FC AI: AI explained, tools, work and policy

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How Large Language Models Actually Work, in Plain English

Chatbots feel like they think, but underneath they do one thing very well: predict the next piece of text. This explainer walks through tokens, training, transformers, fine-tuning and context windows, and shows why that design explains both the impressive answers and the confident mistakes.

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Explainers, in order

New to AI? Read these first. How it actually works, in plain English.

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  1. 01AI Bias and Fairness Explained: Where It Comes From and How It's FixedAI systems can treat people unequally even when nobody intends it. This explainer covers where bias enters, from skewed data to flawed goals, real US examples in facial recognition, hiring and healthcare, why 'fair' has several competing definitions, and what organizations and individuals can actually do about it.6 min read
  2. 02How Large Language Models Actually Work, in Plain EnglishChatbots feel like they think, but underneath they do one thing very well: predict the next piece of text. This explainer walks through tokens, training, transformers, fine-tuning and context windows, and shows why that design explains both the impressive answers and the confident mistakes.7 min read
  3. 03AI vs. Machine Learning vs. Deep Learning vs. Generative AI, ExplainedAI, machine learning, deep learning, neural networks and generative AI are often used as if they mean the same thing. They don't. This guide untangles the terms, shows how they nest inside one another and explains which kind of AI is behind the tools Americans use every day.6 min read
  4. 04Why AI Chatbots Make Things Up, and How to Catch It Before It Costs YouAI hallucinations are not random glitches. They follow predictably from how language models are built, which means you can anticipate when they are likely, recognize the warning signs and set up simple checks that catch invented facts, fake citations and confident errors.6 min read

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