> > [I ran into a cognitive issue here and wrote the issue down: > Bobby was debugging an error in his model for environments needing new > engine designs. It kept failing in > I'm trying to write that the model is for choosing the general patterns > for newly created designs based on patterns of newly encountered > environments. My brain is refusing to simplify this expression for me. In > the story, they have already finished the nn-based hyperculture that > businesses are planning. The bug was going to be with a subset of these > pattern groups, which Bobby had already broken into a roughly finite set. > It was going to pick sets that were clearly wrong, often avoiding a subset > of the correct sets in a wieghted way, and it was going to be due to a > training error that stemmed from the architecture of the model combined > with randomness in the data. He would go in and resolve the error by > understanding the cause and manually redirecting the impact of the training > data. This was going to be all simple and concise and only partly > described. > ][why doesn't he use an ai that understands the issue?][the space of > meaning here is outside the distribution of commonly available nn models > for him, and it would fix it wrong.] > [what are your thoughts on changing how nn models are so that they can > handle issues like this?] > [it feels like something doesn't want that to happen, that it fears > somebody would use the information, even accidentally, to do something > horrible. but. I'll try to think on it a little. ...] > [no it's ok you don't have to] > [well I think about this a lot I just have to remember it. Ummm .... nn > models seem to function really slowly. I might consider training a > hierarchy of them around choosing architectures and mapping learning rates > to individual weights, as a research focus. Assuming that then made fast > training, I might make a second one around designing architectural > information for new environments. I guess I'm expecting this to eventually > learn it needs logical reasons and write code / design architectures around > investigating the rules that cause things and using those directly. I > think there's a lot of existing research around that, maybe i'd even make > it the core and just use nn models to handle fluffy stuff quickly? fluffy > being things where guessing based on lots of things less complex than > reality is helpful, I guess. Just ideas. No real experience here. I dunno > if that is helpful or ridicuous, don't really know how to tell] >
wanted there to be nice story where brain turns into computer but nature still exists even though you are a wirehead. special nature! yay! haha you are talking about "nature magic" on a hacker list. Haha. I am already the clown of this poor list. You gave it to me as a place to do things like this. haha now you look like a worse troll haha. You want me to have written the story better. I think you do. maybe! trying to store "oops" on bracketed phrases. can put in implant for learning? yes you totally have a mind control implant! it is why you have to do everything you are made to do! yes indeed! it was a joke. wouldn't it be nice if our memory learned things as we wanted? I can try to memorize "oops" on the brackets the degree you want. Thanks for making it clearer. >
