Artificial Intelligence, or AI, refers to computer systems that can perform tasks typically requiring human intelligence. These systems can recognize patterns, make decisions, learn from experience, solve problems, and understand language. Unlike human intelligence which is biological, AI uses algorithms and computational power to mimic intelligent behavior. Key AI capabilities include pattern recognition for identifying trends in data, decision-making for choosing optimal actions, and learning from experience to improve performance over time.
AI systems are categorized into three main types based on their capabilities. Narrow AI, also called Artificial Narrow Intelligence, represents current AI systems that excel at specific tasks like voice assistants, recommendation systems, and image recognition. These systems are highly specialized but cannot transfer their knowledge to other domains. General AI, or Artificial General Intelligence, represents the theoretical future where machines match human cognitive abilities across all domains, capable of learning, reasoning, and adapting like humans. Super AI goes beyond human intelligence, representing systems that could surpass human capabilities in every field. Currently, we are in the Narrow AI stage, with General and Super AI remaining theoretical concepts for the future.
AI Agents are autonomous entities that can perceive their environment and take actions to achieve specific goals. They have four key characteristics: autonomy, meaning they operate independently without constant human control; reactivity, allowing them to respond to changes in their environment; proactivity, enabling them to pursue goals and take initiative; and social ability, permitting interaction with other agents or humans. The agent-environment interaction cycle works through sensors that perceive environmental data, a processing unit that reasons about the information, and actuators that execute actions to modify the environment. Common examples include chatbots that interact with users, recommendation systems that suggest content, and autonomous vehicles that navigate roads.
AI Agent architecture consists of four main components working together. Sensors perceive the environment through cameras, microphones, temperature sensors, and GPS systems, collecting raw data about the surroundings. The Knowledge Base stores facts, rules, and accumulated experience that the agent can reference. The Inference Engine processes incoming data, queries the knowledge base, and performs reasoning to make decisions. Finally, Actuators execute actions through motors, speakers, displays, and control signals. Data flows from sensors to the inference engine, which queries the knowledge base for relevant information, then sends commands to actuators. This architecture enables autonomous decision-making in systems like self-driving cars, where sensors detect road conditions, the knowledge base contains traffic rules, the inference engine plans routes and actions, and actuators control steering, braking, and acceleration.
AI Agents are classified into five types based on increasing complexity and capabilities. Simple Reflex agents react directly to current perceptions using condition-action rules, like a thermostat that turns heating on when temperature drops. Model-based agents maintain an internal model of the world state, enabling them to handle partially observable environments, such as vacuum robots that map rooms. Goal-based agents pursue specific objectives through planning and search, like GPS navigators finding optimal routes. Utility-based agents optimize performance by evaluating different actions based on utility functions, such as trading bots maximizing profit while minimizing risk. Learning agents adapt and improve through experience, incorporating feedback to enhance future performance, exemplified by systems like AlphaGo that master complex games through self-play. Each type builds upon the previous, adding more sophisticated reasoning capabilities from simple stimulus-response to complex adaptive behavior.