Systems can be designed to be learnable, meaning they can adapt and improve their performance over time based on feedback or new information. For example, machine learning algorithms are learnable systems. These algorithms improve performance on specific tasks by analyzing data and adjusting their internal parameters to better fit the data.

Neural networks represent another example of learnable systems—machine learning algorithms inspired by the structure and function of the human brain. These networks learn to perform tasks such as classification and prediction by adjusting the weights and biases of their interconnected nodes (or "neurons") based on input data.

In general, learnable systems improve their performance through trial and error by analyzing data and adjusting internal parameters accordingly. This approach proves valuable in various applications, including image and speech recognition, natural language processing, and predictive analytics.