Technological advances have shown that manufacturing management still has a vast path to explore. An example of this is Machine Learning, which is gaining more and more strength in the industrial segment.
This concept may seem new, but the 1960s were already talking about machine learning. Machine Learning has gained its space in the industry with the popularization of the internet and the development of increasingly intelligent technologies.
This article will explain this concept and how you can use this strategy to optimize industrial processes.
What Is Machine learning?
With the increase in the number of data available on the internet, there was also the need to interpret them. Machine Learning is nothing more than the ability of machines to analyze information without the interference of a programmer.
It is a set of algorithms that improves as it processes many files available for specific purposes. So this technology:
- Accelerates the development of analytical models;
- Learn from them;
- Implements improvements;
- Create new models.
Machine Learning systems can increase productivity in industries and identify bottlenecks, failures, and business opportunities from predictive analysis of records.
What Types Of Machine Learning Are There?
Today, there are at least five types of machine learning. Each of them is related to the level of data processing, the amount of information processed and its ultimate goal.
Combining processing level, quantity, and objectivity makes the machines deliver increasingly accurate results with highly competitive value. Check out how the main types of Machine Learning work:
Deep Learning
This system is directly related to Artificial Intelligence (AI). In this case, the machine learning process focuses on image classification, speech recognition, object detection, and content description.
It is one of the most complex learning processes, as it involves different analyzes and the need for a massive volume of files to process information.
Reinforcement Learning
Widely used for robotic equipment and systems, reinforcement learning works through trial and error. In this case, the algorithm will determine which actions have the best rewards from the hit probabilities.
Supervised Learning
In this case, the algorithm receives ready-made labels of inputs and outputs. He will learn from the essential information and the actual production to compare the results and find faults in the initial modules delivered. Therefore, the algorithm modifies the model according to its results in the learning process.
Semi-Supervised Learning
In the case of semi-supervised learning, both labeled and unmarked data can be used to train the algorithm. Generally, a small number of labeled information and many raw records are used. This system uses methods such as classification and prediction to create new models.
Unsupervised Learning
Here, data that does not contain any history are used to train the algorithm. In this case, the system does not have a correct answer to use as a basis. Its objective is to explore and discover some structure that can be used as a model in the information.
What Is The Difference Between Machine Learning And AI?
Generally speaking, it is a subset of Artificial Intelligence (AI). The difference between the two is basically in the function that each one has.
AI aims to create systems that can simulate human intelligence. It is one of how this artificial intelligence can be achieved.
In addition, learning systems seek to find unseen patterns in data that do not have a labeled history. AI uses machines to solve complex problems, simulating the human mind.
What Are The Advantages Of Using Machine Learning In The Industry?
As we have seen, different types of machine learning can be used in the industry. In addition to being efficient, these systems can solve numerous problems faced by manufacturing, especially those related to productivity.
In addition, it also makes it possible to identify business opportunities with predictive analytics models. These machines can optimize results by using interactivity and continuous learning, increasing market value.
