AI Foundations
Understand what AI is, how systems learn, where they fail, and how to judge claims without hype.
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155 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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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.
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Conversational AI is technology that lets people interact with computers through natural back-and-forth dialogue, by text or voice, instead of menus…
Language AICoreference resolution is the task of figuring out when different words in a text refer to the same thing, such as linking "she" or "the CEO" back to "Maria.
Language AIText embeddings turn words, sentences, or documents into lists of numbers (vectors) that capture meaning, so that texts with similar meanings end up close…
Language AISliding window attention restricts each token to attend only to a fixed-size neighborhood of nearby tokens instead of the entire sequence.
Language AIByte-Pair Encoding (BPE) is a compression-inspired algorithm that builds a vocabulary by repeatedly merging the most frequent pair of symbols.
Language AISubword tokenization splits text into units smaller than words but larger than characters, like 'token' plus 'ization'.
Language AIFlashAttention is a memory-efficient algorithm that computes the exact same attention as standard transformers but without ever writing the giant attention…
Language AIGrouped-Query Attention (GQA) is a way to shrink the memory needed during text generation by letting several query heads share the same key and value heads.
Language AIRotary Position Embeddings (RoPE) encode where each token sits in a sequence by rotating its query and key vectors by an angle proportional to position.
Language AIPrefix tuning is a parameter-efficient way to adapt a frozen language model by training a small set of continuous vectors that get prepended to every layer's…
Language AIPrompt tuning adapts a frozen language model by learning a handful of continuous 'soft prompt' vectors prepended to the input, rather than writing words…
Language AIRetrieval reranking is the second stage of modern search: after a fast retriever pulls a candidate set, a more powerful model re-scores those candidates so…
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