What Is Machine Learning And Why Is It Important To Innovate?
1950 was the year famous mathematician Alan Turing first raised the possibility that machines could think, paving the way for modern Artificial Intelligence. A decade later he moved towards artificial neural networks, a computational model inspired by the human mind that scientists Marvin Minsky and Dean Edmonds tested, managing to create a computer program capable of learning through experience.
These were the first steps of “machine learning” which, as data expert José Luis Espinoza explains, “is a master of pattern recognition, capable of converting a data sample into a computer program that draws inferences from new sets of data. data for which it has not been previously trained.” Thus, machine learning is a fundamental branch of Artificial Intelligence, responsible for allowing programs to learn without being expressly programmed for a purpose.
And although 1960 saw its formal appearance, the limited technology of the time also made it enter a period of inactivity that would last until the end of the 1990s, when the IBM Deep Blue system defeated the world multi-champion of that game in a game of chess. Garry Kasparov. With this context, at first glance, it may seem that machine learning is an aspect of high technology that is removed from the daily use of computers, as is, for example, quantum computing. However, this perception could not be more wrong, because unlike other innovations, machine learning lives with us every day. What is it for the common user? What potential does it still have for the most specialized uses?
The Mind Of A Computer
Since 1997 when the famous IBM chess machine achieved its representative victory, a line of research continues to the present day developing artificial intelligences that can learn to play strategy games, managing to defeat their greatest flesh and blood champions. However, these are experiments designed to test and increase your analytical capabilities in the laboratory, the question is where does machine learning really apply?
It is present in practically all the entertainment applications that most of us use on a daily basis, including Netflix and Spotify, specifically in the recommendations of new movies or music that they make us, being also responsible for the predictive capacity in the keyboard of WhatsApp or Gmail. In the smartphone assistants are also excellent examples of machines learning from the data that, with the daily use that we provide them, improve what they show us when we consult them. And while these simple uses may seem logical or overly simple, they are the result of advancements that until very recently were impossible for even the world’s most powerful computers.
Looking to the future, machine learning is shaping up to be especially useful in the business world, since this ability to adapt in real time to the data that enters a system can improve an already established model by discovering new, unconventional ways of working. Several banks use it, for example, to predict changes in markets and customers, balancing supply and demand to offer personalized prices to investors. Innovation in scientific disciplines also uses this aspect of artificial intelligence capable of learning and making discoveries, but with uses as disparate as the design of antennas for NASA or the creation of algorithms that allow the structure of new artificial proteins to be predicted.
Already combined with other innovations, machine learning converges in inventions such as autonomous cars where the learning of Artificial Intelligence merges with the power of 5G networks, allowing driving programs to improve their driving progressively, analyzing data in real time at those who can access through the new generation cellular network.
“The possibilities of machine learning are virtually infinite as long as there is data available to learn its possibilities even towards creative fields that, traditionally, can only be carried out with the direct influence of a human being.
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