# What AI actually does with your prompt ## Метаданные - **Канал:** Steve Mould - **YouTube:** https://www.youtube.com/watch?v=1ICzp9TXFBw - **Дата:** 15.05.2026 - **Длительность:** 2:57 - **Просмотры:** 218,366 - **Источник:** https://ekstraktznaniy.ru/video/51546 ## Описание You can buy my books here: https://stevemould.com/books You can support me on Patreon and get access to the exclusive Discord: https://www.patreon.com/stevemould just like these amazing people: Glenn Watson Peter Turner Joël van der Loo Matthew Cocke Mark Brouwer Deneb Twitter: http://twitter.com/moulds Instagram: https://www.instagram.com/stevemouldscience/ Facebook: https://www.facebook.com/stevemouldscience/ TikTok: https://www.tiktok.com/stevemould Buy nerdy maths things: http://mathsgear.co.uk ## Транскрипт ### Segment 1 (00:00 - 02:00) [] One of the things I find most interesting about generative AI is how it shows the ways in which artificial intelligence is similar to human intelligence and the ways in which it is quite different. Like the way AI distills different layers of meaning from a passage of text seems very human, but the fact that it doesn't know how many Rs there are in strawberry seems very unhuman. To be able to create an image from a text prompt, the model needs to be able to understand the meaning of the text. So maybe we need a separate model that can understand text. The way a text model or a language model works is really clever. Basically, it goes through several iterations where its semantic understanding of the text becomes more and more nuanced. So like in the first iteration, maybe it realizes that the word blue is in front of the word boat, so that means the boat is blue. Then a few iterations later, it might realize that the person mentioned at the start of the text prompt is actually on the boat and the waves mentioned later are choppy and that's causing the boat to rock. And then by the 20th iteration, it knows that the text was written as a gothic horror with an unreliable narrator. What you get out the other end for our purposes is basically just a long string of values that represents all these different meanings. That string of values is usually referred to as a vector because you can think of it like a point in this super high-dimensional space of semantic meaning. I said earlier that it might be impossible to understand how these models work and I think this is probably a good example of what I meant by that. Like to a certain extent, you can interrogate these vectors that represent the meaning of a prompt and you can discover that a particular value might represent the linguistic concept of gender. So that if that value increases, then the prompt becomes semantically more feminine. Maybe it's a few vectors that represent different parts of the prompt. But in general, it's very hard to see what these different values, or directions if you like, in this super high dimensional space actually represent semantically. But it works somehow anyway. And maybe we shouldn't be training AI to mimic human intelligence, because then we find ourselves having to quash all these human fallibilities like bias and hallucination. Artificial intelligence was trained to play Go by watching previous human matches. And when it eventually won against a human, it was mostly playing human moves. But the clinching move, interestingly enough, was one that no human had ever seen before. And when the AI was retrained without seeing any human games, just playing against itself, it learned to beat humans more quickly and more comprehensively with moves never seen before, which is both fascinating and terrifying.