AI categories and features
AI Categories
Artificial intelligence can be organised in several ways, and one common method is to classify different types of AI based on what they are capable of doing. This approach compares systems according to the complexity and extent of their abilities.
1. Artificial Narrow Intelligence (ANI)
Artificial Narrow Intelligence refers to AI systems built to perform a specific task or a small set of tasks. While they can be highly effective within their specialised domain, their reasoning abilities are far more limited than those of humans. Nearly all AI technologies in use today fall into this category.
2. Artificial General Intelligence (AGI)
Artificial General Intelligence describes a theoretical form of AI that would possess broad, adaptable intelligence, allowing it to learn and carry out most tasks a human can. Although its thinking processes might not exactly match human cognition, it would demonstrate comparable versatility. Opinions differ on how close we are to achieving AGI—some believe it may emerge within the next decade, while others argue that it is still far off.
AI Features and categories
Reactive AI
Reactive AI represents the simplest level of artificial intelligence. These systems respond to specific inputs with predetermined behaviours, showing little or no variation in how they act.
Limited memory AI
Limited memory AI differs from reactive AI because it can draw on past data, apply more advanced classification methods, and make decisions informed by previous experience.
Generalisation Ability
Generalisation ability describes how an Artificial General Intelligence (AGI) can take what it has learned in one situation and apply it to completely new, unseen contexts.
Embodied Interaction
Embodied interaction describes how an AI system operates within and responds to the physical world using a body—whether that body is robotic, virtual, or equipped with sensors. Unlike AI that exists only as software, embodied AI can sense its surroundings, move through space, and take actions, allowing it to learn directly from real-world experience.
Advanced learning and reasoning
AGI systems are expected to go beyond pattern recognition and memorisation. Advanced learning and reasoning refer to the ability to form abstract concepts, draw inferences, and solve complex problems using logic and structured thinking.
Life-long and Meta Learning
Lifelong learning allows an AGI system to continually build and update its knowledge base throughout its existence, similar to how humans learn over time. Meta-learning—often described as “learning to learn”—gives the system the ability to refine its own methods of learning by reflecting on past performance.
Autonomy and Resilience of AI
AGI systems need to make independent decisions in complex and changing environments, demonstrating both autonomy and resilience. Autonomy allows the system to act without constant human guidance, while resilience ensures it can handle unexpected problems. recover from errors etc.