3
applied to problems like truck dispatching, where each dis-
patch affects the state of the mining, affecting the entire
shift [7]. Like Go, there’s a huge number of possibly con-
figurations and subtle interactions, but this is an area that
now can be tackled by AI.
This work is very new. Reinforcement learning tech-
niques have recently been combined with techniques from
deep learning to help agents understand complex data
as well as gain new observations from things like images
rather than simpler data sets. These approaches show a lot
of promise for tackling complex problems in mining opera-
tions, full of unknowns and uncertainty.
Generative AI
Some AI architectures from deep learning can generate
images, text, and more using the advancements in deep
learning. This is a fast-moving area of AI right now, which
is under a lot of active research.
Text generation especially is very powerful, and it can do
a surprising number of tasks. The large pre-trained models
like ChatGPT, which do these text output cases, are known
as large language models (LLMs.) These models take input,
which is usually text, referred to as prompts, and generate
relevant responses. Despite the power of these models, the
prompts need to be created intelligently. Prompts need to
be clear and contain enough information for the model to
be able to generate a meaningful response.
To solve this prompting problem, in-context learning
is used, where data from specific datasets is injected into the
prompt. In this area, people have added the ability to chat
about the contents of PDF files, books, and more. This has
created chat interfaces that can quickly search through huge
amounts of data while providing citations to the injected
data to be sure that the AI isn’t just hallucinating incorrect
outputs. [8]
LLMs have also been extended with the ability to use
tools. Code is itself just text, so it can be generated quite
effectively by LLMs and then executed. Beyond program-
ming languages, there are formats for transmitting data
(like CSV files and JSON files, which use text to represent
data), and using these LLMs can be hooked up to interact
with programs or even the real world, enhancing their abili-
ties. One case is to let an LLM query a database [9], which
can be used to answer questions with accurate data even in
a complex environment like a mine.
Having a tool that can do intelligent searches through
large, unstructured data, provide answers, and more can
be a large performance booster. Mining has a lot of docu-
ments, processes, and a lot of complex data, and LLMs can
help users sort through it all.
Reasoning
Reasoning in classic symbolic AI is defined as using the pro-
vided rules to figure out new, implied data. This is like how
humans deduce new facts from known information.
Reasoning with LLMs is more general, involving the
ability to think through complex multi-step problems.
While LLMs can do some reasoning naturally due to the
large number of problems they’ve seen in their training
data, they’ve struggled with more complicated problems.
One example of this is solving a Sudoku puzzle. Solving
this would be extremely difficult for an LLM that hasn’t
seen that specific Sudoku puzzle before, even though the
rules are clear.
Recently, efforts have been made to combine LLMs
with Reinforcement Learning to solve these sorts of prob-
lems, with OpenAI releasing a first version of this technol-
ogy with their o1-preview model[10]. As this technology
advances, these reasoning AIs will be able to do more com-
plex optimization cases automatically. Combined with
in-context learning and the correct data being provided,
reasoning LLMs will be able to think through and give
answers to complex mining problems where many elements
interact.
Summary
AI has had several distinct but complementary approaches.
From solving explicit, well-defined problems up to han-
dling uncertain, complex problems that require reasoning,
different AI techniques and combinations of techniques
can prove useful.
The common thread between all these techniques is
having good data and understanding of that data. From the
data used to train machine learning models to getting data
to use with in-context learning to the understanding of the
data to define rules and relationships in expert systems, this
data work is required to work with AI.
As AI continues to evolve, proper organization and
understanding of underlying data will remain key.
AI-READY KNOWLEDGE GRAPH
The previous section highlighted many different AI
approaches and how they relate to mining. However, to
effectively leverage those AI technologies, data needs to be
organized in a way that makes it accessible. Data organi-
zation will allow for rapid development of AI solutions,
which can create outsized effects. This section presents a
knowledge graph architecture that achieves this type of
accessible, organized data.
The proposed knowledge graph is made up of four
components that work together synergistically: business
applied to problems like truck dispatching, where each dis-
patch affects the state of the mining, affecting the entire
shift [7]. Like Go, there’s a huge number of possibly con-
figurations and subtle interactions, but this is an area that
now can be tackled by AI.
This work is very new. Reinforcement learning tech-
niques have recently been combined with techniques from
deep learning to help agents understand complex data
as well as gain new observations from things like images
rather than simpler data sets. These approaches show a lot
of promise for tackling complex problems in mining opera-
tions, full of unknowns and uncertainty.
Generative AI
Some AI architectures from deep learning can generate
images, text, and more using the advancements in deep
learning. This is a fast-moving area of AI right now, which
is under a lot of active research.
Text generation especially is very powerful, and it can do
a surprising number of tasks. The large pre-trained models
like ChatGPT, which do these text output cases, are known
as large language models (LLMs.) These models take input,
which is usually text, referred to as prompts, and generate
relevant responses. Despite the power of these models, the
prompts need to be created intelligently. Prompts need to
be clear and contain enough information for the model to
be able to generate a meaningful response.
To solve this prompting problem, in-context learning
is used, where data from specific datasets is injected into the
prompt. In this area, people have added the ability to chat
about the contents of PDF files, books, and more. This has
created chat interfaces that can quickly search through huge
amounts of data while providing citations to the injected
data to be sure that the AI isn’t just hallucinating incorrect
outputs. [8]
LLMs have also been extended with the ability to use
tools. Code is itself just text, so it can be generated quite
effectively by LLMs and then executed. Beyond program-
ming languages, there are formats for transmitting data
(like CSV files and JSON files, which use text to represent
data), and using these LLMs can be hooked up to interact
with programs or even the real world, enhancing their abili-
ties. One case is to let an LLM query a database [9], which
can be used to answer questions with accurate data even in
a complex environment like a mine.
Having a tool that can do intelligent searches through
large, unstructured data, provide answers, and more can
be a large performance booster. Mining has a lot of docu-
ments, processes, and a lot of complex data, and LLMs can
help users sort through it all.
Reasoning
Reasoning in classic symbolic AI is defined as using the pro-
vided rules to figure out new, implied data. This is like how
humans deduce new facts from known information.
Reasoning with LLMs is more general, involving the
ability to think through complex multi-step problems.
While LLMs can do some reasoning naturally due to the
large number of problems they’ve seen in their training
data, they’ve struggled with more complicated problems.
One example of this is solving a Sudoku puzzle. Solving
this would be extremely difficult for an LLM that hasn’t
seen that specific Sudoku puzzle before, even though the
rules are clear.
Recently, efforts have been made to combine LLMs
with Reinforcement Learning to solve these sorts of prob-
lems, with OpenAI releasing a first version of this technol-
ogy with their o1-preview model[10]. As this technology
advances, these reasoning AIs will be able to do more com-
plex optimization cases automatically. Combined with
in-context learning and the correct data being provided,
reasoning LLMs will be able to think through and give
answers to complex mining problems where many elements
interact.
Summary
AI has had several distinct but complementary approaches.
From solving explicit, well-defined problems up to han-
dling uncertain, complex problems that require reasoning,
different AI techniques and combinations of techniques
can prove useful.
The common thread between all these techniques is
having good data and understanding of that data. From the
data used to train machine learning models to getting data
to use with in-context learning to the understanding of the
data to define rules and relationships in expert systems, this
data work is required to work with AI.
As AI continues to evolve, proper organization and
understanding of underlying data will remain key.
AI-READY KNOWLEDGE GRAPH
The previous section highlighted many different AI
approaches and how they relate to mining. However, to
effectively leverage those AI technologies, data needs to be
organized in a way that makes it accessible. Data organi-
zation will allow for rapid development of AI solutions,
which can create outsized effects. This section presents a
knowledge graph architecture that achieves this type of
accessible, organized data.
The proposed knowledge graph is made up of four
components that work together synergistically: business