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AI Foundations
Understand what AI is, how systems learn, where they fail, and how to judge claims without hype.
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84 plain-English guides, structured learning paths, and an open library — built by an independent 501(c)(3) nonprofit so anyone can understand modern AI.
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Five outcome-based courses
Each course includes explicit outcomes, mapped competencies, practice activities, and an applied capstone.
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Responsible AI User
Use AI productively while protecting privacy, checking outputs, and preserving human accountability.
03
AI at Work
Evaluate workplace use cases, run safe pilots, measure value, and communicate changes responsibly.
04
AI Policy & Society
Analyze AI systems through rights, equity, governance, safety, and public-interest outcomes.
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Building with AI Systems
Understand language models, retrieval, agents, evaluation, cost, and deployment safeguards through practical system design.
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FundamentalsCore concepts: how AI works at a high level.84 guides Language AILLMs, NLP, ChatGPT, prompting, and text understanding.155 guides Visual AIImage generation, computer vision, video, and design.133 guides Audio AIVoice, music, podcasting, and audio-first AI.117 guides CompaniesProfiles of OpenAI, Anthropic, Google, Meta, and more.121 guides ApplicationsHow AI shows up in coding, search, agents, and daily life.111 guides IndustriesAI in healthcare, finance, education, energy, and more.91 guides TechnicalChips, fine-tuning, RAG, vectors, and AI infrastructure.177 guides SocietyBias, privacy, copyright, safety, and the AI future.30 guides
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Fundamentals
Predictive AI
Predictive AI uses observed information to estimate an unknown outcome, such as demand, delivery time, or a category.
FundamentalsAI Systems Thinking
AI systems thinking examines how data, models, people, interfaces, and operating policies interact.
FundamentalsModel Lifecycle
The model lifecycle covers problem definition, data preparation, training or selection, evaluation, deployment, monitoring, and retirement.
FundamentalsAI Evaluation Basics
AI evaluation tests whether a system meets a defined purpose under stated conditions.
FundamentalsHuman-AI Collaboration
Human-AI collaboration divides work between people and AI systems while keeping responsibility and control clear.
FundamentalsAI Decision-Making
AI can supply predictions, organize evidence, or recommend actions, but choosing an action also requires goals, constraints, and responsibility.
FundamentalsAI Failure Modes
An AI failure mode is a repeatable way a system can produce an unacceptable result.
FundamentalsGroup Normalization
Group Normalization is a technique that stabilizes neural network training by normalizing features within small groups of channels, independently for each…
FundamentalsGated Recurrent Units
A Gated Recurrent Unit (GRU) is a streamlined type of recurrent neural network cell that uses two gates to decide what information to keep and what to forget…
FundamentalsWeight Decay and L2 Regularization
Weight decay is a simple, powerful technique that nudges a model's weights toward zero during training, discouraging it from relying too heavily on any…
FundamentalsDropout and Stochastic Regularization
Dropout is a regularization trick that randomly switches off a fraction of neurons during each training step, forcing the network to build redundant, robust…
FundamentalsEarly Stopping
Early stopping is a regularization technique that halts model training the moment performance on held-out validation data stops improving.
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