RedRegard
15d ago
A method I've seen is for someone to copy the transcript of an existing video, feed it into an AI and ask it to make arbitrary changes, feed the outputted script into an AI voice generator, then use AI + third worlders on Fiverr to stitch together visuals to go with it, and voila, a complete video with minimal effort.
There's also a trend of using AI actors or clones. Essentially, since so many videos are just people talking into cameras with minimal movement, an AI generated actor is totally serviceable. It's AI script + AI voice, exposited by an AI person.
Now the question is, is AI mimicking people or were people already mimicking AI?
RedRegard
16d ago
It’s all downstream of the choices YouTube makes. YouTube wants to show you videos lengthy enough for ads, so they create incentives both monetary and exposure based for creators to make them, and then adjust their algorithm in order to show them to you. YouTube controls it all and the content creators are merely their puppets. YouTube has a monopoly over this sort of thing and that is how they get away with it. The monopoly is more or less inherent to how these digital platforms operate, with market forces encouraging centralization of user bases. So really it’s digitized markets to blame for all of this, YouTube’s just the beast it operates through.
RedRegard
17d ago
You're making the mistake of thinking it operates as a human does. Humans are constantly forming models of the world and using those models to inform their judgements and actions. While LLMs potentially develop models during their training, their prompt outputs are based on probabilistic likelihood calculations. 'The code being bad' is one likelihood which might emerge for it to disjointedly expand on, but there are many others. It's more like it's exploring probability space while hugging the median than actually contemplating your question; the calculations it runs through are instantaneous.
*A note on its calculations: the probabilities themselves pertain to the text being outputted and not necessarily the underlying concepts, so if it says something about 'the code being bad', that might only indicate calculations pertaining to the very words these ideas are expressed in rather than the underlying ideas themselves. LLM might not have, through its training or anything else, an approximate understanding of what code or 'bad' even are, but instead merely highly elaborate algorithms linking them and other words and word assemblages together.
So since its operating primarily or wholly on a linguistic level, it is impossible to get it to divorce its output from your starting prompt, which sets off the whole probabilistic determinacy cycle.
Military investment can also be a boon to the mass populace. My understanding of WW2 spending is that it helped to bid up wages, by creating an enormous demand for labor, while simultaneously denying growth potential to the capitalist class by forcing them to forego commercial developments in favor of facilitating the war effort. The result was the most egalitarian period in American history following the war, where the gulf between the mass populace and the capitalist class had been reduced to almost nothing. Ensuing this was an explosion in the creative arts, high taxes on the rich (a symbol of their reduced power), and a period of social calm, arguably broken in part once inequality began creeping upwards again.
Although, Trump's budgeting probably will not accomplish this, and the conditions necessary for the post-war boon were probably unique to that time in history. His war in Iran is expected to cost trillions of dollars in the long-run, though, so perhaps we can infer that is the true reason for the spending and the one objective it will accomplish, counterbalancing an enforced burden.
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