Speaking of artificial intelligence and everything related to it seems too futuristic, but the truth is that the term was used for the first time just over 60 years ago, more precisely in 1956. However, the concept was not applied exactly in the way we know it today.
The technological development of the following decades was what encouraged not only a deeper understanding of the topic but also its use in a more comprehensive way. Currently, artificial intelligence is at our fingertips, literally.
But, after all, what is AI all about? In a very summarized and simplified way, it is a field of study that studies the ability of machines to perform tasks, which when performed by human beings depend on cognitive intelligence.
The truth is that artificial intelligence has paved the way for different technologies that are often confused with it. An example of this is machine learning.
Continue reading this article to understand the differences between the two concepts and their applications.
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What is machine learning?
The foundations of machine learning, also known as machine learning, were laid practically together with artificial intelligence, but it was in the 1980s that the concept became widely disseminated.
As with AI, there are a number of possible definitions for machine learning. Simply put, machine learning is the ability of software to modify its own behavior or responses automatically as it “learns” from interactions.
The systems undergo initial training using a significant database and, from there, identify consumer standards. It is this first information that will serve as a rule for them to make decisions that are more appropriate to the context to which they were exposed.
One of the main advantages of machine learning is that this entire process takes place practically without human intervention. In other words, after training, the software is capable of operating autonomously and efficiently.
The Python programming language, in machine learning, is one of the most used because it has complete platforms, several modules and libraries to choose from.
There are different machine learning methods, but one of the best known and most used are artificial neural networks. They were created to simulate the organization of the human brain, where the “nodes” of each network are like neurons.
The global machine learning market is expected to reach $79 billion by 2024, growing at a compound annual growth rate of approximately 36–42%, and is projected to surpass $500 billion by 2030, according to sources such as DemandSage.
This demonstrates the technology's scalability in sectors such as finance, manufacturing, and healthcare.
Used for more complex analyzes with a greater volume of data, neural networks have several layers and allow the decoding of information into references that can be used. This is basically the structure of deep learning, a branch of machine learning.
It may seem difficult to understand, but in practice, we come into contact with machine learning regularly. A clear example of this is when we receive recommendations of what to buy on Amazon based on previous purchases or products we have viewed.
Differences between machine learning and artificial intelligence
But, after this explanation, a question may arise that is quite common: wouldn't machine learning be exactly the same thing as artificial intelligence? Actually, yes and no. Yes, because one thing is part of the other and not because AI represents an entire field of study, a broader concept than machine learning.
Machine learning is, therefore, one of the facets of artificial intelligence. It is its practical application as we know it until now. AI refers to the ability of machines to perform any task, from the simplest to the most complex, in a similar way to human beings.. To do this, they consult a pre-configured database and repeat patterns.
Machine learning, in turn, has to do with the ability to learn, in a simulation of the human brain.
In general, the two concepts end up being related, with the application of machine learning directly depending on the use of artificial intelligence.
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The benefits of machine learning for businesses
The use of machine learning has experienced a boost, especially in the last decade. The technology began to be adopted on a large scale by well-known companies such as Google, Amazon, which we mentioned previously, and Spotify. However, even small businesses have discovered its viability and main advantages.
According to a study carried out by Gartner, the adoption of artificial intelligence practices, with emphasis on machine learning, by companies led to their growth of 4% to 14% in the period of one year, between 2018 and 2019.
According to a recent McKinsey survey, by 2024, AI adoption by organizations jumped to approximately 72%, up from 50% in previous years, with effective use across a variety of functions.
Furthermore, research from DemandSage estimates that 80% of companies reported increased revenue from the use of machine learning.
It is important to remember, however, that machine learning will only be truly strategic when led by a specialized team. Contrary to what many think, artificial intelligence did not come to replace professionals, but to optimize dynamics and results.
Knowing this, let's see the benefits of using machine learning by companies, specifically those that have activities based on mathematical and statistical algorithms.
According to the TIC Empresas 2024 Survey, conducted by Cetic.br, approximately 13% of Brazilian companies reported using AI between March and November 2024 — a figure that remains stable compared to 2023.
Adoption is concentrated in large companies (38%), while medium and small companies represent only 29% and 10%, respectively.
Furthermore, 76% purchase off-the-shelf solutions, only 25% develop them internally, and only 12% maintain partnerships with universities or research centers.
Carrying out predictive analysis
Many decisions made by businesses could be different if they were able to predict the behavior of the target audience. With machine learning, this is possible. Based on previous behaviors, the system is able to analyze what the customer's next decisions will be.
Speaking specifically about educational institutions, the software is based on a broad database and can identify actions by students and candidates. It makes a comparison, for example, between the profiles of students who actually enrolled and those who left the institution.
Based on this information, the software is able to identify which leads have the potential for conversion, as well as which already enrolled students are at risk of dropping out.
This is precious data for schools and colleges, as they can carry out specific campaigns for each of these groups, promoting enrollment and retention. Instead of more general marketing strategies, they directed actions with more strategy and effectiveness.
Improved brand communication
Using the data generated to better understand who its audience is and what they want, the company can optimize the way it communicates. Just like in predictive analysis, instead of “shooting everywhere”, it will focus its strategies on the group that is looking for exactly what it has to offer.
This is where a marketing plan designed to attract leads comes in, improving productivity and promoting a much more effective use of resources.
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Image: Visual applications of artificial intelligence and machine learning in a digital environment, highlighting the flow of data in real time.
Reduction of operational costs
From the moment a company can resort to automation, it naturally eliminates some expenses and is still able to guarantee improvements in the service provided. A classic case is the use of chatbots, which often replace or reduce the need for a team to provide telephone support.
These virtual assistants, properly programmed, can offer faster support than a call center. Chatbots are available 24 hours a day, reduce waiting times, respond quickly and generally have a positive impact on the customer experience.
Educational establishments can schedule their assistants to carry out registrations, enrollment and reception of documents, thus streamlining administrative processes. Even if it is necessary to resort to human assistance, the software carries out the routing and the operator has access to all previous conversations.
The functioning of chatbots is directly linked to deep learning. Do you want to know more about this technology that is also behind facial and voice recognition?
Common questions about machine learning and artificial intelligence
What is the difference between machine learning and artificial intelligence?
Machine learning is one of the practical applications of artificial intelligence. While AI refers to the ability of machines to perform human-like cognitive tasks, machine learning focuses on enabling these machines to learn from data and adjust their behavior based on identified patterns.
How can machine learning help educational institutions?
Machine learning helps institutions analyze student and lead behavior, detect dropout or enrollment patterns, and personalize marketing and retention strategies more accurately — improving student engagement and institutional performance.
Does machine learning replace professionals?
No. Machine learning serves as a support tool that optimizes processes, allowing professionals to focus on more complex strategies. It automates repetitive tasks but still requires specialists for setup, analysis, and decision-making.
What are the benefits of machine learning for companies?
The main benefits include: predictive analytics, more efficient audience segmentation, reduced operational costs, process automation, and improved communication with customers. All of this contributes to increased revenue and loyalty.
Where do we encounter machine learning in everyday life?
Machine learning is present in product recommendations on Amazon, music suggestions on Spotify, personalized ads on social media, smart chatbots, and facial or voice recognition systems — all based on behavior patterns.
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