Have you ever stopped to think about how streaming music services, podcasts and series know your preferences so well, to the point of combining indications in a playlist that have everything to do with content you are consuming at a certain point in your life? This is one of the results of machine learning applied in practice.
What is machine learning?
Machine learning is a field of application of Artificial Intelligence (AI) that provides computers with the ability to learn, improving their performance and adapting to different situations, without the constant need for a programmer to input commands to the system. This machine learning makes technologies more useful, versatile, and closer to your life and business.
For decades, computer science has contributed to the development and evolution of technological applications capable of learning from experience. Gathered as a segment of Artificial Intelligence (AI), these tools are becoming more and more present in platforms and systems. Important machine learning applications can be easily found in Industry 4.0, which also generally involve the use of Big Data techniques, that is, the manipulation of large volumes of data coming from different sources and with different formats.
The ability to make machines learn, with the aim of improving themselves in an automated way, allows, for example, applications to capture and understand the environment in which they are applied, as well as the preferences of their users, and thus be able to provide better experiences as you go. Machine learning, after all, is much more than opening Netflix and finding a selection of directions than watching. Check out some areas where this technology is already being used to generate results in industry and business.
Machine learning and IT security
With the volume of data generated daily, one of the focuses of developers and technology security experts is to keep information secure. In this way, the systems are conditioned to find and prevent fraud and failures that could cause damage to companies and individuals.
An example of the use of machine learning in IT security is the behavior analysis of credit card customers. Thousands of cardholders have already been spared from improper purchases when credit operator systems realized that a different consumption pattern was being applied, which can happen when personal information is cloned.
Learn more: The Amazing Ways How Mastercard Uses Artificial Intelligence To Stop Fraud And Reduce False Declines
Anticipation in industrial maintenance
Using machine learning in industry is one of the ways to anticipate problems and provide predictive maintenance for equipment. Through the combination of data analytics and learning algorithms, companies that use Big Data strategically can use information generated by equipment to predict when machines will fail and wear out.
This is one of the factors that make Windbox customers use Athenas Plant in their industries. Through Big Data, the dashboards of this tool present information that shows the manager the anticipation of a problem.
Production and demand adjustment
The use of machine learning is linked to industrial production in terms of adjusting production according to demand. When a certain product is leaving stock at a rate considered non-standard, the system can indicate to the production sector that something is different. With this, contact must be made between the production team and the sales team to understand the change in pattern and adjust the demand in relation to the search for a certain product.
This decision-making process, based on machine learning, makes the process faster and more efficient, bringing better results for the entire production and distribution chain.
Information and technology make machine learning happen
These are some examples of the application that machine learning can generate for business, but what can be achieved with machine learning goes further. Through the crossing of data, and the way this information is received by the system, different applications can be given to computers.
There are three main categories of machine learning that reflect how computers are taught:
Supervised learning
Consider the problem of differentiating four- or two-wheeled vehicles based on images. In supervised learning, the machine learning system will be exposed to thousands of examples showing the difference between cars and motorcycles based on how many wheels each has. Each example will be previously identified as being from a vehicle with four or two wheels, and the system will then learn what this difference consists of. In this process, known as training, it is as if a teacher were present to teach the machine and tell it when it hits or misses.
The next step is to use the machine to indicate which vehicles are from one group or another. The detail is that the vehicle images to be presented have never been seen by the application, and since it has learned, it will be able to respond correctly anyway!
This type of machine learning is one of the best known and most applied, allowing to solve different tasks in terms of difficulty and domain, such as classifying products and services based on previously known characteristics, predicting when a certain component of a wind turbine is about to fail and many others.
Unsupervised learning
Unsupervised learning does not consider a database with previously classified examples, that is, the figure of a teacher who knows the right answers during training does not exist. In this case, the computer arrives at the results, for example, based on similarity metrics calculated from the data and grouping techniques.
In this case, the result of the applied technique can be a more generic classification of the entities of interest, such as separating customers of a dealership among buyers of red cars from those who prefer white ones.
Reinforcement learning
Another way to pursue machine learning is to develop a mechanism to teach the system to prioritize one action or another in each situation. In this format, the computer learns that something is more important, and that from the right choice it will be rewarded.
This is the situation applied to car systems that do not need a driver, for example. The system will know which delays can occur for different reasons: being involved in a possible accident or being stopped by an external cause. In this case, being stopped by something that was not provoked by him will not incur a penalty, so he will do his best to receive a reward.
Machine learning is the future in action
Machine learning applications are numerous and with gigantic growth potential. Therefore, technology companies invest in training, experimentation, and application of knowledge in this area in the development of new tools and possibilities within existing systems.
If you are interested in the subject and want to know more, as an IT professional, or a manager who wants to understand how to bring Artificial Intelligence to your business, talk to our team of consultants! We have experts ready to talk more about technology and how our solutions can be part of your company’s growth.
