AI Foundations
Understand what AI is, how systems learn, where they fail, and how to judge claims without hype.
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30 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.
Topic tracks
Jump into the area you care about. Every track has multiple plain-English guides.
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AI ethics examines how AI development and use affect people, institutions, and the environment.
SamfunnAI can change the tasks inside jobs, the way work is organized, and demand for particular skills.
SamfunnAI security protects models, data, tools, and surrounding services from unauthorized access or manipulation.
SamfunnAI regulation is the set of legal requirements that can apply to developing, selling, or using AI systems.
SamfunnCommon AI myths confuse a system’s observable behavior with broader claims about knowledge, reliability, autonomy, or understanding.
SamfunnThe future of AI is uncertain and depends on technical progress, resources, policy, economics, and human choices.
SamfunnAI påvirker samfunnet gjennom institusjonene, tjenestene, informasjonssystemene og arbeidsplassene som tar den i bruk.
SamfunnModellutvinningsangrep lar en motstander klone en proprietær AI-modell bare ved å spørre etter dens offentlige API og trene en copycat på svarene.
SamfunnBelønningshacking er når en AI maksimerer belønningssignalet sitt på utilsiktede måter i stedet for å gjøre det designere faktisk ønsket.
SamfunnEt medlemskapsslutningsangrep prøver å finne ut om en spesifikk persons data ble brukt til å trene en modell, bare ved å sondere modellen.
SamfunnDataforgiftning ødelegger en modell ved å tukle med treningsdataene, og bakdørangrep skjuler en hemmelig trigger som får modellen til å oppføre seg dårlig på kommando.
SamfunnRask injeksjon er når skjulte eller ondsinnede instruksjoner kaprer et AI-system til å ignorere reglene og gjøre angriperens bud.
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