[00:00] This came up in a full video that I did dissecting You see, when tools like Chachipt process text, and they associate each piece with a large vector, some long list of numbers.
[00:16] and it's helpful to imagine these embedding vectors as directions in some very more than three dimensions.
[00:28] encode meaning into the directions of this high dimensional space. If you take the difference between the embeddings of man and woman and you add that to the embedding of uncle, you get a vector very close to the embedding of aunt.
[00:43] embedding of Hitler, you get something very close to the embedding of Mussolini. It's as if the model learned to associate some directions in this high dimensional space with Italian-ness, and others with World War II axis leaders.