5 ms·
As the models keep improving, wouldn’t you be able to task a newer AI to “clean up this mess”?
by orev 5mo ago
As the models keep improving, wouldn’t you be able to task a newer AI to “clean up this mess”?
- aaron_m04 5mo agoHow could anyone answer that with any level of certainty?
- jeremyjh 5mo agoFrankly this is what everyone is counting on whether they know it or not. The question though is not “will the models get good enough?”. The question is does the repo even contain enough accurate information content to determine what the system is even supposed to be doing.
- hennell 5mo agoAi runs `rm -rf`
- dalmo3 5mo agoBeyond the Singularity, we reach the Nullarity.
- AussieWog93 5mo agohttps://youtu.be/m0b_D2JgZgY https://youtu.be/m0b_D2JgZgY
- malfist 5mo agoAre they improving? I thought they were just getting more expensive
- maplethorpe 5mo agoMythos apparently wrote a poem so beautiful it made Dario cry.
- missedthecue 5mo agoEvery frontier model from each major US lab is cheaper than their frontier model this time a year ago with the exception of Anthropic whose pricing has remained exactly the same.
- SpicyLemonZest 5mo agoPeople are often skeptical when I say this, but there's simply no guarantee that it's possible in principle to clean up a bad architecture. If your system is "overfitted" to 10,000 requirements from 1,000 customers, it may be impossible to satisfy requirements 10,001 through 10,100 without starting over from scratch.
- literalAardvark 5mo agoIt may be difficult, but impossible is such a big word to use here
- SpicyLemonZest 5mo agoIt's really not that big of a word. The CAP theorem shows that as few as three reasonable-sounding requirements with no obvious conflicts can be impossible to satisfy simultaneously. (User needs will start more flexible than strict mathematical requirements, of course, but once people start to build production workloads on top of your systems that flexibility is radically reduced.)
- fg137 5mo agoHow is a newer AI going to "clean up" dropped databases, compromised computers or leaked personal data? (None of above is theoretical)
- ActorNightly 5mo agoI really am surprised that people on a heavy CS themed forum still have trouble grasping this. Imagine the year is 1995, C exists, but some guy out there is working on essentially what modern Python is. He says to you "check out this language, you can just import stuff, and use it and dynamically modify anything at run time". You can probably come up with hundreds of arguments about things that could go wrong, like memory clean up, threading, e.t.c, but turns out, incrementally, they were all solved and we have the modern Python that basically is good enough to build these large LLM models. Now imagine modern programming and computing is what C was back in 1995, and AI use is that guy building the Python code.
- fg137 5mo agoWhat's your point? I think you have some serious misunderstanding here.
- ActorNightly 5mo agoThe point is that in the future, AI will be able handle things like missing databases just like the modern high level dynamic languages can import a library to handle whatever you want.
- maplethorpe 5mo agoI can't tell if you're being facetious, but a future AI really may be able to fill in a missing database. Like, if it knew some of the entries, it could infer the rest.
- defrost 5mo ago
- jcalx 5mo agoSomeone responded to a previous comment of mine [0] positing a Peter principle [1] of slopcoding — it will always be easier to tack on a new feature than to understand a whole system and clean it up. The equilibrium will remain at the point of near, but not total, codebase incomprehensibility. [0] https://news.ycombinator.com/item?id=48037128#48038639 https://news.ycombinator.com/item?id=48037128#48038639 [1] https://en.wikipedia.org/wiki/Peter_principle https://en.wikipedia.org/wiki/Peter_principle
- thefourthchime 5mo agoYes. And as the models get better, it works better. But at one point you do have to understand the code because it's also just guessing as to what your actual intentions are. It doesn't know what mess you want to clean up. A lot of times AI just starts making up new patterns on top of other patterns and having backwards compatibility between the two. How does it know which one you actually like?