The opinion of experts agrees: it is impossible to survive in any market without being constantly attentive to technological developments, especially in the fields of artificial intelligence and data science. These systems govern the world, since they serve to explain the past, interpret the present and anticipate – wisely – the future.
For example, the so-called “data-centered artificial intelligence” is allowing companies and institutions of all types to operate with more abundant and precise information—from citizens, taxpayers, users, clients. This work is carried out without affecting its security and without affecting operations due to its complexity or scope.
Natural language processing continues to expand thanks to the ability of machines to better understand how humans communicate. The interaction between automatic devices and flesh and blood beings has never been more fluid thanks to this innovation. And the same can be said for machine learning platforms.
Without the need for any professional to intervene, these tools carry out experiments to constantly correct errors and enhance successes, without becoming demoralized or discouraged by mistakes, and without becoming overconfident or boasting about excellence. They are instruments that always respond the same, regardless of whether it is managing goods in a seaport or supplying food to a school cafeteria. For engineers, that is not a defect, but a guarantee.
Its complement is located in pattern detection, with a term that is causing a sensation on a global scale: “Edge AI”. This form of “on the edge” artificial intelligence allows rapid adaptation to a changing reality—in fact, in real time—while maintaining the privacy of the material collected from the subjects or organizations that are immersed in these tasks.
Gartner consultants predict that, in 2025, 55% of the data study carried out by deep neural networks will be produced in this way, compared to just 10% in 2021. IBM technicians describe predictive analytics as the formula that combines machine learning, data mining and statistics based on historical content to avoid unexpected surprises in areas such as the climate emergency, scientific research or medical care.