@screwlisp@gamerplus.org I should also say, LLMs also work on non-textual information like images and and sound. That is the advantage of using purely statistical methods, rather than the symbolic method you described. You can easily interface a LLM with a neural network that decomposes an image or sound into quanta that can be mapped onto the vectors that encode some “meaning” of words/tokens in a sentence, so you can prompt an LLM to create an image from text, or to create a textual description of an image. Statistical methods can also create a “sentiment” vector for passages of text, finding some way to encode the meaning of a textual passage into a number that you can use to compare to other numbers to decide if two passages are similar in meaning. This is useful for quickly searching through a large text corpus. Most modern chat bots do this, it is what they call “Retrieval Augmented Generation.” They use sentiment analysis to find text passages that seem like they may be related to your prompt, and use the “similar in sentiment” text passages to augment your prompt before then constructing an output. The purely symbolic language models with sparse matrices would probably be much more energy efficient, but the trade-off is that you lose the ability interface with neural networks trained on image or audio signals and also the ability to perform sentiment analysis. @AmenZwa@mathstodon.xyz @aartaka@merveilles.town @dougmerritt@mathstodon.xyz @kentpitman@climatejustice.social @neauoire@merveilles.town @rwxrwxrwx@mathstodon.xyz