In AI, what is a neural network?

A neural network is a computational model that draws inspiration from the way the human brain operates. This model consists of interconnected layers of nodes or 'neurons,' which process input data through weighted connections. Each input is transformed, passed through the network, and ultimately results in an output. The structure of a neural network allows it to learn complex patterns and relationships in data, mimicking the way biological brains learn from experience.

In contrast, the first option incorrectly implies that neural networks are limited to processing only linear data. While some algorithms exist for linear modeling, neural networks excel at capturing both linear and non-linear relationships due to their architecture.

The third choice presents a simple database as a comparison, which does not accurately reflect the functionalities of a neural network. Databases primarily store data without the processing capabilities inherent in neural networks, which analyze and learn from data.

Lastly, the option suggesting that a neural network is a network of separate AI systems misrepresents what a neural network is. While AI systems can be interconnected, a neural network functions as a singular system that operates on data inputs as a cohesive model, rather than as separate distinct entities interacting with one another.