Computer Science – Automated and emerging technologies | e-Consult

AI systems learn through a process called machine learning. This involves feeding the system large amounts of training data. Training data is information used to teach the AI system to identify patterns and make predictions.

At the heart of AI learning are algorithms. An algorithm is a set of instructions that the AI system follows to analyze the training data. Different types of algorithms exist, such as supervised learning, unsupervised learning, and reinforcement learning.

Supervised learning involves providing the AI system with labeled data – data where the correct output is already known. The algorithm learns to map inputs to outputs. For example, showing the system images of cats and dogs, labeled as "cat" or "dog".

Unsupervised learning deals with unlabeled data. The algorithm tries to find patterns and structures within the data on its own. For example, clustering customers based on their purchasing behavior.

Reinforcement learning involves an agent learning to make decisions in an environment to maximize a reward. The agent receives feedback (rewards or penalties) for its actions and learns to choose actions that lead to the highest cumulative reward. Think of training a robot to navigate a maze.

The algorithm adjusts its internal parameters based on the training data, iteratively improving its ability to make accurate predictions or decisions. This process continues until the AI system reaches a desired level of accuracy.