AI + text · large language models
We just built a machine that guesses a salary. Point it at language and it does one thing: guess the next word.
First, a snag
A photo’s easy — its pixels are already numbers. Words aren’t. So step one: chop the sentence into chunks — tokens — and swap each for a number.
“the cat sat on the …” → a row of numbers. Same chunk, same number, every time.
Then — the exact same machine
Now do it across everything
A whole life of reading ≈ 8 billion words.
The biggest models train on ≈ 8 trillion words a month.
“It’s just autocomplete…”
To fill in that one word, it has to follow the whole story. Guessing the next word well is understanding.
One more step
Raw, it just carries on the text — sci-fi, but worse. A second round of training turns it into something helpful.
base model → instruct model. Let’s see both, side by side.