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Machine learning, deep learning, generative AI (LLM, prompt engineering, RAG, Agent) to AI applications, computing power and ethics governance
Use a structured knowledge map to build a holistic understanding of artificial intelligence
New to AI? Read in this order, from the basics upwards, to build a framework
Build a complete picture of artificial intelligence by starting from domain areas
從「會思考的機器」到今天的 AI，先建立整體輪廓與關鍵詞彙。
Browse →讓電腦從資料中「學規則」：監督、非監督與強化學習。
Browse →神經網路、CNN、RNN 到 Transformer——當代 AI 的引擎。
Browse →LLM、提示工程、RAG、Agent、擴散模型——本專區的核心。
Browse →視覺、語言、語音到教育與醫療，AI 如何走進真實場景。
Browse →GPU／TPU、訓練與推論成本，以及把模型變產品的 MLOps。
Browse →偏誤、隱私、幻覺、對齊與各國法規——AI 的責任與邊界。
Browse →Look up companies, compare tools, track major events, and connect knowledge back to the real world
Three-in-one query database: AI companies/labs (by stage and country), AI tools/products (by use), and a list of major models (by type and openness). See who is doing what.
Start query →Token, Embedding, RAG, Transformer, RLHF, hallucination... bilingual terminology and plain-language definitions; if an article or news item leaves you stuck, start here, then follow the related links to go deeper.
Search terms →From the Turing Test and Transformer to ChatGPT and AI regulations around the world: a timeline of key events that have reshaped the AI landscape, helping you understand the context behind the news.
View timeline →The module’s positioning, scope of knowledge coverage, and how to learn together with the Uedu AI TA (by asking AI-related questions directly in the conversation).
Find out more →This section contains original educational material intended to build foundational concepts and an overall perspective in artificial intelligence; it is not investment advice, nor the latest real-time information. AI technology and industry conditions change rapidly, so model and tool information is deliberately kept at the “family/institution” level; please check against official and authoritative sources before citing.