Neural networks are a key component of AI, modeled after the human brain’s structure. These networks consist of layers of nodes or “neurons” that process information in a hierarchical fashion. The deeper the network (i.e., the more layers), the more complex data it can process, which is why deep learning has become a major subset of AI.
Neural networks are used in various AI applications, including facial recognition, natural language processing, and autonomous vehicles. For example, when you upload a photo to Facebook, neural networks help identify and tag people in the image. In healthcare, neural networks are used to detect anomalies in medical images, helping doctors diagnose diseases early.
The primary advantage of neural networks is their ability to learn and improve over time, but they require vast amounts of data to function effectively. They are also “black boxes,” meaning it’s difficult to understand how they make decisions, which has led to concerns about transparency and accountability in AI systems.