Machine Learning in analytical intelligence

What is the role of machine learning in analytical intelligence?

Analytical intelligence consists of processing information, interpreting the results, and communicating the relevant findings to those who can make a decision based on that interpretation. It makes it possible to understand the behavior or state of a system and eases decision-making.

Understanding is a human activity, but information processing — and part of its interpretation — can be automated through software using machine learning techniques.

With the boom of Big Data, the Internet of Things (IoT/IIoT), and digital transformation, organizations handle volumes of data that are almost humanly impossible to analyze. That is where machine learning becomes relevant: it emulates some of the analytical processes humans perform, leveraging human knowledge and the available computing power.

“There are things that are easy for a human to do and hard for a computer to do.”

Computers can do things humans cannot (billions of calculations in very little time), but some things are relatively easy for a human and hard for a computer — such as interpreting a data point in full context. These conditions are what make machine learning valuable for automating analytical processes wherever large volumes of data must be analyzed frequently.

How does a machine learn?

When people talk about machine learning there is sometimes the fanciful expectation that a machine magically learns on its own and proposes better solutions than a human would. In reality, a machine can learn to perform predefined tasks through programs that adjust themselves based on measurements. The algorithms fall into two broad categories:

• Supervised learning: algorithms trained to detect events from a dataset already classified into a state (for example, positive/negative), then run on the data being recorded live.
• Unsupervised learning: algorithms that run on unstructured information and return a set of automatically identified patterns to which a state can be assigned.

What can a machine learn?

• Fine-tuning the rules of the game to reach a goal (adjusting parameters).
• Identifying patterns and trends.
• Processing images to recognize objects.
• Recognizing and interpreting sounds and language.

Although there is no physical limitation preventing a machine from imitating a human capability, there is one condition: that capability must be describable, explicitly or implicitly, by a human — it depends on human knowledge and descriptive capacity.

What to consider before starting a Machine Learning project

The organization must be aware of the limits, scope, use cases, and benefits of these AI techniques so results meet initial expectations. These techniques do not displace human knowledge: they amplify it, reducing the effort of running repetitive tasks over large volumes of data and freeing people to focus on decision-making.