AI Wiki
A collection (WIP) of explainers for AI concepts that goes beyond machine learning.
Intelligence
Understanding definitions for the "intelligence" in "artificial intelligence", withing the context of modern AI.
Agents & AI
A simple model for understanding agents and the role of artificial intelligence.
Environments
Categories for various environments that influence how we design agent systems.
Intro to Search
Explaining search and how to frame problems like route navigation and puzzle-solving as search problems.
Local Search
A simple, memory-efficient search method that chooses actions based on neighbors.
Intro to Optimization
Explaining how optimization problems arise from search problems where the goal states cannot be specified explicitly.
Solving Optimization Problems
An introduction to solving optimization problems using search.
Gradient Descent & Hill Climbing
Optimization algorithms that leverage local search to find the minimum or maximum of an objective function using greedy methods.
Global vs Local Optimums
Gradient descent & hill climbing can fail at optimization problems by getting stuck in local minima or maxima, preventing them from finding global minima or maxima.
Stochastic Gradient Descent & Hill Climbing
Variants of gradient descent and hill climbing that help overcome local optima.
Local Beam Search
A generalization of gradient descent and hill climbing that uses more agent memory to improve the odds of finding a global optimum.