Machine learning is a concept used to characterize the technology that allows systems and machines to identify patterns and habits in order to acquire new knowledge. This is done by patterns, which are previously defined by algorithms.
This innovation is increasingly present in the management routine and, although it may go unnoticed, it makes a total difference in its tasks and results. This was developed with the purpose of allowing software to carry out activities without the help of a human. For example, chatbots, which are automatic customer service systems.
However, this technology is often confused with artificial intelligence (AI), which is a great misconception. Learn more about this below.
The father of artificial intelligence, the engineer Arthur Samuel, was the one who coined the term in 1959. Machine learning emerged due to the need to handle and store data in an efficient and optimized way, since a lot of data was being collected on the internet.
In this scenario, we get a concept that is already part of large companies: the big data. This technology is used jointly, as it allows the storage of thousands of information using algorithms that receive and carry out the data management.
Although related in the same system, machine learning and artificial intelligence are two different technologies. AI is conceptualized by machines that are capable of performing tasks that, until now, were carried out only by human beings. For example, credit analyses and approvals of the financial sector.
Meanwhile, machine learning is what makes it all work, that is, it is the engine of AI. Therefore, it is a technology that is characterized by being a database in order to allow the artificial intelligence to carry out human tasks.
For machines and systems to have this ability to learn, algorithms, statistical methods and big data are used in order to allow operation patterns to be identified, making it possible for machine learning to learn them through the creation of connections.
This happens when algorithms use statistics to learn the most common processes in order to predict responses and attitudes. That way, the machine is able to carry out tasks that follow these patterns, always in an attempt to make the least possible mistakes.
For this to be done, the algorithms are separated into supervised and unsupervised. The first category, as its name suggests, is the means that still needs to be supervised and controlled by people, such as the input and output of data and information that a system needs in order to learn something.
The second category is the one that does not need human intermediation. The algorithms can already learn and work alone in the processing of activities and information. Meanwhile, the big data, as we mentioned, is responsible for storing and processing all this vastness of information.
The applications of machine learning have been so common and normal that we hardly stop to realize where this technology is used. The following are some examples of how this learning of machines makes your routine easier.
It is easy to identify a translation that was made automatically from those made by humans, because it is never 100% faithful. This is because it is necessary to consider the context of each sentence. However, with machine learning, this process is done more precisely, as this technology is able to identify patterns and learn.
Do you know when you are surfing the internet, especially on social media, and you always see ads for products and companies related to your preference? This is machine learning identifying your patterns.
Another example of this scenario is the streaming platforms, those we use to watch movies, videos, TV shows, and listen to music. In them, machine learning can identify and learn your preferences and start to recommend them to you.
Companies need to have a very secure process for receiving and handling information that is confidential and valuable to organizations. The financial sector, i.e. banks, financial companies and investment firms, for example, need to be even more cautious about this.
In this context, machine learning is used to detect inappropriate transactions or those that don’t follow the pattern because they are fraudulent.
Machine learning is already used by companies that need to analyze and interpret important and complex documents, and machines can do this at an extremely efficient speed compared to a human. This avoids rework and errors that are not identified by your team, especially when it comes to inaccurate data and contractual information.
Carrying out a cost reduction without influencing process efficiency is a constant challenge for companies and managers. In this search, machine learning plays an important and strategic role. The first example is in the reduction of the labor required to perform repetitive tasks that can be done by this technology.
Another form of this reduction is in the spending of electricity. We know that energy companies represent a large part of the costs. Because of this, there are already organizations that use this technology to learn and identify the consumption patterns of a team in order for the machine learning to monitor power systems, but without affecting the quality of services or the productivity of the company.
Machine learning is a technology that is capable not only of learning tasks that your team performs, but also ignoring certain limitations that the human mind and body have. That way, you will have more reliable results in your activities and also reduce significant costs.
To learn even more about this subject, keep reading our blog and check the article on how to ensure a good data analysis by using management software.
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