Post #3147652

you may notice several problems with this entire structure. don't worry, nobody who likes ai cares about any of them! the word embeddings are a major source of bias. basically, the entire math that they're based on presumes that the set of text data used for creating the embedding has an overall 100% perfectly balanced bias, that is, the data set has no pull or push towards or away certain viewpoints, writing styles.etc. also, the impact of a given bias scales with the amount of text containing it, and is entirely unrelated to the actual importance or authority of the source. so like, if say Alex Jones talks about something more than anyone, that topic in the embeddings will ties words together in a way disproportionately like him than anyone else. there's zero way to have any human in the loop on any of this at all. the attention mechanism is also rather odd. neat idea in theory maybe but given the size of the typical token window (we're talking highly advanced models don't go much over a dozen or so tokens!), it's entirely inadequate for any discussion longer than a single sentence or two. I also imagine it's even more susceptible to bias than the embeddings, as the only reasonable training methods I can conceive of are either human guided (which usually involves just hiring a company to do it for you, whose employees are all likely culturally similar!) or trying to do some manner of automated training likely using the embeddings to begin with (so therefore the attention mech will just exaggerate it bias even more!) the fact that each decoder layer has to have a correction applied via the encoder stacks final output seems like a good indication that something's wrong here, but don't put it past AI bros to not give a shit at all costs!! also the final output token being just randomly selected is just hilarious, since that token then feeds into the system for the next tokens. so you better hope to god it never randomly picks a bad one even once the whole time!