XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3 1073
can be considered a deep learning algorithm (IBM,
2022).
• Machine Learning is more dependent on human
intervention to learn. Human experts determine the
hierarchy of features to understand the differences
between data inputs, usually requiring more struc-
tured data to learn (IBM, 2022). Machine Learning
needs data to learn from and build prediction models.
Large-language models (LLMs) that are used in
ChatGPT ®, Bard ®, and any generative image pro-
cessing software are trained on large datasets sourced
from the internet, books, images etc.
• Artificial Intelligence encompasses all software
architecture levels, where deep neural networks and
machine learning are used to increase predictability
and mimic human behaviour.
Since its inception, Artificial Intelligence has been widely
used in several software systems and applications mimick-
ing human behaviour. The most common applications of
AI systems are:
• Speech Recognition—Smartphones use ASR (auto-
mated speech recognition) software to convert speech
to text or recognize speech for actionable tasks.
• Computer Vision—Computer vision is widely used
in manufacturing and automation to identify defects
and distinguish images or objects. Computer vision
is the backbone of most autonomous vehicles or self-
driving cars.
• Recommended Engines—AI is widely used in data
science to sift through large volumes of data to iden-
tify patterns. These techniques are commonly used
by biotech, mining, oil and gas industries to discover
new opportunities and insights.
• Virtual Agents—Virtual agents mimic humans on
various websites, providing customer experience and
online service.
Figure 2. Narrow vs General AI (Chandramohan, 2023)
Figure 3. Levels of software architecture (modified, GMG,
2022)
can be considered a deep learning algorithm (IBM,
2022).
• Machine Learning is more dependent on human
intervention to learn. Human experts determine the
hierarchy of features to understand the differences
between data inputs, usually requiring more struc-
tured data to learn (IBM, 2022). Machine Learning
needs data to learn from and build prediction models.
Large-language models (LLMs) that are used in
ChatGPT ®, Bard ®, and any generative image pro-
cessing software are trained on large datasets sourced
from the internet, books, images etc.
• Artificial Intelligence encompasses all software
architecture levels, where deep neural networks and
machine learning are used to increase predictability
and mimic human behaviour.
Since its inception, Artificial Intelligence has been widely
used in several software systems and applications mimick-
ing human behaviour. The most common applications of
AI systems are:
• Speech Recognition—Smartphones use ASR (auto-
mated speech recognition) software to convert speech
to text or recognize speech for actionable tasks.
• Computer Vision—Computer vision is widely used
in manufacturing and automation to identify defects
and distinguish images or objects. Computer vision
is the backbone of most autonomous vehicles or self-
driving cars.
• Recommended Engines—AI is widely used in data
science to sift through large volumes of data to iden-
tify patterns. These techniques are commonly used
by biotech, mining, oil and gas industries to discover
new opportunities and insights.
• Virtual Agents—Virtual agents mimic humans on
various websites, providing customer experience and
online service.
Figure 2. Narrow vs General AI (Chandramohan, 2023)
Figure 3. Levels of software architecture (modified, GMG,
2022)