Artificial intelligence sometimes begins with a school letter, a wet field, or the sound of an engine.
«Zwischen Alltag und Algorithmus» tells stories about people who did not arrive at AI through the straight computer-science route. A nurse checks false alarms. A former shop owner hears customer conversations differently from a chatbot. A homemaker discovers that translation tools only help once someone knows where they fail. The characters are fictional, but their questions are taken from ordinary life.
All protagonists are editorial composite characters. No story claims private facts about real people.
§ 01 · What this is about
Paths into AI that rarely appear on conference slides.
People arrive at learning systems through care work, craft, school, migration, insolvency, farming, art, translation, journalism, or retirement. They bring forms of knowledge that often stay quiet in technical conversations: shift handovers, workshop noises, dialects, official letters, customer frustration, soil moisture, stage light.
The thread is simple. Someone runs into a concrete problem. Then the work begins. Not neatly, and not with immediate success. A model sounds too certain. A spreadsheet has been poorly maintained. An image generator produces patterns that look beautiful and cannot be used. A health chatbot talks for too long when a person needs help. These are the moments where the stories become interesting.
§ 02 · No stars, no polished careers
Invented composite figures bundling plausible experiences.
The series avoids celebrity biographies. It does not rebuild the lives of well-known researchers, founders, or public AI voices. Instead, it works with invented composite figures: a retailer after bankruptcy, a taxi driver with local street knowledge, a student with little money, a retired teacher, a construction worker with a nose for danger.
That is not a trick meant to imitate authenticity. It is an editorial choice. It allows us to show typical pathways without appropriating real people or inventing verifiable life details. The stories are meant to carry the texture of reality, but they are not case files.
§ 03 · What AI is allowed to do in these portraits
Not a box of wonders. An object of testing.
In this series, AI sorts, suggests, translates, marks, compares, warns, and condenses. Sometimes it helps. Sometimes it gets things wrong. A good system shows its uncertainty and leaves room for the moment when a human has to decide.
That is why the portraits focus not only on tools, but on situations: a bank branch where a risky transfer is checked; a depot where old photographs have been labelled badly; a construction site where dust confuses a camera model; a kitchen table where a school letter is written in a way that makes people hesitate to ask. The technology is viewed where it meets responsibility.
§ 04 · Ways into the series
Which portraits to read first.
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For people who want to learn AI
Start with the stories about school, study, parent work, senior education, and accessible learning tools. They show that an entry point does not require perfect prior knowledge.
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For small businesses and self-employed people
Read the portraits about stock levels, gastronomy, customer service, planning, and data quality. They make visible why simple workflows often matter more than large promises.
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For social, cultural, and public-sector work
The paths through care, counselling, journalism, archives, art, and local initiatives show why language, rights, sources, consent, and human handover remain important.
Have you seen a path into AI that does not fit a career template?
We are not collecting perfect success stories. We are interested in breaks, detours, failed attempts, and small improvements that hold up in practice. Write to us if an experience from everyday life, work, or education could belong in this series.
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