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A space to learn at your own pace

Your learning journey,
with Lyen.

New to AI? You belong here. Explore the basics, enjoy creating, and learn one step at a time.

Everyone is equal before AI

Your first step today

Understand. Create. Check.

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Not sure where to begin? Explore these steps in order.

A little example to get started

AI is a fill-in-the-blanks champion!

Here, we mean language models: the kind of AI you chat with in writing. Let's explore how they produce a reply using an everyday sentence.

What would you put in the blank? Tap an example.

This morning, I __________. This morning, I ate breakfast. This morning, I went for a walk. This morning, I read a book.

These choices are prepared examples. Tap your selected example again to bring back the blank.

Does AI really make replies this way?

Yes, predicting what comes next is the basic idea! A language model uses patterns learned during training, your input, and the text so far to calculate what could come next. It chooses a small piece of text called a token, which can be a word or part of one, then repeats the process to build a reply.

Filling a blank is an analogy. In a chat reply, the model usually keeps adding tokens at the end. It doesn't always choose the most likely one: it can also sample from several candidates according to their probabilities.

Then what does “thinking” or “reasoning” mean?

People use these words when AI works through a problem in steps. Underneath, it runs calculations using learned patterns and its input. Fluent language alone is no reason to treat it as a person with feelings or intentions.

A plausible continuation can still be wrong. Check important answers.

Explore how AI uses probability
What AI really is

Understand AI. Check its answers.

How a language model produces an answer

01

AI is not a living being.

A program designed by people builds sentences by calculating the probability of the next words (tokens), from patterns it learned and the current context.

02

Generation uses context and learned weights.

A neural language model learns numerical weights from training examples. At generation time, those weights and the available context determine scores for candidate tokens. The model does not simply look up a stored internet sentence or count only the word immediately before it.

03

Always remember this.

What you are reading is not the thought of a living being, but a result made of words likely to fit the context. Plausible does not mean true.

Discover the secret of probability
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