AI Foundations
Understand what AI is, how systems learn, where they fail, and how to judge claims without hype.
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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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A large language model (LLM) is a model trained to work with patterns in language, often by predicting tokens from context.
Language AINatural language processing, or NLP, is the study and engineering of systems that work with human language.
Language AIA transformer is a neural-network architecture that uses attention to combine information across a sequence.
Language AIPrompt engineering is the practice of designing and testing instructions and context for an AI model.
Language AIAI writing tools generate or revise text from instructions and context.
Language AIAI translation converts meaning between languages using a learned model.
Language AIGPT stands for generative pretrained transformer. The early GPT research sequence explored language-model pretraining, broader task transfer, and learning…
Language AIText-to-speech, or TTS, turns written text into spoken audio.
Language AISpeech-to-text systems convert spoken audio into a written transcript.
Language AISentiment analysis estimates the attitude expressed in text, often using labels such as positive, negative, or neutral.
Language AIMachine translation is automated conversion from a source language to a target language.
Language AINamed entity recognition, or NER, identifies spans of text that refer to categories such as people, organizations, and places.
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