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Artificial Intelligence: Types of Artificial Intelligence

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Artificial Intelligence – Artificial Intelligence is a vast field, and there are various types of AI systems that can be classified based on their capabilities and functionalities. This article explores the different types of Artificial Intelligence, including how these types of AI are used in different applications, from machine learning and natural language processing to robotics and decision-making systems.

Also, let’s explore History of AI

Table of Contents

Definition of Artificial Intelligence

Since AI has become the main power of technology, AI has launched itself in today’s technology, like a big boom in the technology world. According to various activities and experiments, AI has not been limited to a single definition; it has over a million meanings – 

Artificial Intelligence is the method to teach the machine to be like a human and perform tasks as the human does. Some AI tasks include working like a human, thinking like a human, reacting like a human, and making a decision depending on various situations.

Artificial Intelligence is how a machine is taught how a human behaves in different situations. Depending on the device’s input, AI’s ability makes a machine work like a human. In Learning, AI tries to make a machine understand human action based on experience.

In simple words, 

Artificial Intelligence is training a machine to perform real-life human tasks, such as identifying a signal light, recognizing a voice command, translating a text into a language, and text-to-speech. AI is on a mission to prepare the ready-to-machine to suppress human Intelligence and be a supermodel in the AI world. 

Types of Artificial Intelligence

Weak or Narrow AI

Artificial Narrow Intelligence (ANI) holds limited functionality and cannot learn beyond the specified field. It works on a single task at a time and can’t be served with multiple tasks simultaneously. It has been provided with specialization tasks. 

In other words, ANI is called “weak” Intelligence since it focuses on a single activity that  excels, such as playing chess against human experts, making sales predictions, and autonomously driving cars. It may also include speech and picture recognition at this time. The term “weak” is used in the sense that it is restricted to a single duty rather than having a broader meaning.

Example: Recognizing the voice command over Siri and Google Assistance and establishing a conversation between a device and a human.

Example: A conversation with a bot or chatbot designed to guide the user.

Example: Recommendation of movies on Netflix, products on Amazon, and detection of the signal barriers while in auto-drive mode.  

General or Strong AI

Artificial Strong Intelligence (ASI) is the next step in AI, focusing on mimicking the human brain. Strong AI has always been the vast force that surpasses the human brain’s potential in creativity, social skills, and wisdom. This type of AI can learn new things and adapt to changing situations, as well as problem-solving abilities.

There are currently no existing examples of Strong AI; however, it is believed that we will soon be able to create machines as intelligent as humans.

Super Intelligence

Artificial Super Intelligence (ASI) aims to be more intelligent than humans. It exceeds human Intelligence and performs tasks better than a human. 

Example: Let’s remind ourselves of a movie scene where we would have seen how rapidly upcoming technology, i.e., AI, is occupying people’s desks and turning to eliminate people from jobs.

4 main types of Artificial Intelligence

The process of learning in AI is categorized under the categories “narrow,” “general,” and “super.” These categories illustrate the capabilities of AI as it grows, performing specific tasks, displaying the same capability to think as humans (general), and performing above human capabilities. Four major kinds of AI are defined in the work of Arend Hintze, a researcher who is also a professor in integrative biology at Michigan State University.

These are:

Reactive Machines

The reactive machine includes AI systems that do not have memory and are task-specific. This means that every input will produce the same results. Machine learning models are typically reactive because they collect customers’ data, such as the history of a search or purchase, and apply it to offer suggestions to the same customers.

This kind of AI is active. It is a “super” AI because the average human wouldn’t be capable of processing a user’s entire Netflix history or providing feedback that is customized to recommendations. The Reactive AI system, for the majority of the time, is trustworthy and is a great choice for technology like self-driving cars. It can’t predict the future without having been given the right data.

Please look at our lives as humans, in which most of our actions aren’t reactionary because we do not have all the facts we need to act upon; however, we can learn and remember. Based on the lessons learned from these experiences, whether they were successful or not, we might be able to act differently soon when confronted with the same situation.

Beat in the chess game by IBM’s supercomputer: One of the most impressive examples of reacting AI is when Deep Blue, IBM’s chess-playing AI system, defeated Garry Kasparov in the late 1990s. Deep Blue could identify its own and its opponent’s pieces on the chessboard to predict the next move; however, it lacks the memory capacity to use past mistakes to guide future decision-making. Deep Blue only predicts the next move for both players and then chooses the most effective option.

Netflix suggestions: Netflix’s recommendation engine is powered by machine learning algorithms that process data from the customer’s viewing history to identify specific films and TV shows they’ll love. If you watch many Korean shows, Netflix can preview upcoming releases on the homepage.

Limited Memory

The next stage of AI in its development is limited memory. The algorithm mimics how our brain’s neurons function, which means it becomes better at its job as it gathers more information to improve its training. Deep Learning enhances image recognition, as well as other forms of reinforcement learning.

AI with limited memory, unlike reacting machines, can explore the past and track particular objects or events in time. This observational data is programmed into AI so that it can be performed based on both current and historical information. In the event of a small memory, the data doesn’t get stored in the AI’s memory for experience to learn from, as is like humans can draw meaning from their successes and failings. The AI gets better as it learns with more information.

Self-driving vehicles: A good example of limited memory AI is how autonomous cars monitor other vehicles on the road to determine the speed they travel, their direction, and even proximity. The information is then programmed into an image of what the vehicle sees around it, like being aware of traffic signals, light curves, bumps, and curves on the road. The information helps the car to decide when to switch lanes so that it is not injured or cut off by the other driver.

Theory of mind

The first two kinds of AI, which are reactive machines as well as limited memory, are the two types that are currently in existence. Theory of Mind and Self-Awareness is AI kinds expected to develop shortly. Therefore currently, there aren’t any real-life examples of these at present.

If it’s created, theory of mind AI might be able to understand the world around us and how thoughts and emotions influence other creatures. This, in turn, impacts their behavior concerning other people around them.

Humans know how our thoughts and feelings affect other people and how the thoughts and emotions of others affect us. This is the foundation of human relations in our society. Soon, theories of mind AI systems could discern people’s intentions and predict their behavior in a way that mimics human interactions.


The ultimate goal for the development of AI could be to develop AI systems with a sense of self and a clear awareness that they exist. This kind of AI currently exists.

This is a leap beyond the concept of mind AI and knowing emotions to be conscious of their current state of mind and the ability to discern or predict the feelings of others. For instance, “I’m hungry” becomes “I know I am hungry” or “I want to eat lasagna because it’s my favorite food.”

We’re still not self-aware AI as there’s still much to learn concerning the human brain’s capabilities and how memory, Learning, and decision-making can work.


In conclusion, this article has provided a comprehensive overview of the various types of Artificial Intelligence, highlighting their capabilities, applications, strengths, and limitations. From rule-based systems and expert systems to neural networks and genetic algorithms, there are different types of AI that can be used in various applications, including machine learning, natural language processing, robotics, and decision-making systems. Understanding the different types of AI can help businesses and developers determine which systems are best suited for their needs and leverage their potential to transform industries.

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