| If that's the goal then perhaps we already surpassed it and I personally am not impressed. It's useful but basically every method of quality control requires a human. I've found that components of general intelligence specialized beyond human capability are much more useful than a model that can mimic a human. I think an LLM is just trying to do too much at once, all of the individual NLP algorithms most of them are made of are very useful to us, but an LLM is just not specialized enough to be any more useful than a human without specialization. Which isn't to say they're _useless_, but obviously not as useful as a specialist (in special contexts, denoted by whatever kind of specialist they are) ETA: as an aside, I'd like to contextualize my presumption that AGI is about AI singularity with the fact that Sam Altman casually stated that he doesn't care if it takes $50 billion to reach AGI. In the real world, with 50 billion dollars, you can do something much more useful than trying to build a product that's basically contradictory by definition. An AGI is (presumably) a general intelligence model but it's implicitly touted as being extremely useful for specialized tasks (because, humans can specialize), but once you specialize, I would argue your general intelligence tends to weaken. (For example I wouldn't expect a Harvard PhD to be 100% up to date with modern slang terms, but I'd be shocked if I went to a local bar and met someone who didn't know what rizz means). This is basically just trying to squeeze two opposite ends of a spectrum together, which sounds kind of like a singularity to me. |