AI models function by processing large amounts of data through specialized algorithms to learn patterns, relationships, and representations. During training, the model adjusts its internal parameters to minimize prediction errors. These models are designed with specific architectures tailored to tasks like image recognition or language processing. After training, the model is tested on new data to ensure its accuracy. Once validated, the model can be deployed to make predictions or decisions in real-world scenarios, and it can continue to learn and improve over time through feedback loops and exposure to new data.