AI systems work by processing large amounts of data and using algorithms to learn patterns and relationships in that data. These algorithms can be supervised, unsupervised, or a combination of both.
Supervised learning involves providing the AI system with labeled examples of input and output data, allowing it to learn to recognize patterns and make predictions based on that data. For example, an AI system might be trained to recognize images of cats by being shown thousands of labeled images of cats and non-cats.
Unsupervised learning, on the other hand, involves feeding unlabelled data into the system and letting it find patterns and relationships on its own. Clustering algorithms, for example, can be used to group together similar data points.
Once an AI system has been trained on a dataset, it can be used to make predictions or decisions based on new data that it has not seen before. This is known as inference or prediction.
AI systems can also be designed to learn from their mistakes and improve their performance over time. This is known as reinforcement learning, and it involves the system receiving feedback in the form of rewards or penalties based on its actions.
Overall, the specific algorithms and techniques used by AI systems depend on the task they are designed to perform, but the general approach involves processing data and learning from it to make predictions or decisions.