Artificial Intelligence learns by processing vast amounts of data and identifying patterns within it. Using techniques like machine learning and deep learning, AI systems analyze examples and use them to make predictions or decisions without being explicitly programmed for every task. For instance, an AI trained on thousands of images of cats can learn to recognize a cat in a new photo by finding common features like shape and color.

This learning process allows AI to improve over time as it receives more data and feedback. However, the quality and diversity of data are crucial because biased or incomplete data can lead to flawed decisions. Understanding how AI learns helps us appreciate its strengths and limitations, ensuring that these systems are designed responsibly to support accurate, fair, and reliable outcomes across various applications. Shutdown123