that require picking
an optimal set given explicit constraints. It can be thought
of as a binary integer programming method in this sense.
This can be used to optimize underground workings, for
example, where each piece of construction has a cost, but
it unblocks a set of further things that can be built. Using
Pseudoflow, you can find the optimal set of things to build
where you’d make the most money.
Like the symbolic AI, these methods work great where
there’s a clear objective and no uncertainty. It’s important
to realize that these methods are only as “optimal” as how
correctly they model reality. Using an optimal method on
estimations where there can also be unknown effects in
operations means the result may not be the optimal method
to execute.
Deep Learning
Machine learning has expanded quite a bit in recent years,
especially in deep learning. Deep Learning refers to neu-
ral networks with multiple layers, allowing them to learn
more complex relationships and patterns from data.
Advancements like convolutional neural networks have
allowed images to be learned from efficiently, expanding
neural networks from just working with numbers.
Different combinations of neural network layers are
called architectures. For example, the transformers architec-
ture[4], which is used in ChatGPT, has made it possible for
AI to understand and respond in natural language. There
are other architectures that allow for many other useful
things, such as autoencoders, which, among other things,
can help understand the similarity between abstract con-
cepts by learning the most important patterns that appear
in data sets.
At this point, audio, pictures, and video can also all be
used. Neural networks can now efficiently do translation,
transcription, image recognition, and more. In mining, this
can be used for things like estimating fragmentation from
blast face images or analyzing material on conveyor belts
with just simple imaging.
Reinforcement Learning and Stochastic Optimization
Stochastic optimization is optimization that works even
with uncertainty, unknowns, and randomness. This makes
it very useful for mining, handling the real-world complica-
tions that come with mining operations.
Reinforcement learning (RL) is a key tool for doing
stochastic optimization. It involves an agent which learns
by taking actions and learning from their results, like how
a person learns from experience. Over time, an agent can
gradually learn how to handle complex problems, achieving
superhuman performance.[5]
This approach is used in sequential decision problems,
which are problems where each choice affects future results.
A famous example is the AlphaGo AI [6], which plays the
game Go at a superhuman level. Go is a complex board
game, with more possible board combinations than atoms
in the observable universe. Reinforcement learning allowed
AlphaGo to defeat the best players in the world, whereas
previously, it was difficult to make an AI that played the
game well at all. In mining, this same approach can be