What characterizes deep learning in artificial intelligence?

Deep learning is characterized by utilizing multi-layered neural networks, which are essential for processing and learning from large sets of data in more complex ways compared to traditional machine learning techniques. These neural networks consist of several layers of interconnected nodes, or neurons, each responsible for transforming the input data into increasingly abstract representations. The depth of these networks allows them to capture intricate patterns and features from the data, enabling better performance in tasks such as image and speech recognition, natural language processing, and many others.

Unlike simple decision trees or linear regression models, which tend to work with more straightforward relationships between input and output, deep learning approaches can model highly complex non-linear relationships. This capability stems from the multiple processing layers that facilitate the learning of hierarchical feature representations, making deep learning particularly powerful in domains involving unstructured data, such as visual images or human language.

Understanding deep learning's reliance on multi-layered neural networks is crucial in AI, as it distinguishes this approach from other traditional machine learning methods that may not leverage the same depth or complexity in data processing.