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.
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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.
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.
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.
How a language model produces an answer
A program designed by people builds sentences by calculating the probability of the next words (tokens), from patterns it learned and the current context.
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.
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.
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