Synthèse
Le texte explore trois éléments essentiels pour acquérir ce qui nous manque : les connaissances (acquises via l'apprentissage autonome et l'IA), la passion (retrouvée par l'introspection et diverses pratiques), et les compétences pratiques (développées ou déléguées grâce à l'IA). L'auteur souligne que l'éducation traditionnelle n'est pas indispensable et que maîtriser l'IA permet de compenser les lacunes dans les domaines qu'on souhaite explorer.
The good news is that if you have the desire from number two to know how things work, you can actually study and learn how things work. You can solve the first one pretty easily, especially with AI now. You can just learn how things work. Traditional education is not even strictly required. In fact, it can be a hindrance because it's so slow.
You can learn a massive amount of stuff from YouTube and become a deep subject expert on things by studying, by practicing with AI, and by learning and building yourself through trial and error. So if you don't have number one, there are lots of ways to go about getting it. If you don't have number two, that's going to take some soul searching. It's going to take some aggression. It's going to take going back into yourself when you were a child.
You've got to ask yourself: did you ever have this fire? Did you ever have passion? Did you ever want to make things? And if so, how do we get back to that? If you never did, well, there are some other exercises you could do there, but it's going to be more difficult.
You can read about meditation, you can maybe experiment with psychedelics or something like that. There are lots of different options, none of which I'm directly recommending—other than maybe poetry from Rilke. And the third one: if you don't have the skills to actually make and build and construct things and take action, that's also a thing you can learn. And it's a thing you can outsource quite a bit with AI. In fact, your skills at AI are in the third category—and in the first category as well, because deeply studying and learning AI skills starts with deeply understanding and learning AI itself.
So I would put that into categories one and three. But essentially, three should be heavily focused on getting extremely good at AI so that you can delegate the parts of number one that you don't understand while you're learning them, if it's one of the things you want to learn.
Source originale
en.wikipedia.org