Types of Artificial Intelligence: Narrow, General, and Super AI Explained
Artificial Intelligence (AI) is a technology that allows computers to think and act like humans. These systems are designed to learn from previous experiences to improve precision, speed, and effectiveness. AI can be divided into different types based on the following criteria −
• Capabilities
• Functionality
1. Based on Capabilities
AI is categorized into the following types based on capabilities −
Normal AI (Weak AI)
Narrow AI allow to performing a specific task intelligently. Narrow AI is bound with limits and cannot perform beyond its limits.
Apple Siri, Alexa, and others voice assistants are good examples of Narrow AI, as they are trained to function a limited range of tasks. Other examples of Narrow AI include facial recognition, chess games and recommendation systems.
General AI (Strong AI)
General AI allow to perform intellectual tasks just like humans efficiently. These systems are trained to learn, understand, adapt, and think like humans.
Although, General AI remains a theoretical concept hoping to develop in the future by the researchers. It is quite difficult, as the system need to trained to be self-aware, make independent choices and recognize its environment. Potential applications could include robots.
Super AI
Super AI refers to a form of artificial intelligence that exceeds human intelligence and can accomplish tasks more effectively than people. It represents a more advanced iteration of general AI, where machines are capable of making their own choices and independently solving problems.
This type of AI would not only carry out tasks but also comprehend and interpret emotions, responding in a human-like manner. Although it is still a theoretical concept, creating such models would be quite challenging.
Based on Functionality
AI can be categorized into different types according to its functionality −
Reactive Machines
Reactive Machines are the simplest form of artificial intelligence. These machines function solely based on current data and do not retain any past experiences or learn from previous actions. Furthermore, these systems react to specific inputs with fixed outputs that cannot be altered.
IBM's Deep Blue serves as an excellent example of reactive machines. It was the first computer system to defeat a reigning world chess champion, Garry Kasparov. It could recognize pieces on the chessboard and make predictions but lacked the ability to remember or learn from past games.
Google's AlphaGo is another instance of a reactive machine, playing the board game Go using a similar pattern recognition approach without acquiring knowledge from earlier games.
Limited Memory
Limited Memory is the most commonly used category in many modern AI applications. It can retain past experiences and learn from them to enhance future results. These machines keep historical data to forecast and make decisions but lack long-term memory. Key applications like autonomous systems and robotics frequently depend on limited memory.
Chatbots serve as an example of limited memory, as they can recall recent conversations to enhance the flow and relevance. Moreover, self-driving cars exemplify this by observing the road, traffic signs, and surroundings to make choices based on past experiences and current conditions.
Theory of Mind
Theory of Mind can comprehend human emotions, beliefs, and intentions. Although this type of AI is still under development, it has allowed machines to accurately interpret emotions and adjust their behavior accordingly, enabling effective interaction with humans. Some potential applications of this type include collaborative robots and human-robot interaction.
Self-Awareness
Self-Aware AI signifies the future of artificial intelligence, possessing self-consciousness and awareness akin to humans. While we are still far from realizing self-aware AI, it remains a crucial goal for AI development. The applications of self-aware AI could include fully autonomous systems capable of making moral and ethical decisions.
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