The world of Artificial Intelligence is very complex, especially for those just starting out. One of the most common questions among beginners arises when they read or hear the names of different types of Artificial Intelligence. How do they differ from one another? How are there different types of AI?
Although the term “Artificial Intelligence” is generally used to refer to the ability of machines to perform human tasks, this type of technology can branch into different types depending on its learning capacity, training method, level of autonomy, or its capacity for human interaction, among other factors.
In this article, we want you to learn about some of the most well-known types of Artificial Intelligence and learn to differentiate between them so you can use them correctly. This will also help you better understand emerging AI news and help you choose the most appropriate AI tools for your needs.
Therefore, we’re going to define the 7 most popular types of Artificial Intelligence. And to help you better understand them, we’ll also explain in each section how to use each AI applied to marketing, so you know when to implement each one in your marketing strategies.
The 7 most important types of Artificial Intelligence
Predictive Artificial Intelligence
Predictive AI is a branch of artificial intelligence that uses statistical techniques and machine learning algorithms to analyze historical data and predict future events or situations with a high degree of accuracy.
In the digital marketing sector, this type of Artificial Intelligence is often used to predict purchasing behaviors and market trends, so it’s also used in customer segmentation. In this sense, it’s one of the most common applications of AI in marketing.
One of the main benefits of AI is that it helps businesses make much more informed and strategic decisions, as well as help them stay ahead of the competition. It’s also tremendously powerful in identifying potential threats and developing a more proactive approach to avoiding them.
Generative Artificial Intelligence
Generative AI is the branch of this type of technology that specializes in creating original and new content from the content it has been trained on. This type of AI can generate content in one specific format or several (depending on the model) and also process requests in one or several formats. In this sense, today we find generative AI capable of processing and generating content in both written and visual formats, as well as video and audio formats.
Another important aspect is that generative AI is capable of learning from its past interactions and improving its performance based on them.
Generative AI has a multitude of applications. In the marketing field, it’s widely used for writing content or generating visual content, as well as inspiration for developing marketing strategies or plans. It’s also very common in the customer service sector, as it’s the technology behind the most powerful chatbots on the market, which can answer frequently asked user questions and offer them a better experience.
The main advantage of this AI in marketing is that it allows teams to increase their efficiency and productivity by serving as an assistant and support for different tasks.
Reactive AI
Reactive AI is one of the most basic types of Artificial Intelligence. It is trained to respond to specific inputs and perform predefined actions. In that sense, it doesn’t have the ability to learn from past interactions (as generative AI does, for example).
It’s very common to see this technology in many programs behind strategy games. For example, Deep Blue, the computer developed by IBM to play chess and which defeated Garry Kasparov in 1997, ran on this type of Artificial Intelligence. In this case, Deep Blue “only” evaluated the current positions on the board and calculated the optimal move, but it didn’t learn from its mistakes or past experiences.
In the field of digital marketing, this type of AI is used in more basic chatbots that respond with pre-programmed responses and can only act on predefined scenarios. Therefore, they cannot adapt to situations not considered during programming. It is also common to see reactive AI in personalized product recommendation tools for websites.
In this case, despite being more basic, this AI can also serve to free up work for certain professionals so they can dedicate more of their time to more strategic or creative tasks.
Limited Memory AI
Limited-memory AI is a type of AI that has reactive capabilities, but can also learn and store information from past experiences (like generative AI), albeit temporarily. That is, it can improve its performance by learning from historical information, but not permanently.
We could say that, in terms of memory, this type of AI is an advance on reactive AI (which lacks the ability to learn from past events), but it’s at a lower level than generative AI. The latter does retain knowledge acquired from past experiences over the long term.
That’s why limited-memory AI is most commonly used in applications that require immediate decisions, such as autonomous vehicles (which make decisions based on real-time road conditions or vehicle speed) or virtual assistants like Siri or Alexa.
Symbolic or rule-based AI
Symbolic AI is based on predefined logical rules and explicit knowledge introduced by humans. Like reactive AI, rule-based AI doesn’t learn from data; rather, it applies rules to make decisions or even predictions.
Unlike predictive AI or generative AI that follow complex algorithms, symbolic AI follows simple and clear rules that allow you to understand how a conclusion or prediction is reached, making them much more transparent.
However, this type of AI cannot adapt to new data or unexpected factors (something that machine learning-trained AI can do). Therefore, rules must be manually updated when new factors or scenarios change.
At this point, you may not fully understand the difference between symbolic AI and reactive AI. The distinction between the two lies in their learning capacity and knowledge representation. While symbolic AI can adapt if we manually incorporate new rules and knowledge into it, reactive AI lacks this capability and operates solely based on the data at hand to respond to stimuli.
Autonomous AI
Autonomous AI is the term used to describe artificial intelligence capable of performing real-world tasks and making decisions independently without human intervention.
It is the AI that enables intelligent robots, for example, to function, offering advantages such as error reduction, freeing up tasks for teams, and increasing productivity.
Some examples of autonomous Artificial Intelligence applied to marketing are AI tools designed to automatically optimize advertising campaigns in real time to maximize their performance.
Autonomous AI represents a significant advance among all types of Artificial Intelligence, as it allows machines to act independently.
Hybrid AI
Hybrid AI, rather than being included in the list of different types of Artificial Intelligence, could be considered a form of AI use that consists of combining the use of machines equipped with this technology with human intelligence.
It can even be considered a philosophy based on the principle of using AI, but always under human supervision. This symbiosis between machine and human would ensure coexistence between the two, rejecting the idea that machines will replace people.
This way, we achieve the best of both worlds: on the one hand, the precision, process optimization, and automation of actions enabled by AI, and on the other, the intuition, human touch, and oversight that we humans provide.
