Humankind (and all species in general) has always been in a constant competition for survival. And one of the most critical factors is the limited existence of resources.
We started by taking advantage of what was already available for direct consumption, we moved on to generating production techniques. And no matter how much time is observed, we will be improving and generating ways of making use of resources.
Technologically we could take, as a starting point, the Neolithic (about 12000 years ago), from which we have the oldest evidence of the development of agriculture and animal husbandry. Since then we have gone through three “Industrial Revolutions”, and we are in the fourth.
Machine learning is a concept that is quite a few years old. In short, a system (computer) is given data (depending on the algorithm, the amount varies) and it returns a programme. This program could solve, in a very precise way, relationships between patterns that are not easy to solve by other means.
A classic example would be to insert images of cats. And the trained machine learning model is then fed an image and it could give us the certainty of whether or not it is a cat.
Machine learning is now a tool that can help to optimise and make better decisions, to tackle climate change problems, supply chain problems and so on.
In industry, an abundance of data can be collected, which, when added to the machine learning process, could provide us with statistical data that would be difficult to consider by other methods. This would improve the decision-making capacity and optimisation of automated systems.
Today there are companies such as Innova IT, which are dedicated to the development of Analytics and Machine Learning for companies in the industrial sector; looking for the optimisation of processes driven by machine learning.
In industry, we will always need machine learning to improve processes; it is time to take the next step towards digital transformation.
Written in collaboration with Arturo Prado Romero
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