Both machines and humans improve their performance through experience. Machines use data and past examples to adjust their algorithms, while humans learn through practice, trial and error, and adapting from past experiences. Although these mechanisms of gathering information are different, the ways in which they use this information are the same. 

Learning in both machines and humans is often guided by feedback. Machines rely on feedback, such as error rates or rewards in reinforcement learning, to fine-tune their models. Humans similarly learn through reinforcement—positive outcomes encourage repetition, while negative outcomes discourage it. 

Both humans and machines excel at recognizing patterns. Machines use algorithms to detect patterns in data, enabling them to make predictions or decisions. Similarly, humans use cognitive processes to recognize patterns in their environment, which helps with problem-solving, learning languages, and making sense of new information.