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ahead · AI for beginners

Just getting started?

In the next 5 minutes you will understand what artificial intelligence really is, and in 30 days you will know how to use it every day. No buzzwords, no panic.

Built for people who do not work in IT. No math. No AGI panic. Plain talk.

Your learning path

12 terms, in the right order.

Click on a term, the explanation opens right here on the page. Once you have read all 12, a short knowledge quiz appears.

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  1. 1

    Artificial Intelligence

    What even is it?

    Artificial Intelligence is the ability of computers to solve tasks that normally require human thinking, such as recognizing images, understanding language, or finding patterns. It learns from examples instead of following fixed instructions.

    Open the full entry in the glossary →

  2. 2

    Algorithm

    The blueprint behind it

    An algorithm is a step-by-step set of instructions for the computer on how to solve a problem. Just as a cooking recipe has steps, an algorithm has precisely defined instructions. AI uses many such sets of instructions to carry out tasks.

    Open the full entry in the glossary →

  3. 3

    Data

    The raw material of AI

    Data is all the information a computer can store and process: text, numbers, images, sounds, videos. AI needs data in order to learn. The more and the better the data, the better it can solve tasks.

    Open the full entry in the glossary →

  4. 4

    Model

    What comes out in the end

    An AI model is the result of the learning process: a program that has learned rules from many examples and can now make predictions on its own. You can picture it like a trained expert, except that it is software.

    Open the full entry in the glossary →

  5. 5

    Training

    How an AI learns

    Training is the name for an AI's learning process: you show it lots of examples, and step by step it adjusts until it masters the task well. It is similar to practicing for an exam, just with millions of examples.

    Open the full entry in the glossary →

  6. 6

    Machine Learning Machine Learning (Maschinelles Lernen)

    The broader term

    Machine Learning means a computer is not programmed directly; instead it receives many examples and learns the rules by itself. The more and the more varied the examples, the better the predictions. This is the core of modern AI.

    Open the full entry in the glossary →

  7. 7

    Generative AI

    AI that creates new things

    Generative AI creates new content on its own: text, images, music, videos, or code. It has learned from many examples and combines what it learned into something new. ChatGPT and Midjourney are part of this.

    Open the full entry in the glossary →

  8. 8

    Large Language Model LLM, transformerbasiertes Sprachmodell

    LLMs like ChatGPT

    A large language model is a computer program that was trained on an incredible amount of text, books, websites, conversations. As a result it has learned how language works and can answer questions, write texts, or translate. ChatGPT is one such model.

    Open the full entry in the glossary →

  9. 9

    Prompt

    How you talk to AI

    A prompt is the instruction or question you give to a language model. How good the answer turns out depends greatly on how clear and precise your prompt is. Writing good prompts is a skill in its own right.

    Open the full entry in the glossary →

  10. 10

    Hallucination

    When AI makes things up

    A hallucination is a made-up or false answer from an AI that sounds plausible. The model has no source for it, but rather invented the information. For important topics you should therefore always check the answers.

    Open the full entry in the glossary →

  11. 11

    Bias Bias (Verzerrung)

    When AI becomes unfair

    Bias means the AI systematically makes unfair decisions, for example because its training data was one-sided. The result: certain groups are disadvantaged. Recognizing and correcting bias is a core task of responsible AI development.

    Open the full entry in the glossary →

  12. 12

    Data Protection Data Protection / Privacy

    What you should keep in mind

    Data protection means safeguarding people's personal data against unwanted storage, sharing or analysis. AI systems often process a lot of data and must strictly follow data protection rules. In Europe this is governed by the DSGVO (GDPR).

    Open the full entry in the glossary →

10 myths, briefly answered

What you often hear, and what is really true.

A lot is going around about AI. Here are the ten most common misconceptions, fact-checked.

  • Myth 01

    „AI thinks like a human."

    Plain talk

    No. AI calculates probabilities based on patterns from past data. It has no consciousness, no emotions, no intentions, even if it sometimes seems that way.

  • Myth 02

    „AI really understands my question."

    Plain talk

    Not in a human sense. AI recognizes patterns in your words and generates a plausible answer. Meaning arises statistically, not through understanding.

  • Myth 03

    „AI is objective."

    Plain talk

    False. AI learns from data created by people, and thereby inherits human prejudices and distortions. Responsibility and review remain necessary.

  • Myth 04

    „AI will replace my job."

    Plain talk

    Rarely completely, often in part. AI takes over repetitive tasks. Creativity, empathy, complex decisions and responsibility remain human.

  • Myth 05

    „AI is new."

    Plain talk

    The concepts have existed since the 1950s. What is new today: lots of data, lots of computing power and the breakthrough in language models since 2017 (transformer architecture).

  • Myth 06

    „AI knows everything."

    Plain talk

    AI only knows what was in the training material up to a cutoff date. Current, personal or rare knowledge is often missing. So: check sources on important topics.

  • Myth 07

    „AI lies to me."

    Plain talk

    Not on purpose. AI hallucinates: it states plausible-sounding falsehoods without knowing it. That means: read critically, ask for sources, cross-check.

  • Myth 08

    „AI is dangerous."

    Plain talk

    Like any powerful tool, context decides. Responsibility lies in how it is used, not in the technology. Clear rules, transparency and oversight reduce risks.

  • Myth 09

    „AI keeps learning through daily use."

    Plain talk

    No. Finished models are frozen. Learning happens only during training, at the maker company, not at your end. Your inputs may, however, be used for future training.

  • Myth 10

    „AI costs a lot of money."

    Plain talk

    Many tools can be used for free (ChatGPT Free, Claude Free, Perplexity, Copilot in Word). Premium versions usually start at around €20 per month, no more expensive than a streaming subscription.

Practice over theory

My first AI day.

This is what a day looks like when AI helps you concretely, from the first email in the morning to the bedtime story in the evening.

  1. 07

    07:00

    Sort emails

    Outlook / Gmail

    Both have built-in AI sorting, important emails come first.

  2. 09

    09:00

    Summarize a meeting

    Microsoft Copilot

    Start the recording, the AI produces minutes with a task list at the end.

  3. 11

    11:00

    Write a report

    ChatGPT / Claude

    Put in bullet points, the AI drafts it, you finalize and review.

  4. 13

    13:00

    Sort a photo album

    Apple / Google Fotos

    The AI sorts by people, places, occasions, automatically in the background.

  5. 15

    15:00

    Plan a trip

    Perplexity

    Ask for current recommendations, the answer comes with source links.

  6. 18

    18:00

    Learn a language

    Duolingo / Lingvist

    The AI adapts the exercises to your level, shorter but more on target.

  7. 20

    20:00

    Bedtime story

    ChatGPT

    Put in a favorite animal + 3 keywords, a new story every evening.

5 questions, 1 recommendation

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Question 1 / 5

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