# Algorithms Underlying AI

AI's capabilities rest on a family of algorithms from symbolic search to neural optimization. This sub-article expands [Section 3 of Hardware & Software](../hardware-software.md).

## Symbolic Methods
- **Search** (A\*, heuristic search, constraint solving).
- **Logic programming** and **planning** (STRIPS-style).

## Classical Machine Learning
- Linear models, decision trees, support vector machines, boosting, clustering, Bayesian inference.

## Neural Architectures
- **CNNs** for vision.
- **RNNs/LSTMs** for sequences (largely superseded by Transformers).
- **Transformers** using attention for language, vision, and multimodal data.
- **Diffusion models** for generative image/video.
- **Reinforcement learning** for decision-making and control.

## Optimization
- Gradient descent and variants (Adam, SGD).
- Evolutionary algorithms and Bayesian optimization for black-box tuning.

## Why It Matters
The algorithm determines what a system can learn and how. Paradigms shift as data and hardware change.

## Sources
- Russell, S. & Norvig, P. (2021). *Artificial Intelligence: A Modern Approach.*
- Goodfellow, I. et al. (2016). *Deep Learning.*

*Back to [Hardware & Software](../hardware-software.md).*