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.
AI helps newsrooms gather, write, fact-check, and distribute stories faster, but it also raises hard questions about accuracy, trust, and whose work gets…
IndustriesAI lets architects explore thousands of design options, optimize buildings for energy and cost, and turn rough sketches into renderings in seconds.
IndustriesAI helps construction teams predict delays, catch safety hazards, track progress from site photos, and coordinate complex builds.
ApplicationsAI slide generation turns a prompt, outline, or document into a formatted presentation deck in seconds.
ApplicationsAI browser automation lets a model see and control a web browser, clicking, typing, and navigating like a person to complete tasks.
ApplicationsAI data extraction pipelines turn messy, unstructured sources like PDFs, emails, and scanned forms into clean, structured data.
TechnicalExplainable AI (XAI) is the toolkit for turning a model's opaque prediction into a human-readable reason.
TechnicalMechanistic interpretability is the effort to reverse-engineer the internal computations of neural networks into human-understandable algorithms.
TechnicalAdversarial examples are inputs perturbed by tiny, often imperceptible changes that cause a model to make confident, wrong predictions.
ApplicationsAI test generation uses machine learning and large language models to automatically write software tests, freeing developers from tedious manual work.
ApplicationsAI demand forecasting predicts how much of a product or service customers will want, using machine learning to crunch sales history, prices, weather…
ApplicationsAI personalization engines tailor what each user sees, from product recommendations to homepage layouts, by learning individual tastes from behavior.
Check what you learned with topic quizzes, then explore our structured courses and current certification requirements. Core guides remain free to read.