Optimization in the context of artificial intelligence refers to the process of fine-tuning the parameters of a model to minimize a loss function, which quantifies how well the model's predictions match the actual outcomes. The objective is to find the optimal set of parameters that leads to the best possible performance on the given task. Various optimization algorithms are employed in machine learning, including Gradient Descent, Adam, and RMSprop, each with its advantages and limitations. The choice of optimization technique can significantly affect the convergence speed and stability of the training process. Successful optimization is critical for training deep learning models effectively, allowing them to learn complex patterns from data.