Traditional AI used symbolic, rule-based approaches that couldn’t handle perception tasks (image recognition, speech) or learn from examples. There was no system that could learn patterns from data the way biological brains do.
Neural networks are computing systems inspired by biological neurons. They learn by adjusting connection weights between layers of artificial neurons, enabling pattern recognition, classification, and generation tasks.
A neural network consists of:
- Input layer — receives raw data (pixels, words, features)
- Hidden layers — transform data through weighted connections and activation functions
- Output layer — produces predictions or generated content
Training uses backpropagation: forward pass computes output, loss function measures error, backward pass adjusts weights via gradient descent.
Modern networks like Transformers, CNNs, and Diffusion models are all neural networks with specialized architectures.
- Universal approximation — can approximate any continuous function given enough neurons
- Learning from examples — no need for hand-coded rules
- Parallel processing — many computations happen simultaneously
- Generalization — can make predictions on unseen data
- Built from: Transformers — specific neural network architecture for sequence modeling
- Built from: Diffusion Models — neural networks trained to reverse noise processes
- Contrasts with: Symbolic AI — rule-based vs learning-based approaches
- Related: CLIP Encoder — neural network for text-image alignment
- Related: T5 Encoder — transformer-based neural network for text encoding
- Related: LoRA Fine-tuning — efficient neural network adaptation technique
- Overfitting — memorizing training data instead of learning general patterns
- Black box — hard to interpret why a neural network makes a specific decision
- Data hungry — need large datasets and significant compute to train effectively
- Adversarial examples — small perturbations can fool networks into wrong predictions