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Artificial Intelligence (AI) is a branch of computer science that aims to create intelligent machines capable of performing tasks that typically require human intelligence. AI systems are designed to mimic certain aspects of human intelligence, such as learning from experience, reasoning, decision-making, and problem-solving.
AI can be classified into two main categories: Narrow AI (also known as weak AI) and General AI. Narrow AI systems are designed for a specific task or a set of tasks, such as speech recognition, image classification, or language translation. General AI, on the other hand, refers to systems that possess the ability to perform any intellectual task that a human being can do.
Machine Learning (ML) is a subfield of AI that focuses on developing algorithms and statistical models that enable computers to learn and make predictions or decisions without being explicitly programmed. ML algorithms can be divided into three types: supervised learning (using labeled data to train models), unsupervised learning (finding patterns in unlabeled data), and reinforcement learning.
Reinforcement learning is an aspect of machine learning that deals with how an agent can learn to make decisions in an environment to maximize rewards. It involves teaching an agent to take actions in an environment, observe the feedback (rewards or penalties) from those actions, and adjust its behavior accordingly.
In reinforcement learning, the agent learns by interacting with the environment through a trial-and-error approach. It aims to maximize cumulative rewards over time by discovering the optimal action or policy. The agent uses a reward signal to determine whether its actions are desirable or not.