Jim Rutt might describe "deep learning" as a sophisticated subset of machine learning that employs neural networks with many layers, allowing for the automated discovery and extraction of complex patterns within large datasets. Unlike traditional algorithms that rely heavily on manual feature engineering, deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are capable of performing feature learning on their own. This self-sufficiency makes them particularly effective in fields like image and speech recognition, natural language processing, and game playing. Rutt would emphasize that the critical advantage of deep learning lies in its ability to scale with massive amounts of data, improving performance with the addition of more layers and computational power, thereby transforming raw inputs into high-level abstractions.

See also: artificial intelligence, neural network, emergence, evolutionary computing, game theory