Types of AI: Definition, Working, & Examples

Artificial Intelligence, or AI, is like a toolbox filled with different tools. These tools help computers do smart things. When we say “Types of AI,” we’re talking about the tools in this toolbox. 

Some tools are good at specific jobs, like sorting pictures or talking to you on your phone. Others are like super-smart tools that can learn and figure out new things independently. 

This journey through the different types of artificial intelligence systems is like opening the toolbox and seeing all the cool gadgets inside.

We’ll look at each tool and see what it’s good at, how it works, and why it’s essential in our tech world. But guys, never forget that AI has different advantages and disadvantages. 

What is AI? 

Guys, the full form of AI is “Artificial Intelligence”. And here is the general definition of AI: 

Artificial intelligence, or AI, is the study and development of computer systems that can do things that generally require smart people to do. These jobs are learning, thinking, solving problems, perceiving, and understanding language. AI aims to make machines that can mimic and simulate human intelligence so that they can do difficult jobs on their own. 

To learn more about artificial intelligence, click here: What is AI? 

Types of Artificial Intelligence

Let’s come to the central part of the article, which is about artificial intelligence types. 

Let me first explain that there are three categories for AI types: capabilities, behaviour, and other general types. So, here, I have come up with all the different levels of AI.

Based on Capabilities

• Artificial Narrow Intelligence (ANI) – Weak 
• Artificial General Intelligence (AGI)
• Artificial Super Intelligence (ASI) – Strong 
• Machine Learning
• Deep Learning

Based on Behavior

• Reactive Machines
• Limited Memory Machines
• Theory of Mind AI
• Self-Aware AI

Other Types

• Natural Language Processing (NLP)
• Computer Vision
• Robotics
• Expert Systems
• Swarm Intelligence
• Sentient AI
• Embodied AI
• Hybrid AI
• Augmented Intelligence (AI working alongside human intelligence)
• Explainable AI (XAI)
• Evolutionary Algorithms

Let’s learn about each Ai type. 

Based on Capabilities

AI is classified according to its capabilities, which reflect the range of intelligence levels displayed by artificial systems. This classification allows us to grasp better how AI systems can execute jobs and solve problems.

The types include:

1. Artificial Narrow Intelligence (ANI) 

Definition: AI systems built for a specific task or a limited set of tasks are called ANI. They excel in a specific topic but lack human-level thinking skills.

Working: ANI is programmed to perform a specialized function and operates within well-defined parameters. It relies on pre-set algorithms to execute tasks efficiently.

Example: Virtual personal assistants like Siri or Alexa excel in specific voice recognition and response tasks but lack a general understanding of the world.

2. Artificial General Intelligence (AGI)

Definition: AGI aims for machines with human-like cognitive abilities capable of understanding, learning, and adapting across various tasks similar to humans.

Working: AGI systems can comprehend, acquire, and apply information in various circumstances. They can also execute general intelligence activities and adjust to new settings.

Example: While not truly an AGI, GPT-4 (and its predecessors like GPT-3) represent steps towards AGI. These models can understand and generate human-like text across various topics, demonstrating a high level of language understanding and generation.

3. Artificial Super Intelligence (ASI) 

Definition: A possible type of AI called “artificial superintelligence” (ASI) is more intelligent than humans in all areas, such as creativity, problem-solving, and social intelligence. However, ASI doesn’t exist yet.

Working: ASI would have cognitive abilities far beyond human capacity, enabling it to excel in intellectual tasks, decision-making, and problem-solving to an unprecedented degree.

Example: ASI is now theoretical, with no real-world applications.

5. Machine Learning

Definition: Machine Learning (ML) is a subset of artificial intelligence that enables computers to learn and improve without additional programming. It focuses on developing algorithms that allow robots to identify patterns and make decisions.

Working: ML algorithms process data, identify patterns, and adjust their models to make predictions or decisions. This learning process allows machines to improve over time.

Example: Spam email filters that learn to differentiate between spam and non-spam emails based on user interactions.

6. Deep Learning

Definition: Deep Learning is a subset of machine learning that uses multiple-layer neural networks (deep neural networks). It resembles the structure of the human brain, allowing the system to learn and make decisions.

Working: Deep Learning algorithms process data through multiple layers of networks, extracting hierarchical features for better understanding and decision-making.

Example: Image recognition systems that use deep learning to identify objects or faces in photos.

Based on Behavior

The classification of AI based on behaviour helps us understand how artificial intelligence systems interact with and respond to their surroundings. It explains how AI systems show intelligence and react in various situations.

The types include:

1. Reactive Machines

Definition: Reactive Machines are AI systems programmed to execute specific tasks. They are incapable of learning from experience or adapting to new situations.

Working: These machines follow predetermined instructions and provide responses based on fixed algorithms without the learning capacity.

Example: Chess-playing programs that use pre-defined strategies to make moves but don’t learn or adapt during gameplay.

2. Limited Memory Machines

Definition: Limited Memory AI systems can learn from historical data to some extent. They use previous experiences to make informed judgments, but their memory is limited compared to more advanced learning systems.

Working: These systems store and retrieve past information to improve decision-making, but they don’t comprehensively understand the entire dataset.

Example: Robotic cars that use past data to predict and react to traffic trends but do not learn continually while driving.

3. Theory of Mind AI

Definition: Theory of Mind refers to artificial intelligence systems that can grasp human emotions, beliefs, and intentions, allowing them to engage with people on a more empathic and socially conscious level.

Working: These systems model and interpret human emotions and behaviours, enabling more nuanced and context-aware interactions.

Example: Virtual assistants can recognise and respond to users’ emotional states, adapting their tone and responses accordingly.

4. Self-Aware AI

Definition: Self-aware AI systems possess a consciousness and a sense of self. They can understand their existence, learn from their mistakes, and think about themselves.

Working: These systems can reflect on their state, make autonomous decisions, and adapt their behaviour based on self-awareness.

Example: Self-aware AI is currently a theoretical concept, and there are no real-world examples as it extends beyond AI’s current capabilities.

Other Types

The categorization of AI into specific types based on functionality helps us understand the diverse applications and capabilities of artificial intelligence. Each type addresses different aspects of human cognition and problem-solving. Here are explanations for each type:

1. Natural Language Processing (NLP)

Definition: NLP refers to AI systems’ ability to perceive, interpret, and generate human language, allowing computers and humans to interact using natural language.

Working: NLP algorithms analyze linguistic patterns, semantics, and context to comprehend and respond to human language, enabling applications like chatbots and language translation.

Example: Virtual assistants like Siri or chatbots that understand and respond to spoken or written language.

2. Computer Vision

Definition: Machines can analyze and make decisions based on visual input thanks to computer vision. It is part of image and video analysis, pattern recognition, and object detection.

Working: Computer Vision algorithms process visual information, identifying objects, recognizing faces, and extracting meaningful insights from images or videos.

Examples: Facial recognition systems, self-driving car perception, and image classification applications.

3. Robotics

Definition: Robotics involves integrating AI with mechanical systems to create autonomous or semi-autonomous machines capable of performing physical tasks.

Working: AI algorithms in robotics allow machines to sense their surroundings, make decisions, and carry out physical activities, paving the way for applications in industry, healthcare, and exploration.

Examples: Industrial robots assembling products and surgical robots assisting in medical procedures.

4. Expert Systems

Definition: Expert Systems are artificial intelligence (AI) programs designed to mimic a human expert’s decision-making abilities in a specific topic. 

Working: These systems use knowledge bases and rule-based reasoning to analyze information and provide solutions or recommendations within a particular field.

Example: Diagnostic systems in healthcare, providing expert-like advice based on symptoms and medical knowledge.

5. Swarm Intelligence

Definition: Swarm intelligence is the action of autonomous, self-organized systems. It is often based on how social creatures act.

Working: AI algorithms simulate interactions among agents to achieve collective intelligence, which is helpful in optimization problems and decentralized decision-making.

Example: Swarm robotics for exploration, environmental monitoring, or disaster response.

6. Sentient AI

Definition: Sentient AI refers to systems with self-awareness and consciousness, aiming to mimic human-like cognitive capabilities.

Working: More theoretical, sentient AI involves machines with subjective experiences and self-awareness.

Example: Sentient AI is a concept without real-world examples at present.

7. Embodied AI

Definition: Embodied AI involves machines with physical bodies or avatars, allowing them to interact with the environment.

Working: AI systems integrate sensory information from the physical world to make decisions and adapt to their surroundings.

Example: Social robots capable of physical interactions or AI-driven avatars in virtual environments.

8. Hybrid AI

Definition: Hybrid AI combines different AI technologies, such as machine learning and rule-based systems, to use the strengths of each approach.

Working: Integrating multiple AI techniques enhances performance and flexibility, allowing systems to handle diverse tasks effectively.

Example: Combining rule-based reasoning with machine learning for a medical diagnosis system.

9. Augmented Intelligence (AI Working alongside Human Intelligence)

Definition: Augmented Intelligence focuses on AI systems complementing human abilities, enhancing decision-making and problem-solving.

Working: AI assists humans by providing insights, automating repetitive tasks, and improving overall performance.

Example: Smart assistants, like AI-powered suggestion tools, help professionals in decision-making.

10. Explainable AI (XAI)

Definition: Explainable AI emphasizes the transparency and interpretability of AI systems, allowing users to understand how decisions are made.

Working: XAI provides clear explanations of AI models, making it easier for users to trust and comprehend the reasoning behind AI-driven decisions.

Example: Machine learning models with easily interpretable features and decision-making processes.

11. Evolutionary Algorithms

Definition: Evolutionary Algorithms use principles inspired by biological evolution to optimize solutions for complex problems.

Working: These algorithms involve populations of solutions evolving over generations through processes like selection, mutation, and crossover.

Example: Genetic algorithms for optimizing solutions in fields like engineering and finance.

Conclusion 

So, my dear ones, this article has discussed artificial intelligence types in great detail. We divided AI into three levels and then explored further types, delving into their definitions, workings, and examples.

I want to add here that AI has genuinely transformed this world, constantly updating itself with each passing day. I hope a significant revolution is on the horizon. So, embracing AI and shaping our future within its world is better.

Most Frequently Asked Questions

Below are some popular questions concerning different types of artificial intelligence. You can also take a look at them.