Artificial Intelligence (AI) is becoming a widely popular technology today, present in many areas of life from business and education to healthcare. So, what is artificial intelligence and what are the types of AI? Understanding common types of artificial intelligence will help us grasp how AI works and apply it effectively in practice.
Artificial Intelligence (AI) is a technology that enables machines (especially computers) to “learn” and “think” like humans. Instead of programming computers with fixed instructions, AI uses machine learning algorithms to learn from data and simulate human intellectual capabilities.
Thanks to this, computers can perform tasks requiring reasoning, such as problem analysis, language comprehension, voice and image recognition, or making intelligent decisions.
To better understand AI, it is often classified in two main ways: (1) classification based on intelligence development (the intelligence or capability of AI compared to humans) and (2) classification based on function and similarity to humans (how AI operates and behaves compared to human intelligence). Today, let’s explore INVIAI to learn in detail about each AI type according to these two classification methods below!
AI Classification Based on Development Level (ANI, AGI, ASI)
The first classification divides AI into three main types based on the intelligence level and capability range of the AI system. These are Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), and Artificial Super Intelligence (ASI).
Among these, Artificial Narrow Intelligence (or Narrow AI) is the only type currently existing in reality, while Artificial General Intelligence and Artificial Super Intelligence remain in research or theoretical stages. Let’s look at the characteristics of each type:
Narrow Artificial Intelligence (Artificial Narrow Intelligence)
Narrow AI refers to AI systems designed to perform a specific task or a limited set of tasks. Importantly, this type of AI is only intelligent within a narrow scope for which it is programmed, without the ability to understand or learn beyond that scope. Most AI applications today fall under Narrow AI, and in fact, this is the only type of AI widely used.
Typical examples of Narrow AI include virtual assistants like Siri, Alexa, Google Assistant – they can recognize voice commands to set alarms, search for information, send messages... but cannot perform tasks outside their pre-programmed functions. Additionally, Narrow AI is present in many other familiar applications, including:
- Recommendation systems on platforms like Netflix, Spotify (suggesting movies or songs based on user preferences).
- Automated chatbots supporting customers, simulating conversations to answer basic questions via text or voice.
- Self-driving cars (such as Tesla electric vehicles) and industrial robots – they use AI to operate autonomously, though within predefined scenarios.
- Image, facial, and voice recognition – for example, facial recognition to unlock phones or voice translation (Google Translate).
These applications show that Narrow AI is everywhere in daily life and often outperforms humans in specific tasks (e.g., AI can analyze large amounts of data faster than humans). However, Narrow AI lacks general “intelligence”; it cannot self-aware or understand beyond its specialized domain.
General Artificial Intelligence (Artificial General Intelligence)
General AI refers to artificial intelligence with human-level capabilities across all intellectual aspects. This means a strong AI system can understand, learn, and perform any intellectual task a human can do, with the ability for independent thinking, creativity, and flexible adaptation to entirely new situations.
This is the ultimate goal AI researchers aim for – creating a machine intelligence with consciousness and general intelligence like the human brain.
However, currently General AI exists only in theory. No AI system has yet reached true AGI level. Developing General AI requires breakthroughs in scientific research, especially in simulating human thought and learning processes. In other words, we still don’t know how to teach machines self-awareness and flexible intelligence exactly like humans.
Some modern AI models (such as large language models like GPT) have shown glimpses of general intelligence traits, but fundamentally they remain Narrow AI trained for specific tasks (e.g., understanding and generating text), not true General AI.
Super Artificial Intelligence (Artificial Super Intelligence)
Super AI is the concept of artificial intelligence far surpassing human capabilities in every aspect. A Super AI system would not only do what humans can do but do it much better – faster, smarter, and more accurately than humans in all fields.
Super AI could learn and improve itself autonomously and even make decisions and solutions that humans have never imagined. This is considered the highest stage of AI development, where machines achieve superintelligence.
Currently, Super AI exists only in imagination and theory – no such system has been created.
Many experts believe achieving Super AI may be very distant or uncertain. Moreover, the prospect of superintelligent AI raises many concerns: If machines become smarter than humans, will they control humans or pose risks to humanity? Ethical and safety issues surrounding superintelligent AI are hotly debated topics.
Nevertheless, scientists continue researching this goal, believing that Super AI, if well controlled, could help solve humanity’s most complex problems in the future.
(In summary, based on development level, we currently have only Narrow AI – specialized AI systems for specific tasks. General AI is still under research, and Super AI remains a future concept. Next, we will look at AI classification based on behavior and the degree of “intelligence” in operation.)
AI Classification Based on Function (Reactive, Limited Memory, Theory of Mind, Self-Aware)
The second classification focuses on how AI operates and its level of “understanding” compared to humans. According to this, AI is divided into four types in ascending order: Reactive Machines, Limited Memory AI, Theory of Mind AI, and Self-Aware AI.
Each type represents an evolutionary stage in AI’s ability to mimic human cognition and interaction. Below are the details of each type:
Reactive AI Technology (Reactive Machine)
This is the simplest level of artificial intelligence. Reactive AI systems only respond to current situations based on their programming, without the ability to “remember” past experiences. In other words, they have no memory and cannot use past experience to influence future decisions.
A classic example of Reactive AI is chess-playing programs. Computers like Deep Blue can analyze the current board situation and choose the best move based on algorithms but do not “remember” previous games or learn from experience; each game starts fresh like a machine reflex.
Despite this, Reactive AI can achieve very high performance in its tasks – in fact, computers have defeated world chess grandmasters, demonstrating superior computational power within a narrow scope.
Reactive AI is characterized by fast response speed and predictable behavior. However, its biggest limitation is lack of learning ability: if the environment or rules change from the original programming, the system cannot adapt.
Today, Reactive AI is still widely used in automated systems requiring immediate and simple responses, such as automatic controllers in industrial machines operating under fixed conditions.
Limited Memory AI (Limited Memory)
Limited Memory AI is the next level, where AI systems can store and use a limited amount of past information to make decisions. Unlike pure Reactive AI, this type learns from historical data (albeit limited) to improve future responses.
Most modern machine learning models fall into this category, as they are trained on existing datasets and use learned experience to make predictions.
A typical example of Limited Memory AI is self-driving car technology. Autonomous vehicles collect data from sensors (cameras, radar, etc.) about the surrounding environment and temporarily remember important information (such as positions of other vehicles and obstacles) to decide when to accelerate, brake, or steer safely.
Although the car does not remember everything it has ever seen, it continuously updates new information during operation and uses a “short-term memory” segment to handle situations – this is the hallmark of Limited Memory AI.
Many current Narrow AI applications also belong to this Limited Memory group. For example, facial recognition systems work by learning from a large set of sample images (training memory) and then remembering key facial features in new images to identify matches in the database.
Virtual assistants or smart chatbots also rely on trained models and can remember short-term conversational context (e.g., your previous question) to respond more naturally. Overall, Limited Memory AI accounts for most AI systems today, offering better performance than Reactive AI by leveraging past data, but still lacks full self-awareness.
Theory of Mind (Theory of Mind)
“Theory of Mind” in AI is not a specific technology but a concept referring to an AI intelligence level capable of understanding humans at a deeper level. The term borrows from the psychological concept of Theory of Mind – the ability to understand that others have emotions, thoughts, beliefs, and intentions of their own. An AI reaching Theory of Mind would be able to recognize and infer the mental states of humans or other entities during interaction.
Imagine a robot that knows when you are happy or sad based on your facial expressions and voice tone, then adjusts its behavior accordingly – this is the goal of Theory of Mind AI. At this level, AI not only processes data mechanically but must understand factors like emotions and motivations of the communication partner. This would allow AI to interact socially more naturally, creating virtual assistants or robots capable of empathy and appropriate responses like real humans.
Currently, Theory of Mind AI is still in the research phase. Some AI systems have begun integrating emotion recognition (e.g., detecting angry tone or sad facial expressions), but achieving full Theory of Mind is still far off. This is a necessary step toward General AI because to have human-like intelligence, machines must also understand humans.
AI researchers continue experimenting to teach machines to understand non-data factors such as emotions and culture – a significant challenge in this field.
Self-Aware AI (Self-Aware AI)
This is the highest level and also the greatest ambition in AI: creating machines with self-awareness. Self-Aware AI means an AI system that not only understands the world around it but also knows who it is, possessing self-consciousness and perceiving its own state just like a human with self-awareness.
Currently, Self-Aware AI does not exist; it remains a hypothetical idea. For a machine to reach this level, it would require copying not only human intelligence but also the human soul – something we do not yet fully understand ourselves. If one day Self-Aware AI becomes reality, it would be a major milestone for humanity but would also bring countless ethical issues.
For example, would a self-aware AI be considered a “living entity” with rights? If it has emotions, do we have moral responsibilities toward it like we do with humans? More importantly, what would happen if AI self-awareness surpasses humans – would it still obey commands or decide its own goals?
These questions remain unanswered. Therefore, Self-Aware AI so far appears only in science fiction books or movies.
Nevertheless, research toward this level helps us better understand the nature of consciousness and intelligence, enabling the creation of smarter AI systems at lower levels. The future of Self-Aware AI may be distant, but it is the ultimate destination in humanity’s AI development journey.
It is clear that the common types of artificial intelligence today are mainly Narrow AI – intelligent systems specialized in solving specific tasks or groups of tasks. The virtual assistants, chatbots, self-driving cars, recommendation systems, voice recognition, and more around us are all results of highly developed Narrow AI.
Meanwhile, General AI and higher levels like Theory of Mind AI or Self-Aware AI remain future prospects, requiring many more years (even decades) of research. Despite many challenges, continuous AI advancements promise to open new horizons for science and human life.
Understanding the types of AI helps us grasp where this technology currently stands and how far it can develop, providing a proper perspective to apply AI effectively and safely in life and work.
In summary, artificial intelligence is making remarkable progress and becoming increasingly intertwined with humans. Classifying AI into different levels and types helps us clearly understand the nature of each technology, make the most of AI’s advantages today, and prepare for the future when more advanced AI forms emerge.
With the rapid development of computer science, who knows, in the not-so-distant future, we may witness the emergence of General AI or even Super Artificial Intelligence – currently only a matter of imagination. Certainly, AI will continue to be a key field shaping the future of human society, and understanding it correctly from now on is extremely important.
