Artificial Intelligence
Agents, search, games, optimisation, probability, language models, attention and image generation.
14 animated lectures · 41 minutes
▶ Start with lecture 1What Is Artificial Intelligence?
A clear, visual introduction: what AI is, how modern AI learns from data, and how AI, machine learning and deep learning fit together.
A Short History of AI
From Alan Turing to ChatGPT in one animated timeline — the breakthroughs, the “AI winters” and the ideas that changed everything.
Intelligent Agents: Perceive, Decide, Act
Watch a robot vacuum perceive its world, decide and act — the agent loop that underlies everything from thermostats to AI assistants.
Breadth-First vs Depth-First Search
Watch two classic search algorithms explore the same maze — one in ripples, one in deep dives — and see why only one guarantees the shortest path.
A* Search: Smarter Pathfinding
A* combines the cost so far with an estimate of the cost remaining. Watch it head for the goal and find the same shortest path while exploring fewer cells.
Minimax and Alpha–Beta Pruning
How game-playing AI thinks ahead: MAX and MIN take turns, values flow up the tree, and alpha–beta pruning skips branches that cannot matter.
Hill Climbing and Simulated Annealing
Local search climbs towards better solutions — but gets stuck on local peaks. Simulated annealing adds controlled randomness to escape.
Genetic Algorithms: Evolution in Code
Selection, crossover and mutation: watch a population of bit strings evolve towards a perfect solution, generation by generation.
Bayes’ Theorem: Reasoning Under Uncertainty
A positive medical test that is “90% accurate” — so why is the chance of being sick only about 32%? Bayes’ theorem explained with 200 people.
How Large Language Models Work
ChatGPT, Claude and friends predict the next token over and over. Watch a model choose words from probabilities, one token at a time.
Attention and Transformers
Self-attention lets every word look at every other word. See how “it” finds “animal”, and how attention matrices power the Transformer.
How AI Image Generators Work
Diffusion models turn pure noise into a picture by removing a little noise at a time, guided by your text prompt.
Search and Problem Solving: A Deep Dive
How AI systems find solutions: state spaces, breadth-first, depth-first, uniform-cost, greedy and A* search, admissible heuristics, local search, simulated annealing, genetic algorithms and constraint satisfaction.
Game-Playing AI: From Minimax to AlphaZero
How machines learned to beat world champions: game trees, minimax, evaluation functions, alpha–beta pruning, Deep Blue, Monte Carlo tree search, UCT, and AlphaGo and AlphaZero’s marriage of search and learning.