AI Foundations
Understand what AI is, how systems learn, where they fail, and how to judge claims without hype.
Free AI library
1019 plain-English guides, structured learning paths, and an open library — built by an independent 501(c)(3) nonprofit so anyone can understand modern AI.
Start here
Each course includes explicit outcomes, mapped competencies, practice activities, and an applied capstone.
Understand what AI is, how systems learn, where they fail, and how to judge claims without hype.
Use AI productively while protecting privacy, checking outputs, and preserving human accountability.
Evaluate workplace use cases, run safe pilots, measure value, and communicate changes responsibly.
Analyze AI systems through rights, equity, governance, safety, and public-interest outcomes.
Understand language models, retrieval, agents, evaluation, cost, and deployment safeguards through practical system design.
Topic tracks
Jump into the area you care about. Every track has multiple plain-English guides.
Full library
1019 of 1019 guides shown. Filter by track or search above.
BM25 is the classic keyword-based ranking function that scores documents by how often query terms appear, adjusted for term rarity and document length.
Language AIHyDE improves retrieval by first asking a language model to imagine a fake answer document, then searching with that document's embedding instead of the raw…
ApplicationsVoice biometrics uses AI to verify your identity from the unique acoustic and behavioral patterns in your speech.
ApplicationsAI turns spoken audio into synchronized on-screen text, automating subtitles for translation and closed captions for accessibility.
ApplicationsAI helps inventors, lawyers, and examiners search millions of patents and analyze them by meaning rather than just keywords.
Language AISpeculative decoding uses a small, fast 'draft' model to guess several upcoming tokens that a large model then verifies in one pass.
Language AILookahead decoding speeds up LLM generation without any extra draft model by guessing and verifying multiple future tokens in parallel using n-grams…
Language AIColBERT is a retrieval model that represents each query and document as many token-level vectors and scores them with a fine-grained 'late interaction' step.
TechnicalWhen training deep networks, error signals shrink toward zero or blow up toward infinity as they travel backward through many layers.
TechnicalA simple, widely used safeguard that caps how large gradient updates can get during training.
TechnicalHow you set a neural network's starting weights before training begins, which strongly shapes whether signals and gradients stay healthy through deep layers.
Visual AIPerceptual loss measures how similar two images look to humans by comparing deep neural network features instead of raw pixels.
Check what you learned with topic quizzes, then explore our structured courses and current certification requirements. Core guides remain free to read.