Surely you have heard about Artificial Intelligence (AI) as something almost magical, but the reality is that you are not born “knowing.”
Just as we need years of school to understand the world, an AI needs a rigorous process to go from being a set of codes to a tool capable of helping us on a daily basis.
Training an AI is, in essence, teaching it to recognize patterns. It all starts by defining a clear problem: do we want it to identify photos of cats or predict the weather in Madrid?
From there, the basis of everything is data. Without quality, clean, well-curated data, the machine simply cannot learn properly.
In the following infographic we show you this fascinating journey from raw data to real intelligence:
As you have seen, training is not something that is done once and that’s it; It is an iterative cycle of trial and error.
It is not enough to “feed” the machine with information; you have to validate what you have learned with data you have never seen to ensure that you really understand and not just memorize.
This effort is worth it because a well-trained AI is much more precise, adaptable and efficient in the use of resources. Once the model is ready, it enters the inference phase, where it applies everything learned to give us answers in real time.
Have you been left wanting more? If you want to delve into the technical details of each stage and discover why this process is the heart of modern technology, we invite you to read our complete article on training an AI.
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