Tagged
#data
5 articles
Privacy and LLMs: what leaves your machine
When you type into an LLM, where does that text actually go — and what happens to it after? A plain-language guide to the data trail.
Data licensing: the real constraint behind AI products
The hardest part of many AI products is not the model — it is whether you are allowed to use the data at all. A plain-language tour of the constraint that quietly decides what gets built.
AI and your data: what training on your inputs means
When a service says it may train on your inputs, what does that actually mean for your text, files, and ideas? A plain-language guide to the trade.
Synthetic data: training models on model output
When real data runs short, models can generate their own training data. It is powerful, slightly circular, and dangerous if you forget where it came from.
Bias in AI, explained without the hype
Bias in AI is neither a myth nor a moral failing of machines. It is a predictable result of how these systems learn. Here is the calm version.