01
AI Foundations
Understand what AI is, how systems learn, where they fail, and how to judge claims without hype.
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133 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.
02
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.
05
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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Visual AI
Video Understanding
Video understanding analyzes visual and sometimes audio information across time.
Visual AIMultimodal Search
Multimodal search retrieves information across forms such as text, images, audio, and video.
Visual AIDocument AI
Document AI extracts and interprets information from files such as forms, reports, invoices, and scanned pages.
Visual AIWasserstein GAN
Wasserstein GAN (WGAN) is a redesign of the GAN training objective that uses the Wasserstein distance instead of the original min-max loss.
Visual AIConditional GANs
Conditional GANs (cGANs) extend ordinary GANs by feeding extra information, like a class label or text, into both the generator and discriminator.
Visual AIPix2Pix Image-to-Image Translation
Pix2Pix is a conditional GAN that learns to translate one type of image into another, such as turning a sketch into a photo or a map into a satellite view.
Visual AIImage Colorization
Image colorization uses AI to add plausible, realistic color to black-and-white photos and film.
Visual AIStructure from Motion
Structure from Motion (SfM) reconstructs 3D scene geometry and camera positions from a set of overlapping 2D photos taken from different viewpoints.
Visual AIMulti-View Stereo
Multi-View Stereo (MVS) takes many calibrated photos of a scene and produces a dense 3D reconstruction by estimating depth at nearly every pixel.
Visual AIDDPM and DDIM Samplers
DDPM and DDIM are two ways to run the reverse process of a diffusion model, turning random noise into an image step by step.
Visual AIScore-Based Generative Models
Score-based generative models create data by learning the gradient of the data distribution — the direction that makes any noisy sample look more like real…
Visual AIFréchet Inception Distance
Fréchet Inception Distance (FID) is the standard metric for judging how realistic and varied a set of generated images is.
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