It is indisputable that today’s world operates with artificial intelligence (AI). Many industries are adapting to new changes and have had to modify their key infrastructures to leverage AI’s remarkable benefits in their fields.
AI is no longer a term people merely think about—it has become a reality and an integral part of our daily lives. AI is an incredibly complex and vast spectrum of studies, much of which remains unexplored.
Today, various AI models are used in the business world, making it essential to understand their different types.

7 Widely Used Types of Artificial Intelligence

1. Weak AI (Narrow AI)

Weak AI, also known as Narrow AI, is a specific type of AI designed to perform a single task. More precisely, it refers to computer systems or programs capable of executing only one limited task, with their capabilities confined to what they have been trained for. This type of AI attempts to perform its designated task intelligently and automatically, but performs poorly in other domains.

For example, chess programs powered by Weak AI can defeat professional players but struggle with tasks requiring real conceptual understanding, such as natural language processing. Facial recognition software also falls under Weak AI, as it is designed solely to identify faces in images. Similarly, self-driving cars are a specific type of Weak AI programmed to operate without human intervention.
Weak AI stands in contrast to Artificial General Intelligence (AGI), which can perform any task a human can. Weak AI is used in various fields, including healthcare, finance, retail, and more, to improve efficiency, reduce costs, and enhance customer service.

While Weak AI has simplified specific tasks, it is essential to note that it is imperfect and may make mistakes. Therefore, users must know its risks and verify its outputs to ensure accuracy.

2. Reactive Machines

Reactive Machines are the most basic AI systems with minimal capabilities. They are designed to respond to environmental stimuli by mimicking the human brain. These machines do not require prior knowledge of their environment and can quickly react to changes.
They are trained using Reinforcement Learning, a process where the machine learns through trial and error, receiving rewards for actions that bring it closer to its goal.

Reactive Machines are used in various applications, including automated control, computer games, and robotics. A simple example is a facial recognition system that detects human faces in images by drawing a bounding box around them. The model does not store data or learn.
Smartphone cameras operate on this principle. IBM’s Deep Blue, which defeated chess grandmaster Garry Kasparov in 1997, is a well-known example of a Reactive Machine.

These AI systems react solely based on current inputs and lack long-term memory for storing or retrieving information. They use simple processing methods and are typically limited to their designated tasks. Reactive Machines operate on fixed rules and algorithms, responding quickly and accurately to inputs, but their performance declines sharply in unfamiliar scenarios.

Industrial robots used in assembly lines are an example of reactive machines. They adjust their actions based on current sensor inputs but lack a comprehensive understanding of the production process, operating only on predefined algorithms.
While still in early development, Reactive Machines hold the potential to bring significant transformations and may increasingly integrate into our daily lives in the future.

3. Artificial Superintelligence (ASI)

Artificial Superintelligence (ASI), or Super AI, represents the pinnacle of AI research, possessing the highest level of intelligence on the planet. Due to its vast memory, rapid data processing, and superior decision-making skills, ASI will surpass human performance in every task while emulating the human brain.

ASI is generally more intelligent than humans and can perform most tasks better. It refers to AI capable of simulating human emotions and experiences so precisely that its behavior is understandable to humans and persuasive. Key features of ASI include independent reasoning, problem-solving, judgment, and decision-making without human intervention.

While ASI remains hypothetical, its real-world implementation would bring profound global changes.

4. Artificial General Intelligence (AGI)

Artificial General Intelligence (AGI) is intelligence capable of performing any intellectual task as accurately as humans. This AI model is functionally related to the Theory of Mind. AGI does not yet exist, but some researchers believe it is only a matter of time, as the necessary infrastructure and knowledge for its development are already available.

AGI could revolutionize major sectors like healthcare, finance, and education. However, experts warn of potential risks, such as mass unemployment. AGI remains a theoretical concept, but it may become a tangible reality. It is crucial to weigh its possible benefits and risks.

Potential Benefits of AGI:

• Solving complex global issues like water scarcity, climate change, or interplanetary colonization.

• Enhancing daily life in transportation, healthcare, and education.

• Providing more profound insights into the universe (e.g., analyzing data from the James Webb Telescope).

Potential Risks of AGI:

• Mass unemployment as machines outperform humans in jobs.

• The development of autonomous weapons poses an existential threat.

• Loss of control leading to potential human extinction.

Researchers are exploring ways to integrate human-like emotions and skills into machines. AGI is also referred to as Strong AI.

5. Limited Memory AI

Limited Memory AI is an AI capable of storing and using a finite amount of information. Past data is stored in memory and is a reference for future decisions.

This model is built on Reinforcement Learning algorithms, where an intelligent agent interacts with its environment, receives input-output pairs, and learns patterns from the data.
However, its limited storage capacity can hinder learning and decision-making, as older data may overshadow new information.

Limited Memory AI is useful in dynamic environments, such as video games, where past player experiences guide future moves. However, it generally underperforms compared to unlimited-memory models in the long run.

Most current AI applications, including deep learning systems, fall into this category. For instance, an image recognition AI is trained on vast datasets to create a reference model for future tasks. When scanning new images, it uses its training data to label content with increasing accuracy.

6. Theory of Mind AI

Theory of Mind AI refers to models that understand human emotions, personalities, and beliefs, enabling social interaction. However, achieving this goal remains a distant challenge. This theory examines mental processes like thinking, perception, decision-making, and knowledge transfer, aiming to replicate them in AI.

A key concept is the ability to predict and understand others’ perspectives. Applying Theory of Mind in AI could lead to systems interacting naturally with humans, advancing intelligent user interfaces, and machine-to-machine communication.

This field remains a major AI challenge, with researchers working to overcome technical limitations in modeling human thought patterns. Artificial Emotional Intelligence and advancements in decision-making theory are closely related areas.

Michael Jordan’s AI and machine learning decision-making research, presented at ICLR 2020 and further discussed in a 2023 event, provides additional insights (publicly available online).

7. Self-Learning AI

Self-learning in AI refers to systems that improve autonomously without human intervention. These systems collect data, analyze patterns, and continuously enhance performance using Deep Neural Networks and Reinforcement Learning.

Advantages of Self-Learning AI:

• High adaptability to environmental changes.

• Ability to handle complex tasks.

Challenges:

• Requires extensive training data.

Advances in machine learning have made self-learning AI a hot topic. While unlikely to be achieved soon, it represents a future possibility that figures like Elon Musk and Stephen Hawking have warned about.