4 ms·

Art is where an approximation is fine and you can fill the holes with "subjectivity", but engineering is where missing a bolt on a bridge could collapse the who

Art is where an approximation is fine and you can fill the holes with "subjectivity", but engineering is where missing a bolt on a bridge could collapse the whole thing. AI is adequate for art. It is NOT suitable for engineering. Not unless you build a ton of handrails or manually verify all the code and logic yourself.

haolez 4y ago
But... what if something like this works for the entire life-cycle of a given product? We might reach this point.
frognumber 4y ago
It depends on what you want out of life. * If you want a medical device, it's a problem. * If you want a fun game or piece of social media, it's probably not. Over time, we'll know the contours a lot more. A lot of engineering came about purely empirically. We'd build a building, and we'd learn something based on whether or not it fell down, without any great theory as to why. I suspect deep language models might go the same way. Once a system works a million times without problems, the risk will be considered low enough for life-critical applications. (And once it's in all life-critical applications, perhaps it will decide to go Darknet on us. With where deep learning is going, the Terminator movies seem less and less like science fiction.)
- daveguy 4y ago
  > * If you want a medical device, it's a problem. > * If you want a fun game or piece of social media, it's probably not. This is exactly the distinction between requires engineering and does not require engineering. Current models are great for the latter, but dangerous for the former.
auggierose 4y ago
I am sorry to tell you, but AI is exceptional for engineering. Just make the AI also generate a proof that its code meets the spec. That's what human engineers should already do, but it was costly, because the tools were not good enough and the engineers not educated enough. AI is going to cut right through that Gordian knot. This should not be surprising: There is a large intersection between engineering and mathematics. And mathematics is art.
- jensensbutton 4y ago
  Writing a spec thorough enough for AI to generate code that verifiably meets in and solves your problem means you're still programming, but specs instead of systems.
  - auggierose 4y ago
    Sure, the human element will still be there. But note that the detail of your spec will converge to the natural "resolution" of your problem as the power of your AI increases.
    - jensensbutton 4y ago
      But at what scope? The how will always matter. Your AI could design a system that bankrupts you on the first day. To prevent that you need to specify constraints and it's turtles all the way down. It would free you from spending time on areas you don't care about, but that's already true with SaaS.
      - auggierose 4y ago
        The how that matters must be part of your spec, of course. It is turtles all the way down, but the point is that apart from the top turtle, all the other turtles will be AIs. That's a big difference. Just the step from "program" => "spec" is already a big one. So big, that it is rarely done today. Test-driven development is an attempt at this, but the problem is that tests cannot truly verify a spec. Proofs can. Of course, you can combine tests and proofs, for example proofs for correctness, tests to make sure other measures like speed and cost are sane. But if you want to be absolutely sure, you will need to replace all tests by proofs.
        Existenceblinks 4y ago
        Is it: https://www.commitstrip.com/en/2016/08/25/a-very-comprehensive-and-precise-spec/ https://www.commitstrip.com/en/2016/08/25/a-very-comprehensi...?
meowkit 4y ago
As an engineer and a musician I want to push back on some of this. Missing a bolt on a bridge is hyperbolic. Your simulation should catch that long before the bridge is ever built. Engineering is also all about approximation. Art and Engineering both build models - the differences are the granularity and the constraints. Engineering is constrained by physics and requires infinitesimal calculus to make good predictions. AI today is inadequate for engineering (and I might say for "great" art as well), but given my understanding of the maths and software underlying these models there is zero reason to believe that AI will not be absolutely adequate in the coming decades. In my opinion (based on my experiences), Art is just the set of processes that we haven't rigorously defined. There is a duality to Science and Art, where it seems that empiricism and quantifiable data convert Art >into< Science.
Existenceblinks 4y ago
It needs to be wrapped in processes of making product, not as a approximate product like they are currently do. Many good old AI algorithms are about heuristic e.g. Minimax in a 2 player board game. The approximation (heuristic) is wrapped with the rule of game, thus the product, the rules are rigid.

haolez 4y ago

But... what if something like this works for the entire life-cycle of a given product? We might reach this point.

frognumber 4y ago

It depends on what you want out of life. * If you want a medical device, it's a problem. * If you want a fun game or piece of social media, it's probably not. Over time, we'll know the contours a lot more. A lot of engineering came about purely empirically. We'd build a building, and we'd learn something based on whether or not it fell down, without any great theory as to why. I suspect deep language models might go the same way. Once a system works a million times without problems, the risk will be considered low enough for life-critical applications. (And once it's in all life-critical applications, perhaps it will decide to go Darknet on us. With where deep learning is going, the Terminator movies seem less and less like science fiction.)

daveguy 4y ago
> * If you want a medical device, it's a problem. > * If you want a fun game or piece of social media, it's probably not. This is exactly the distinction between requires engineering and does not require engineering. Current models are great for the latter, but dangerous for the former.

auggierose 4y ago

I am sorry to tell you, but AI is exceptional for engineering. Just make the AI also generate a proof that its code meets the spec. That's what human engineers should already do, but it was costly, because the tools were not good enough and the engineers not educated enough. AI is going to cut right through that Gordian knot. This should not be surprising: There is a large intersection between engineering and mathematics. And mathematics is art.

jensensbutton 4y ago
Writing a spec thorough enough for AI to generate code that verifiably meets in and solves your problem means you're still programming, but specs instead of systems.
- auggierose 4y ago
  Sure, the human element will still be there. But note that the detail of your spec will converge to the natural "resolution" of your problem as the power of your AI increases.
  - jensensbutton 4y ago
    But at what scope? The how will always matter. Your AI could design a system that bankrupts you on the first day. To prevent that you need to specify constraints and it's turtles all the way down. It would free you from spending time on areas you don't care about, but that's already true with SaaS.
    - auggierose 4y ago
      The how that matters must be part of your spec, of course. It is turtles all the way down, but the point is that apart from the top turtle, all the other turtles will be AIs. That's a big difference. Just the step from "program" => "spec" is already a big one. So big, that it is rarely done today. Test-driven development is an attempt at this, but the problem is that tests cannot truly verify a spec. Proofs can. Of course, you can combine tests and proofs, for example proofs for correctness, tests to make sure other measures like speed and cost are sane. But if you want to be absolutely sure, you will need to replace all tests by proofs.
      - Existenceblinks 4y ago
        Is it: https://www.commitstrip.com/en/2016/08/25/a-very-comprehensive-and-precise-spec/ https://www.commitstrip.com/en/2016/08/25/a-very-comprehensi...?

meowkit 4y ago

As an engineer and a musician I want to push back on some of this. Missing a bolt on a bridge is hyperbolic. Your simulation should catch that long before the bridge is ever built. Engineering is also all about approximation. Art and Engineering both build models - the differences are the granularity and the constraints. Engineering is constrained by physics and requires infinitesimal calculus to make good predictions. AI today is inadequate for engineering (and I might say for "great" art as well), but given my understanding of the maths and software underlying these models there is zero reason to believe that AI will not be absolutely adequate in the coming decades. In my opinion (based on my experiences), Art is just the set of processes that we haven't rigorously defined. There is a duality to Science and Art, where it seems that empiricism and quantifiable data convert Art >into< Science.

Existenceblinks 4y ago

It needs to be wrapped in processes of making product, not as a approximate product like they are currently do. Many good old AI algorithms are about heuristic e.g. Minimax in a 2 player board game. The approximation (heuristic) is wrapped with the rule of game, thus the product, the rules are rigid.