PupBiru

thus the term “energy mix”… nobody arguing in good faith says PV is all we need

i don’t agree with that definition of creative… there’s lots of engineering work that’s creative: writing code and designing systems can be a very creative process, but doesn’t involve feeling… it’s problem solving, and thats a creative process. you’re narrowly defining creativity as artistic expression of emotion, however there’s lots of ways to be creative

now, i think thats a bit of a strawman (so i’ll elaborate on the broader point), but i think its important to define terms

i agree we should be skeptical of marketing hype for sure: the type of creativity that i believe ML is currently capable of is directionless. it doesn’t understand what it’s creating… but the truth lies somewhere in the middle

ML is definitively creating something new that didn’t exist before (in fact i’d say that its trouble with hallucinations of language are a good example of that: it certainly didn’t copy those characters/words from anywhere!)… this fits the easiest definition of creative: marked by the ability or power to create

the far more difficult definition is: having the quality of something created rather than imitated

the key here being “rather than imitated” which is a really hard thing to prove, even for humans! which is why our copyright laws basically say that if you have evidence that you created something first, you pretty much win: we don’t really try to decide whether something was created or imitated

with things like transformative works or things that are similar, it’s a bit more of a grey area… but the argument isn’t about whether something is an imitation; rather it’s argued about how different the work is from the original

that’s a lack of understanding of concepts though, rather than a lack of creativity… curation requires that you understand the concept that you’re trying to curate: this looks more like a dog than this; this is a more attractive sunset than this

current LLMs and ML don’t understand concepts, which is their main issue

id argue that it kind of does “think about its own thoughts” to some degree: modern ML is layered, and each layer of the net feeds into the next… one layer of the net “thinks about” the “thoughts” of the previous layer. now, it doesn’t do this as a whole but neither do we: memories and neural connections are lossy; heck even creating a creative work isn’t going to turn out exactly like you thought it in your head (your muscle memory and skill level will effect the translation from brain to paper/canvas/screen)

but even we hallucinate in the same way. don’t look at a bike, and then try and draw a bike… you’ll get general things like pedals, wheels, seat, handlebars, but it’ll be all connected wrong. this is a common example people use to show how our brains aren’t as precise and we might like to think… drawing a bike requires a lot of very specific things to be in very specific places and that’s not how our brain remembers the concept of “bike”

it’s only qualitative because we don’t understand it

when an LLM “experiences” new data via training, that’s subjective too: it works its way through the network in a manner that’s different depending on what came before it… if different training data came before it, the network would look differently and the data would change the network as a whole in a different way

and experience is ongoing learning, so if an LLM were training on things after the pretraining period then that’d allow it to be creative in your definition?

but in that case, what’s the difference between doing that all at once, and doing it over a period of time?

experience is just tweaking your neurons to make new/different connections

well now you’re just describing ansible

who hurt you?