- Artificial Intelligence Tutorial
- AI - Home
- AI - Overview
- AI - History & Evolution
- AI - Types
- AI - Terminology
- AI - Tools & Frameworks
- AI - Applications
- AI - Real Life Examples
- AI - Ethics & Bias
- AI - Challenges
- Branches in AI
- AI - Research Areas
- AI - Machine Learning
- AI - Natural Language Processing
- AI - Computer Vision
- AI - Robotics
- AI - Fuzzy Logic Systems
- AI - Neural Networks
- AI - Evolutionary Computation
- AI - Swarm Intelligence
- AI - Cognitive Computing
- Intelligent Systems in AI
- AI - Intelligent Systems
- AI - Components of Intelligent Systems
- AI - Types of Intelligent Systems
- Agents & Environment
- AI - Agents and Environments
- Problem Solving in AI
- AI - Popular Search Algorithms
- AI - Constraint Satisfaction
- AI - Constraint Satisfaction Problem
- AI - Formal Representation of CSPs
- AI - Types of CSPs
- AI - Methods for Solving CSPs
- AI - Real-World Examples of CSPs
- Knowledge in AI
- AI - Knowledge Based Agent
- AI - Knowledge Representation
- AI - Knowledge Representation Techniques
- AI - Propositional Logic
- AI - Rules of Inference
- AI - First-order Logic
- AI - Inference Rules in First Order Logic
- AI - Knowledge Engineering in FOL
- AI - Unification in First Order Logic (FOL)
- AI - Resolution in First Order Logic (FOL)
- AI - Forward Chaining and backward chaining
- AI - Backward Chaining vs Forward Chaining
- Expert Systems in AI
- AI - Expert Systems
- AI - Applications of Expert Systems
- AI - Advantages & Limitations of Expert Systems
- AI - Applications
- AI - Predictive Analytics
- AI - Personalized Customer Experiences
- AI - Manufacturing Industry
- AI - Healthcare Breakthroughs
- AI - Decision Making
- AI - Business
- AI - Banking
- AI - Autonomous Vehicles
- AI - Automotive Industry
- AI - Data Analytics
- AI - Marketing
Artificial Intelligence - Types
Artificial Intelligence (AI) is a technology that enables computers to think and act like humans. These systems are trained to learn from past experiences to enhance speed, precision, and effectiveness. Further based on the following criteria artificial intelligence can be categorized into different types −
Based on Capabilities
AI is classified into the following types based on capabilities −
Normal AI (Weak AI)
Narrow AI is a type of AI that enables to perform a specific task with intelligence. Narrow AI is trained only for a specific task and fails to perform beyond its limitations.
Voice assistants like AppleSiri, Alexa, and others are a good example of Narrow AI, as they are trained to operate within a limited range of functions. Some of the other examples of Narrow AI are chess games, facial recognition, and recommendation engines.
General AI (Strong AI)
General AI is a type of AI that enables to perform intellectual tasks as efficient;y as humans. The systems are trained to have the capability to understand, learn, adapt, and think like humans.
Though it seems efficient, the General AI still seems to be a theoretical concept that researchers aim to develop in the future. It is quite challenging, as the system should be trained to be self-conscious, to get aware of the surroundings, and to make independent decisions. The potential applications could be robots.
Super AI
Super AI is a type of AI that surpasses human intelligence and can perform any task better than humans. It is an advanced version of general AI, where machines make their own decisions and solve problems by themselves.
Such AI would not only perform tasks but also understand and interpret emotions and respond like humans. While it remains hypothetical, development of such models would be complex.
Based on Functionality
AI is classified into the following types based on the functionality −
Reactive Machines
Reactive Machines are the most basic type of artificial intelligence. These machines operate only on the present data and do not store any previous experiences or learn from past actions. Additionally, these systems respond to specific inputs with predetermined outputs and cannot be changed.
IBM's Deep Blue is a great example of reactive machines. It is the first computer system to defeat a reigning world chess champion, Garry Kasparov. It could identify pieces on the board and make predictions but could not store any memories or learn from the past games.
Google's AlphaGo is another example of a reactive machine, playing the board game Go with a similar method of pattern recognition without gaining knowledge from previous games.
Limited Memory
Limited Memory is the most used category in most modern AI applications. It can store past experiences and learn from them to improve future outcomes. These machines store historical data to predict and make decisions but do not have long-term memory. Major applications like autonomous systems and robotics often rely on limited memory.
Chatbots is an example of limited memory, where it can remember recent conversations to improve the flow and relevance. Additionally, self-driving cars is another example that observes the road, traffic signs, and surroundings to make decisions based on past experiences and current conditions.
Theory of Mind
Theory of Mind could understand the human emotions, beliefs, and intentions. While this type of AI is still in development, it has enabled machines to interpret emotions accurately and modify behavior accordingly so that machines could interact with humans effectively. Some of the possible applications of this type are probably collaborating robots and human-robot interaction.
Self-Awareness
Self-Aware AI represents the future of artificial intelligence with self-consciousness and awareness similar to humans.While we are far from achieving the goal of self-aware AI, it is an important objective for the development of AI. The applications of self-aware AI could be fully autonomous systems that could take moral and ethical decisions.