1950 was the year in which the famous mathematician Alan Turing first raised the possibility that machines could think, opening the way to modern Artificial Intelligence. A decade later, progress was made 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 in machine learning. 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 emergence, the limited technology of the time also caused it to enter a period of inactivity that would last until the late 1990s, when the IBM Deep Blue system defeated the multi-chess world champion, Garry Kasparov, in a game of chess. Against this backdrop, at first glance, it may seem that machine learning is an aspect of cutting-edge technology that is removed from the everyday 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 good for the common user? What potential does it have even for the most specialized uses?
The mind of a computer.
Since 1997, when IBM's famous chess machine achieved its representative triumph, a line of research continues to the present day, developing artificial intelligence that can learn to play strategy games, managing to defeat their greatest champions in flesh and blood. However, these are experiments designed to test and augment their analytical capabilities in the laboratory, the question is where is machine learning applied?
It is present in virtually all entertainment applications that most of us use daily, including Netflix and Spotify, specifically in the recommendations of new movies or music that they make to us, being also responsible for the predictive capabilities in the WhatsApp or Gmail keyboard. In smartphones, assistants are also excellent examples of machines learning from data that, with the daily use we provide them perfect what they show us when we consult them. And although these simple uses may seem logical or too simple, they are the result of advances that until very recently were impossible even for the most powerful computers in the world.
In the future, machine learning is shaping up to be especially useful in the business world, as this ability to adapt in real-time to the data entering a system can improve an already established model by discovering new and unconventional ways of working. Several banks use it, for example, to predict changes in markets and customers, balancing supply and demand to offer customized 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 AI learning merges with the power of 5G networks, allowing driving programs to progressively improve their driving by analyzing real-time data they can access through the next-generation cellular network.
Enter aciesdecision.com to learn more about innovations in Artificial Intelligence and other exponential technologies directly from our experts.
Comments