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Types of AI

Types of Artificial Intelligence (AI)

Artificial Intelligence (AI) is a broad and rapidly evolving field. AI can be classified into various types based on different criteria. It’s important to note that these are just a few of the many ways to categorize AI. As AI technology continues to evolve, new classifications may emerge.

Below, we explore AI classification from several perspectives:

Based on Capabilities

  • Narrow AI (Weak AI): This is the most common type of AI currently. It is designed to perform specific tasks, such as:
    • Voice Assistants: Siri, Google Assistant
    • Image Recognition: Facial recognition, object recognition
    • Recommendation Systems: E-commerce recommendations, music recommendations
    • Game AI: AlphaGo
  • General AI (Strong AI):
    • This type of AI is still hypothetical. It would have the ability to understand or learn any intellectual task that a human being can.
  • Super AI:
    • This is also hypothetical and would surpass human intelligence and capabilities.

Based on Functionalities:

  • Reactive Machines:
    • Simple AI systems that react to some input with a predefined response. These AI systems can only react to the present situation and cannot learn from past experiences.
    • Examples: Deep Blue, IBM’s chess-playing computer.
  • Limited Memory AI:
    • These systems can store and process past data to inform future decisions.
    • Examples: Self-driving cars.
  • Theory of Mind AI:
    • This type of AI would be able to understand and respond to human emotions and intentions. This type is still in research.
  • Self-Aware AI: This is the most advanced type of AI, which would have its own consciousness, self-awareness, and emotions. This type is purely hypothetical.

Based on Techniques:

  • Supervised Learning
    • Trained on labeled data.
    • Examples: Classification tasks like spam detection, image classification.
  • Unsupervised Learning
    • Trained on unlabeled data to find hidden patterns.
    • Examples: Clustering, market basket analysis.
  • Reinforcement Learning
    • Trained by receiving rewards or penalties.
    • Examples: AlphaGo, robotics.
  • Deep Learning
    • A subset of ML that uses neural networks with many layers.
    • Examples: Image and speech recognition.