| It actually goes a lot deeper than that. Companies like this helped organize cheap workers around the world to just put all that information once and for all, to train the AI and interpolate / remix the answers for people: https://www.forbes.com/sites/kenrickcai/2023/04/11/how-alexa... OpenAI this year hired a bunch of people to just manually do the work of basic coding once and for all: https://www.semafor.com/article/01/27/2023/openai-has-hired-... This is a story of using cheap labor to train machines at scale. OpenAI's content moderators have just unionized: https://futurism.com/the-byte/ai-content-moderators-unionize... Moreover, this is a story of taking labor of artists all over the world, remixing it at scale and selling it, destroying any sort of scarcity: https://the-decoder.com/ai-images-sold-on-the-internet-artis... More artist issues: https://www.nytimes.com/2023/05/01/arts/design/ai-art-class.... There's GitHub, which had tons of people contribute code, and then the AI just ingested all that work for remixing by Microsoft Copilot:
https://www.techtarget.com/searchsoftwarequality/news/252526... And of course, finally there's Wikipedia, which had tons of people contribute actual changes, vet them for accuracy, etc. over 20 years, in many languages -- simply taken for use by AI companies:
https://www.vice.com/en/article/v7bdba/ai-is-tearing-wikiped... But once again, in 2023, where we are now, there is a staggering amount of humans just producing this kind of content, and the AI is mainly used to model and remix it. Perhaps to do so can form some sort of internal understanding of this text, and that's what's interesting. But even Sam Altman has lately said "scale is not all you need". So a new approach is here needed. https://www.youtube.com/watch?v=PsgBtOVzHKI |