Data, Analytics & AI

Understanding the different types of AI

Get a full overview of the different types of AI along with their applications.

From Siri to self-driving cars, artificial intelligence (AI) has woven itself into the fabric of our everyday lives. But with so many terminologies and classifications, understanding the types of AI can feel overwhelming. Fear not, dear reader! Let us embark on an enlightening journey to unravel the mysteries of AI, its various types, and the impact it has on our world.

Key takeaways

  • AI is a technology divided into multiple categories based on its functionality, learning capabilities and application.
  • Natural Language Processing (NLP), Computer Vision and Robotics are some of the most prominent uses for AI.
  • Current trends in AI point to its huge potential but responsible use must be ensured through consideration of ethical implications.

Introduction to AI

Artificial intelligence involves building smart machines from vast datasets, including the development of artificial intelligence systems. It aims to replicate human intelligence and perform tasks that would otherwise require our input, such as decision-making, object recognition, and problem-solving.

The term artificial intelligence also refers to AI systems integrating previous knowledge and experiences to speed up and enhance the accuracy and efficiency of human efforts. With the use of complex algorithms and methods, machines can make independent decisions that revolutionise industries and alter our lifestyle and work habits.

Machine learning and deep learning, two subfields of AI, lie at the heart of this technology, utilising complex algorithms and neural networks to empower machines to learn and adapt. This blog post will explore the different types of AI – Narrow AI, General AI, and Superintelligent AI, along with their applications across various sectors.

Types of AI 

The capabilities of AI systems can be classified into three primary categories: Narrow AI, General AI, and Superintelligent AI. Narrow AI, also known as Weak AI, focuses on performing specific tasks without the ability to learn beyond its intended purpose.

General AI, or Strong AI, possesses human-like intelligence, capable of executing multiple tasks simultaneously. Lastly, Superintelligent AI surpasses human intelligence, performing any task better than humans. Let’s examine each of these AI types and their distinct capabilities more closely.

Narrow AI (Artificial Narrow Intelligence or Weak AI)

Artificial Narrow Intelligence (ANI) is a type of Artificial Intelligence which mainly focuses on executing specific commands. These AI tools can perform proficient tasks as per the instructions provided to them. These systems fulfill particular tasks without the capacity to learn beyond their intended purpose, such as image recognition software, self-driving cars, and AI virtual assistants like Siri. Although Narrow AI has made significant advancements in recent years, it is not without its drawbacks.

The limitations of Narrow AI include:

  • Lack of flexibility
  • Incomplete comprehension of context
  • Incapacity to adapt and learn
  • Reliance on data

Despite these shortcomings, Narrow AI continues to play an essential role in many AI applications, providing practical solutions to everyday problems and enhancing user experiences.

General AI (Artificial General Intelligence or Strong AI)

Artificial General Intelligence (AGI) is a more advanced form of AI, capable of learning, thinking, and carrying out a vast array of tasks in a manner comparable to humans. The objective of designing AGI is to create machines that can execute multifaceted duties and serve as lifelike, intellectually comparable assistants to people in daily life. However, we are still considerably distant from constructing an AGI system.

The realisation of AGI requires the development and refinement of fundamental technologies, such as supercomputers, quantum hardware, and generative AI models like ChatGPT. As researchers continue to push the boundaries of AI, the prospect of achieving General AI remains an exciting and significant milestone in the field.

Superintelligent AI

Super AI, or Artificial Superintelligence (ASI), is the theoretical level of AI wherein its capabilities exceed that of human intelligence, and it attains self-awareness. These hypothetical AI systems possess the potential to become the most proficient form of intelligence on the planet, outstripping human intelligence and being markedly better at all tasks we undertake.

The concept of self-aware AI raises ethical concerns and debates surrounding the creation of sentient AI. While the idea of superintelligent AI might sound like science fiction, it serves as a reminder that as AI research and development continues to advance, potential risks and ethical implications must be carefully considered and addressed.

AI based on functionality

Another way to classify AI systems is based on their functionalities, which can be divided into categories such as types of artificial intelligence:

  1. Reactive Machines: These AI systems perform tasks based on current data without learning from past experiences.
  2. Limited Memory AI: These AI systems utilise past data to make informed decisions and enhance their performance over time.
  3. Theory of Mind AI: These AI systems focus on understanding and interpreting the mental states of other agents.

Let’s examine each of these functional classifications of AI in greater detail.

Reactive Machines

Reactive Machines are basic AI systems that solely operate on current data and execute specific tasks without gaining knowledge from past experiences. Examples of reactive machines include:

  • IBM’s Deep Blue, which defeated chess grandmaster Garry Kasparov in 1997.
  • AI systems used for filtering out spam from email inboxes.
  • AI systems used for recommending movies based on recent Netflix searches.

Reactive machines can be employed for executing fundamental autonomous processes.

Despite their usefulness in certain applications, reactive machines have limitations. They don’t allow for learning or adaptation; they can only recognise and respond to a certain amount of data. Consequently their functionality is limited in comparison to those that have the ability to learn and improve. Moreover, they are unable to build upon previous knowledge or perform complex tasks that require learning and adaptation.

Limited Memory AI

Limited Memory AI systems, as the name suggests, utilise past data to make informed decisions and enhance their performance over time. By observing other vehicles’ speed and direction, self-driving cars can navigate the road and adjust accordingly. This kind of AI evolves over time as it is taught on more data, making it more advanced than reactive machines.

Limited Memory AI has found applications in various sophisticated use cases, such as chatbots, virtual assistants, and natural language processing. These systems demonstrate the potential of AI to learn from past experiences and improve their capabilities over time, offering more flexible and adaptable solutions compared to reactive machines.

Theory of Mind AI

Theory of Mind AI is an advanced category of AI systems that focus on comprehending and interpreting the human mind, including emotions, beliefs, and intentions. These systems are still under research and development, with the goal of enabling AI to better understand and interact with humans and other agents. For example, a self-driving car that is aware of a neighbor’s child playing near the street after school would naturally reduce speed when passing that neighbor’s driveway – something a basic limited memory AI would be unable to do.

Despite the potential benefits of Theory of Mind AI in various applications, there are concerns and challenges to consider. Emotional cues are highly complex, and it may take a significant amount of time for AI machines to master them, leading to potential errors during the learning stage.

Additionally, once technologies can detect and respond to emotional signals, it could result in the automation of certain occupations, raising ethical and societal questions.

AI based on learning capabilities

The learning capabilities of AI systems can be classified into categories such as:

  1. Machine Learning: This allows us to give machines the ability to interpret, process, and analyse data, helping them solve real-world problems.
  2. Deep Learning: A subset of Machine Learning, it utilises artificial neural networks to acquire knowledge from data.
  3. Reinforcement Learning: Another type of Machine Learning, it uses rewards and punishments to acquire knowledge from its environment.

Let’s further investigate each of these classifications based on learning capabilities.

Machine Learning

Machine Learning is an AI system that acquires knowledge from data in order to generate predictions and decisions. It operates by examining data and recognising patterns or associations within the data. Machine Learning algorithms use techniques to estimate a target function and predict output variables based on input variables. By inputting training data into the algorithm, it learns from the data to generate a model that can make predictions or identify patterns in new data.

Machine Learning algorithms reduce human intervention and can be utilised for a broad range of tasks involving data analysis and pattern recognition. They are essential in many AI applications, including image recognition, natural language processing, and self-driving cars. As more data becomes available, Machine Learning models continue to improve their predictions and decision-making capabilities.

Deep Learning

Deep Learning is an advanced AI system that utilises neural networks to solve intricate problems, such as image recognition and natural language processing. It is a subset of Machine Learning centered around the application of artificial neural networks with multiple layers. Deep learning neural networks possess multiple layers, comprising input and output layers, and are able to carry out complex operations such as representation and abstraction.

Examples of Deep Learning applications include facial recognition algorithms on Facebook, self-driving cars, and virtual assistants such as Siri and Alexa. As Deep Learning continues to advance and mature, it is expected to play an increasingly significant role in AI research and development, unlocking new possibilities and applications across various industries.

Reinforcement Learning

Reinforcement Learning is an AI system that learns through trial and error, optimising its actions to achieve specified objectives. It involves:

  • establishing a system of rewarding desired behaviours
  • punishing negative behaviours
  • learning the most efficient behaviour in an environment
  • acquiring the maximum reward.

Reinforcement Learning focuses on learning optimal actions through exploration and exploitation, distinguishing itself from other machine learning methods like supervised and unsupervised learning.

Noteworthy examples of Reinforcement Learning in practical applications include:

  • automated robots
  • natural language processing
  • marketing and advertising
  • image processing
  • game optimisation and simulation
  • self-driving cars
  • industry automation
  • finance and economics
  • healthcare
  • broadcast journalism.

As Reinforcement Learning algorithms continue to improve, they are expected to play a crucial role in the future development of AI systems and their applications.

AI based on application

AI systems and their applications can be found in various industries, transforming the way we live and work. From healthcare to banking and finance, marketing, and entertainment, AI is employed across industries worldwide, offering innovative solutions and enhancing user experiences.

Some examples of AI applications in everyday life include Google’s predictive search algorithm, Netflix’s movie recommendation system, and Facebook’s facial recognition tagging system. As AI continues to advance, its applications are expected to grow exponentially, with new use cases emerging in diverse sectors and industries.

Natural Language Processing (NLP)

Natural Language Processing (NLP) is a branch of AI that facilitates machines to comprehend and process human language. NLP has applications in enhancing user experience and communication, with potential use cases in various industries and fields.

In the following subsections, two specific applications of NLP – Computer Vision and Robotics, will be discussed.

Computer Vision

Computer Vision is an AI system that enables machines to interpret and analyse visual information from the world, such as images and videos. It utilises pattern recognition algorithms to educate computers to interpret and comprehend the visual world, analogous to how the human brain comprehends visual information. By employing computer vision systems, we can enable applications such as:

  • facial recognition
  • object detection and tracking
  • image and video analysis
  • autonomous vehicles.

The potential of computer vision is vast, with applications ranging from security and surveillance to healthcare and entertainment. As computer vision technology continues to advance, we can expect to see even more innovative and impactful applications emerge in the future.


Robotics is the field of AI that incorporates AI systems into robots to perform tasks autonomously. By integrating AI into robots, they can explore their environment, identify and recognise objects, and handle objects without human interference. AI empowers robots with capacities like spatial relations, computer vision, and motion control, enabling them to carry out tasks that necessitate intelligence and adaptability.

Robotics has applications in various industries, including manufacturing, healthcare, and service industries. As AI research and development continue to progress, we can expect to see increasingly advanced and capable robots emerging, with the potential to revolutionise the way we work and live.

Current and future trends in AI

The current state of AI research and development is at its peak, with daily breakthroughs being made and new technologies emerging. AI has the potential to augment efficiency, productivity, and accuracy in various sectors, with predictions suggesting that it may drive revenue and profit growth, doubling economic growth rates by 2035 and generating trillions of dollars in value.

With the ongoing advancement of AI, researchers are striving to develop basic versions of self-aware AI, extending the limits of possibilities and prompting significant ethical and societal discussions. The future of AI holds exciting and transformative possibilities, but it is crucial to consider the potential risks and ethical implications that come with these advancements.


This blog post has covered a broad range of AI types, their applications, and potential influence on society and future technology. From Narrow AI systems designed for specific tasks to the hypothetical Superintelligent AI, the capabilities and potential of artificial intelligence are vast and ever-evolving.

While we continue to expand the limits of AI research and development, we must take into account the ethical and societal consequences of these advancements, aiming for a future where AI enhances every aspect of our lives.

In conclusion, the realm of AI is vast and complex, encompassing various types, functionalities, and learning capabilities. From enhancing user experiences in everyday life to revolutionising industries, AI holds the key to transformative advancements in technology and society. As we continue to explore and unlock the potential of AI, it is crucial to consider the ethical implications and potential risks associated with these powerful technologies, ensuring a future where AI serves the greater good.


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AI frequently asked questions

What are the 4 main types of AI?

AI can be categorised into four primary types: reactive, limited memory, theory of mind and self-aware.

What type of AI is Siri?

Siri is an example of conversational AI, utilising machine learning and natural language processing to respond to queries.

What is the difference between Narrow AI and General AI?

Narrow AI is designed to do one task efficiently, while General AI can solve complex problems by mimicking human intelligence.

How does Reinforcement Learning differ from other machine learning methods?

Reinforcement Learning focuses on finding optimal actions, making it distinct from other machine learning methods such as supervised and unsupervised learning.

What are some current and future trends in AI research and development?

AI research and development is a growing field, with job openings increasing and projections that AI will double economic growth rates by 2035. It is expected to generate trillions of dollars in value.

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