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It reminds me of the "code generator" phase we went through a decade or so ago. At least then we could see the "code generator" code and adjust it when it produ

It reminds me of the "code generator" phase we went through a decade or so ago. At least then we could see the "code generator" code and adjust it when it produced dreck. I'll also note that the next hardest part of programming is troubleshooting "in production" whether a Web application, in an embedded device, or running on someone's machine. Is the "AI" going to help there? Are we going to even be able to fix those problems when doing so could make the code we don't understand fail in another way or hit the wrong side of the performance tradeoff the "optimized code" entailed?

hinkley 4y ago
Code generation is an epicycle. My dad was worried about whether I should go into a CS degree because there was a code generation cycle going on at the time and people thought the computers would be programming themselves. We've had a few since, and my skills are more valuable than ever.
- didericis 4y ago
  This particular cycle seems likely to be really destructive. Software is way more prevalent in nearly every aspect of our lives than it used to be, and all of the large organizations that don't really understand the problems that are going to compound invisibly are going to turn their products into unintelligible giant heaping piles of garbage very quickly if they're too enthusiastic. Large codebases almost always turn into giant heaping piles of garbage, so that's nothing new, but you can usually get someone to dig through and salvage things from the garbage pile when it goes bad. This article is from 2017, and I don't know about you, but I increasingly feel like software is more and more broken year after year. I think that has a lot to do with "delegating complexity" and building things without really understanding what they're doing. I think the correlation between human understanding of the fine details and underlying logic and desired outcome and software quality is pretty tight. That doesn't mean "software 2.0" neural net stuff doesn't fit in, you just need a human to plug it in right that really understands its benefits and its limitations. The author mentions the downsides, but I think they underestimate them. If you lean on AI too heavily and don't ever translate to a traditional language, you've basically liquified your logic/there's no garbage dump to salvage from when things go bad and zero understanding of implicit context. If you use it to generate ostensibly human readable code you get "documentation" with no guarantee of accuracy, which makes it worse than having no documentation (depending on how high the error bars are). While that's not a new problem either, if it's autogenerated that means it's easy to create way more of it than human generated code, which means it'll probably be an ever larger portion of what gets sucked up into later AI models. If they become too self referential they'll become increasingly detached from human judgement about whether the code is doing what it should and error bars will grow. I'm still convinced these things are virtually all going to end up in a fancy autocomplete suggestion and compression niche after a lot of pain. But that's still a big deal/I don't think the limitations of these means they don't have a big future place. The sheer number of things you can have autosuggest for now with these AI models are amazing, and that expansion is boosting productivity and creating a large number of new products that are going to become essential tools. That being said, every time this type of thread comes up I'm like "woah woah woah, pump the breaks, these things have no understanding of what they're doing. You can't just stop thinking about stuff and let a machine do it, bad bad bad idea."

hinkley 4y ago

Code generation is an epicycle. My dad was worried about whether I should go into a CS degree because there was a code generation cycle going on at the time and people thought the computers would be programming themselves. We've had a few since, and my skills are more valuable than ever.

didericis 4y ago
This particular cycle seems likely to be really destructive. Software is way more prevalent in nearly every aspect of our lives than it used to be, and all of the large organizations that don't really understand the problems that are going to compound invisibly are going to turn their products into unintelligible giant heaping piles of garbage very quickly if they're too enthusiastic. Large codebases almost always turn into giant heaping piles of garbage, so that's nothing new, but you can usually get someone to dig through and salvage things from the garbage pile when it goes bad. This article is from 2017, and I don't know about you, but I increasingly feel like software is more and more broken year after year. I think that has a lot to do with "delegating complexity" and building things without really understanding what they're doing. I think the correlation between human understanding of the fine details and underlying logic and desired outcome and software quality is pretty tight. That doesn't mean "software 2.0" neural net stuff doesn't fit in, you just need a human to plug it in right that really understands its benefits and its limitations. The author mentions the downsides, but I think they underestimate them. If you lean on AI too heavily and don't ever translate to a traditional language, you've basically liquified your logic/there's no garbage dump to salvage from when things go bad and zero understanding of implicit context. If you use it to generate ostensibly human readable code you get "documentation" with no guarantee of accuracy, which makes it worse than having no documentation (depending on how high the error bars are). While that's not a new problem either, if it's autogenerated that means it's easy to create way more of it than human generated code, which means it'll probably be an ever larger portion of what gets sucked up into later AI models. If they become too self referential they'll become increasingly detached from human judgement about whether the code is doing what it should and error bars will grow. I'm still convinced these things are virtually all going to end up in a fancy autocomplete suggestion and compression niche after a lot of pain. But that's still a big deal/I don't think the limitations of these means they don't have a big future place. The sheer number of things you can have autosuggest for now with these AI models are amazing, and that expansion is boosting productivity and creating a large number of new products that are going to become essential tools. That being said, every time this type of thread comes up I'm like "woah woah woah, pump the breaks, these things have no understanding of what they're doing. You can't just stop thinking about stuff and let a machine do it, bad bad bad idea."