AI systems require large amounts of data to function effectively. Data serves as the foundation for training AI models, enabling them to recognize patterns, make predictions, and perform tasks. In fields such as healthcare, AI can analyze medical records and imaging data to assist in diagnosing diseases and recommending treatments. The availability of big data has revolutionized AI, allowing for more sophisticated models and applications. However, the quality and diversity of the data are critical, as biased or incomplete data can lead to inaccurate and unfair outcomes.

AI systems require large amounts of data to function effectively. Data serves as the foundation for training AI models, enabling them to recognize patterns, make predictions, and perform tasks. In fields such as healthcare, AI can analyze medical records and imaging data to assist in diagnosing diseases and recommending treatments. The availability of big data has revolutionized AI, allowing for more sophisticated models and applications. However, the quality and diversity of the data are critical, as biased or incomplete data can lead to inaccurate and unfair outcomes.

Machine learning, a subset of AI, enables systems to learn from data and improve over time without being explicitly programmed. This is achieved through algorithms that identify patterns and make decisions based on the data they are fed. For instance, recommendation engines on platforms like Netflix and Amazon use machine learning to analyze user behavior and suggest content or products tailored to individual preferences. As more data is collected, these systems become more accurate and effective, continually refining their predictions and enhancing user experience.

Machine learning, a subset of AI, enables systems to learn from data and improve over time without being explicitly programmed. This is achieved through algorithms that identify patterns and make decisions based on the data they are fed. For instance, recommendation engines on platforms like Netflix and Amazon use machine learning to analyze user behavior and suggest content or products tailored to individual preferences. As more data is collected, these systems become more accurate and effective, continually refining their predictions and enhancing user experience.