What is Machine Learning (machine learning)?
The name already gives a lot, doesn’t it? This is the ability of a man-made device to analyze data to automate the creation of analytical models.
It is, therefore, an aspect of artificial intelligence , a broader concept that concerns the ability of a machine to make decisions based on reasoning that resembles human thinking.
In the case of Machine Learning, these decisions made by the equipment are expected to be based on learning with data and identifying patterns with minimal (or no) human intervention .
Machine Learning was born from the idea that machines could learn to perform specific tasks without being programmed to do so.
The great goal of a developer of this aspect of AI is to create software that, when exposed to new data, can adapt independently.
These data, added to previous calculations and sometimes subjected to repetition, produce reliable decisions and results.
Although some people see artificial intelligence and machine learning as trends that bring robots closer to what is most human and subjective in us, the basis of everything is still the Exact Sciences .
What allows a machine to have anything like intelligence are algorithms, and we’ll talk more about them later.
The evolution of machine learning
Although we are talking about a relatively new concept, the origin of Machine Learning is almost 70 years old .
In the 1950s, when the first computer models were being developed, Alan Turing, considered the forerunner of informatics, began to carry out the first tests to analyze the reasoning power of machines .
It was nothing very elaborate, but for the time, watching computers repeating sequences of commands was already impressive.
A little later, using Turing’s discoveries as a reference, an American computer scientist named Arthur Lee Samuel went further and created the first software capable of learning .
The experiment consisted of a virtual checkers game, in which the system improved performance as the games passed.
That is, the machine specialized, learning moves and creating strategies from its history, and it became increasingly difficult for someone to overcome it in a match.
Samuel was also responsible for using the term Machine Learning for the first time, in 1959 .
Since then, technology has evolved a lot, and so has the amount and complexity of information.
As a result, new concepts and tools are also introduced, such as Artificial Intelligence , Big Data , the Internet of Things , among others.
It is difficult to predict what these new machines will be able to do in the future, but it is undeniable that they will evolve a lot and become increasingly integrated into our routines.
How does Machine Learning work?
There are approaches to artificial intelligence that study brain structures, that is, the working model of neurons, to create intelligent machines.
But it is not expected, at least not in the next few decades, that the same result can be obtained with machines as that originated by millions of years of natural selection.
In AI, Machine Learning and all of computer science, algorithms are the foundation of everything .
They are sequences of rules and operations that, when applied to a set of data, give rise to a certain result.
In order for machines to learn, the algorithms are subjected to certain methods, which are divided into two approaches.
The first is supervised , in which the algorithm learns because it receives data that contains the correct answer.
In the unsupervised approach , on the other hand, the data the algorithm receives is not labeled, so the effects of variables are unpredictable.
This second approach, therefore, is more complex and advanced, because in it the machine itself finds the desired patterns and improves its filters according to use.
What is Machine Learning for?
This is the era of automation and information technology in the service sector and in the production of consumer goods.
In fact, automation itself has been around for quite some time.
What is new is the use of artificial intelligence, Big Data, the Internet of Things, and, of course, Machine Learning.
This new reality of automation has allowed a leap in productivity worldwide.
With Machine Learning, factories are smarter, and machines are improving on their own.
In the service sector, robots replace humans in customer service.
And we’re not talking about the mazes in the SAC of telephone companies, but algorithms that learn over time and increasingly meet the information needs of customers.
When we use the word “robots”, we are not talking about androids, those with human form, or even more “square” robots.
Most of the time, they are just programs, sets of codes built to handle a certain function.
Whatever the area, it is important to keep in mind that the goal is not just to make entrepreneurs earn more money.
The quality of products and services also tends to improve over time because machine learning is much faster than human learning.
How important is Machine Learning?
Human beings do their best when performing manual or intellectual tasks.
Within our possibilities, we do what we can.
What happens is that today’s world is so computerized that today, in a single day, more information is produced than in entire past centuries.
Here’s Big Data – there’s so much that it’s humanly impossible to harness all that data, which is why we turn to machines.
Those who use the Waze application, for example, are providing data on the speed of travel on the road where they are.
Based on this information and the data collected from users who travel along the same road, the application will or may not recommend that other drivers reach their destination by the same route.
A huge team of human beings would not be able to collect this data and turn it into real-time route suggestions.
The app’s algorithm, which works 24 hours a day, succeeds.
Even in this way, with the creation of algorithms designed to take advantage of this immense amount of data, there is still a lot of wasted information.
That’s what Machine Learning is for: so that machines can learn on their own to process this data and put it to use.
Thus, man-made software and equipment will be able to analyze increasingly complex and numerous data, automatically and quickly.
The result will be, as we highlighted before, results that are equally more accurate and faster, even on a large scale and with much lower risk.