What makes us think that encoding all functionality using natural language is somehow more compact than using code? Sure, describing a website with a button is easy, but I don’t quite see how you would describe Kubernetes or a device driver without writing a book in “legalese”.
Or try formulating a math proof with natural language.
Edit: besides, if it could work you lose the competitive edge. I could describe a much faster more cost effective system which the machine can implement. And we are off to the races again..
If/when it is a generally capable intelligence it very much does work that way. That's how it worked with humans. We saw that stuff needed to work at scale and created something to achieve that.
>I think (hope) that is still off in the distance.
It's being rumored that OpenAI is currently training GPT-5 which will be ready in December, and that many people in the company think it will be a human or better level AGI. Even if it isn't the consistently supersonic jumps they are making every generation suggests we don't have long until human brains are outmoded legacy hardware.
>We’d have other problems than scaling crud apps, I think.
Ever since I first interacted with the original GPT-3 in 2020 I've had the realization that our future was going to be curtailed and distorted into an inconceivable Escher piece. It seems that future is nearly upon us.
My first encounter with GPT-3 messed me up too. Interesting.
I'm all over the place with this. Some days I think it's no big deal, but sometimes I'll get angsty about it.
Today, for example, I'm using it to generate some animations and stuff I generally don't like dealing with (math problems). I remember spending hours on this and not getting anywhere. This thing makes all that effort seem like handcoding websites in the era of templates.
Whenever it hits my direct line of work I'm like "no way that thing works, see, it did this small thing wrong and it proves it is fundamentally incapable of anything". When I use it for domains outside of my expertise I switch to "yeah, sure, but this was either already exceedingly obvious and/or nonsense busy-work to begin with".
It's a matter of degree: you can probably already get GPT4 to generate simple proofs; I'm not expecting it to break new ground in mathematics. What I'm getting at is just that the usage is declarative: you'd be describing what you want, not the details of some particular mathematical formulation (though you would likely iterate, making references to formulation details, but still speaking mostly in natural language).
I understand, thanks for the clarification. What I like to add is that our reality is a competitive one. If everybody can say “do this general thing”, what is the competitive edge? It’s all relative.
We as humans are not just in the business of solving general problems. We are competing with each other. We need to be faster than the slower ones to survive. (I like to change that but that is not a technical issue.)
One of the ways to compete is to “talk faster” with it. Iterate quicker than the competition. How? I daresay we might get there faster by talking in some sort of modified language.. a code of sorts..
Another way to compete is to become a deep domain expert. Expert of what exactly, if AI is doing it all? Human psychology?
I guess I am just interested in the competitive aspect of it. I have no idea what will happen, but definitely curious what will be possible.
Or try formulating a math proof with natural language.