Honestly, the main thing that interests me in this AI wave isn’t the chatbots or the text generation. It’s the vectorization. The fact that we can take language and encode it into a point in some high-dimensional space, and words, images and videos get coordinates which have meaning… That’s what’s interesting to me. Not the model that talks back to you, but the way things relate to each other in ways we never explicitly taught them. These embeddings already capture relationships in hundreds or thousands of dimensions, information we can’t even visualize. If we got really good at building those high-dimensional semantic structures, I think that’s where we can really start accelerating this field. And isn’t that literally the transition that happened before: from rule-based NLP systems (symbolic AI, grammar rules, hand-engineered features) to statistical NLP, and then eventually to distributional semantics and vector spaces (Word2Vec, GloVe, and later contextual embeddings in Transformer models)? Right now, we are optimizing LLMs, the transformers that function as interpreters, to do this “mediation” job more efficiently. They basically help organize the vector space into interpretable semantic dimensions. But these “meaningful directions” are what actually changed this tech from a bad word-generation bot into something that’s actually useful. What do you think? Maybe I’m just getting too excited from seeing those gradient descent simulations and all this “high-dimensional” talk. submitted by /u/User4f52
Originally posted by u/User4f52 on r/ArtificialInteligence