Artificial Intelligence represents a fundamental shift from traditional programming. Instead of writing explicit rules and instructions, AI systems learn patterns from data. Traditional programming follows a simple formula: input plus rules equals output. But AI flips this concept - we provide input and desired output, and the system learns the rules. This learning happens through neural networks, which process information through interconnected layers of nodes, mimicking how our brain processes information. The key insight is that AI discovers patterns we might never have thought to program explicitly.
Neural networks are the backbone of modern AI, structured in layers that process information hierarchically. The input layer receives raw data, like pixel values from an image. Hidden layers extract increasingly complex features - first detecting edges, then shapes, then objects. The output layer makes final predictions. Each neuron performs a simple calculation: it multiplies inputs by weights, adds a bias, and applies an activation function. These weights are learned during training, determining how much influence each input has. The network's power comes from combining millions of these simple calculations across multiple layers.
The learning process is where AI truly becomes intelligent. During training, the network makes predictions through a forward pass, then calculates how wrong it was using a loss function. The magic happens in backpropagation - the network traces back through its layers, calculating how each weight contributed to the error. Gradient descent then adjusts these weights in the direction that reduces error most effectively. This process repeats thousands of times across many epochs, with the network gradually climbing down the loss landscape toward optimal performance. Each iteration brings the AI closer to understanding the underlying patterns in the data.