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What are the technologies behind artificial intelligence?

Artificial intelligence is an overarching concept, not a single technology. The setting, its use, its purpose; everything varies to create completely different iterations from the same base.

To better understand what AI is, we also need to familiarize ourselves with specific technologies under this umbrella. Discover the most used methods to create intelligent algorithms.

Machine Learning

Machine Learning, or machine learning, is the concept of processing, analyzing and adapting an algorithm without human interaction in this adjustment.

The most used ML model today is pattern reinforcement. The machine receives basic indications of purpose (result it must achieve, rules to reach that result, exceptions, etc.) and a large source of data to achieve its objective on its own, aiming efficiency and optimization.

From there, the algorithm starts to group and segment the large volume of information in search of patterns that are compatible with what it needs. Every unwanted interaction becomes negative reinforcement. Each desired interaction becomes positive reinforcement.

Thus, over time and millions of calculations, AI begins to reinforce what works in pursuit of a specific goal. The more experienced you are in analyzing that data package, the more accurate and customized the answer to related questions.

With this, we can say that Machine Learning is the most basic iteration of a simulation of human creativity and learning. After all, he can use past experiences to compare ideas and generate new proposals from this interaction.

Deep Learning

Deep Learning, or deep machine learning, is a recent evolution of Machine Learning. It is, at the same time, a continuation and a subcategory. The difference between this model and the previous one is the complexity of the information and the paths used to reach analysis results.

Therefore, Deep Learning can use many more layers of interaction between data, something that not even humans are capable of doing as efficiently and quickly as an algorithm. In this way, DL processing becomes non-linear and cascaded, exponentially increasing the possibilities of interpretation and suggestion of paths.

It is an area with a lot of investment from the industrial sector, mainly due to its ability to project future scenarios. Think of Deep Blue, which designed hundreds of millions of possible moves to win at chess. This is nothing compared to the current possibility of technology.

Neural Network

Even with an absurd processing complexity, Deep Learning is not the only AI possibility for the future. On the contrary, the Neural Network is a technology that promises to be even more efficient and powerful for data analysis.

As the name suggests, a Neural Network takes inspiration from the structure of our brain’s neurons for intelligence that is actually similar to how our minds work.

Instead of a linear structure like ML, or a cascade structure like DL, RN uses a cloud of connecting processes from a data source. This opens up exponentially greater possibilities for interaction between information for even more complex responses in the future.

It’s a fascinating and frightening possibility at the same time: an algorithm that works like the human brain, but without our biological limitations. It is very likely that this technology will be the future of computing, including architectural theories for quantum computers.

How important is artificial intelligence, risks and precautions for the future?

You realized that it is impossible to talk about artificial intelligence without talking about the future. The exponential development nature of autonomous algorithms should very quickly lead us to a world in which they are present in different aspects of life and business.

However, this transformation also leads us to necessary discussions about the risks of artificial intelligence and the precautions we need to take for the future.

The main one is the concept known as black box, or black box. Deep Learning and Neural Networks are making data processing so complex that we humans can no longer follow the path that AI takes between receiving a question and providing an answer.

This technology blind spot leads to several questions about ethics and data security. It is necessary to find ways to take advantage of the processing power of these tools without exposing people’s privacy unnecessarily.

What we can do now, especially in the business world, is stay informed about the advancement of technology and know where to find a balance. That is, identifying artificial intelligence that is increasingly advantageous on the market, but that does not become a destructive factor for society as a whole in the future.

What are the main examples?

Several examples of artificial intelligence are already present in our routines. Some we actively seek, others we don’t even notice. See the most notable of them.

Recommendation algorithms

How does your Netflix account always recommend the series you’re most interested in, even though you don’t even know them? Or how YouTube videos are presented in a way that you spend a lot of time watching without even realizing it?

Behind these recommendations are specialized AI algorithms. These are codes that analyze your viewing history and suggest content according to your profile, your interests and based on how other people similar to you have behaved in the past.

Recommendation algorithms are now in subscription services, social networks and online stores, influencing your consumption and entertainment decisions.


Chatbots are conversational software used by companies to carry out simple interactions with customers and resolve less complex issues. You may have already needed or even used one.

They serve as a triage system for queries, queries and suggestions.


Speaking of AIs that became famous even outside of their primary uses. Did you know that the ChatGPTIs it, at its core, a chatbot? The algorithm became popular from 2022 onwards for offering conversational interaction with users based on a very efficient content generation model.

It is an iteration of artificial intelligence that demonstrates the technology’s productive support potential. By knowing how to use the tool. Professionals can create content structures in a short time, accelerating stages of the creative process.

Midjourney and the like

Other very famous generative AIs are those for creating images, such as Midjourney. These tools use a huge visual database to create photos and illustrations based on basic user commands.

Like ChatGPT, they are in the middle of important debates about originality and protecting rights. However, they are very useful when used in the right way.



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