Section
How AI actually works, in plain English.
5 stories
Type a sentence and an AI tool paints a picture in seconds. Behind that trick is a process that learns to turn random noise into images, guided by your words. Here is how diffusion models are trained, what happens when you hit generate, why they still stumble on hands and text, and what the settings actually do.
AI 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.
AI, 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.
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.
AI 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.
