1. Reactive Machines
Description: Basic AI that reacts to inputs with no memory or learning.
How it works: Pattern recognition and pre-programmed responses in real time.
Examples: IBM Deep Blue; simple obstacle-avoiding robots.
2. Limited Memory AI
Description: Learns from historical data for specific tasks; memory is narrow.
How it works: Combines training data with real-time observations; retraining needed to improve.
Examples: Self-driving car perception and planning; virtual assistants; most industry ML models.
3. Theory of Mind AI (Emerging)
Description: Aims to model human emotions, intentions, and social context.
How it works: Research-stage combination of NLP, affective computing, and social cognition modeling.
Examples: Experimental emotion-sensing customer-service bots; companion robots (early-stage).
4. Self-Aware AI (Not Yet Achieved)
Description: Hypothetical AI with consciousness and self-awareness.
Status: Not realized in practice.
5. Narrow AI (Weak AI)
Description: Purpose-built systems excel at specific tasks but lack general reasoning.
Examples: Recommendation engines, image recognition, radiology assistants, trading bots.
6. General AI (AGI)
Description: Would perform any intellectual task a human can across domains.
Status: A research goal; not available today.
7. Supervised Learning Systems
Description: Trained on labeled datasets where the correct outputs are known.
Applications: Spam detection, quality inspection, predictive maintenance.
8. Unsupervised Learning Systems
Description: Find patterns and structure in unlabeled data.
Applications: Market segmentation, customer clustering, bioinformatics pattern discovery.
9. Reinforcement Learning Systems
Description: Learn via trial and error using rewards and penalties.
Applications: Robotics control, game-playing agents, logistics and routing optimization.
10. Deep Learning Systems
Description: Multi-layer neural networks that learn complex representations.
Applications: Speech recognition, autonomous driving perception, natural language generation.
11. Generative AI
Description: Produces new content (text, images, audio, video, code) from learned patterns.
Examples: Large language models; image, music, and code generation tools.
12. Edge AI
Description: Runs AI on devices (phones, wearables, cameras, IoT) rather than only in the cloud.
Applications: Real-time vision on smart cameras, on-device health monitoring, factory automation.
13. Hybrid AI Systems
Description: Combine multiple approaches (e.g., rules and neural networks) for better reliability.
Applications: Autonomous systems, decision-support platforms, enterprise AI workflows.
Summary
The most widely used systems today are narrow AI powered by machine learning and deep learning, including generative AI. Theory of Mind AI and AGI remain largely research goals, while self-aware AI is still hypothetical.