Function Composition

Function composition, the process of combining simpler functions to create more complex ones, is a central theme in machine learning and artificial intelligence research, aiming to improve efficiency and generalization capabilities. Current research focuses on understanding how humans and machines learn and reason with compositional functions, exploring methods like chain-of-thought prompting and Bayesian optimization to improve the learning of these functions, particularly within deep neural networks and reinforcement learning frameworks. These advancements have implications for various applications, including dynamic pricing, improved reasoning in language models, and the development of more trustworthy and robust AI systems.