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Remember Deep Thought, the greatest computer ever built that spent 7.5 million years computing the Answer to the Ultimate Question of Life, the Universe, and Ev

by _pdp_ 7mo ago

Remember Deep Thought, the greatest computer ever built that spent 7.5 million years computing the Answer to the Ultimate Question of Life, the Universe, and Everything? The answer was 42, perfectly correct, utterly useless because nobody understood the question they were asking. That's what happens when you hand everything to a machine without understanding the problem yourself. AI can give you correct answers all day long, but if you don't understand what you're building, you'll end up just like the people of Magrathea, staring at 42 and wondering what to do with it. True understanding is indistinguishable from doing.

bitwize 7mo ago
Well, yes, but AI can also give you wildly incorrect answers with alarming frequency. I know, I know, "skill issue"/"you're holding it wrong". And maybe that's vacuously true, in that it's so hard to guess what will produce correct output, because LLMs are not an abstraction layer in the way that we're used to. Prior abstraction layers related input to output via a transparent homomorphism: the output produced for an input was knowable and relatively straightforward (even with exotic optimization flags). LLMs are not like that. Your input disappears into a maze of twisty little matmuls, all alike (a different maze per run, for the same input!) and you can't relate what comes out the other end in terms of the input except in terms of "vibes". So to get a particular output, you just have to guess how to prompt it, and it is not very helpful if you guess wrong except in providing a wrong (often very subtly so) response! Back in the day, I had a very primitive, rinky-dink computer—a VIC-20. The VIC-20 came with one of the best "intro to programming" guides a kid could ask for. Regarding error messages it said something like this: "If your VIC-20 tells you something like ?SYNTAX ERROR, don't worry. You haven't broken it. Your VIC-20 is trying to help you correct your mistakes." 8-bit 6502 at 1 MHz. 5 KiB of RAM. And still more helpful than a frontier model when it comes to getting your shit right.
- _pdp_ 7mo ago
  You are correct. One minor note. The skill issue isn't about failing to prompt it correctly, but rather failing to understand what it actually does. There's an entire crop of professionals who believe we can Harry Potter our way out of any situation with the right magic words.
rmunn 7mo ago
The question to which 42 was the answer was, of course, "How many roads must a man walk down, before you call him a man?"
slopinthebag 7mo ago
> AI can give you correct answers all day long, but if you don't understand what you're building, you'll end up just like the people of Magrathea, staring at 42 and wondering what to do with it. Yeah it's so true. LLM's tell you what you want to hear based on the input you give it. If you know the domain, it's really powerful because it can output what you want it to output at an incredibly fast rate. If you don't, you're basically rolling the dice on what it gives you. The idea that programmers are now useless because it can output something is hilariously wrong. The quality of the input has a direct impact on the quality of the output, meaning people with domain expertise (i.e. software developers) is a crucial component to its utility. In other words, vibe coding is useless unless the viber has the correct vibes. Or in other other words, being a good programmer is fundamental to the technology producing useful results. As such, we aren't going to see a total replacement of software developers, but rather that good software developers will increase their productive output.

by _pdp_ 7mo ago

bitwize 7mo ago

Well, yes, but AI can also give you wildly incorrect answers with alarming frequency. I know, I know, "skill issue"/"you're holding it wrong". And maybe that's vacuously true, in that it's so hard to guess what will produce correct output, because LLMs are not an abstraction layer in the way that we're used to. Prior abstraction layers related input to output via a transparent homomorphism: the output produced for an input was knowable and relatively straightforward (even with exotic optimization flags). LLMs are not like that. Your input disappears into a maze of twisty little matmuls, all alike (a different maze per run, for the same input!) and you can't relate what comes out the other end in terms of the input except in terms of "vibes". So to get a particular output, you just have to guess how to prompt it, and it is not very helpful if you guess wrong except in providing a wrong (often very subtly so) response! Back in the day, I had a very primitive, rinky-dink computer—a VIC-20. The VIC-20 came with one of the best "intro to programming" guides a kid could ask for. Regarding error messages it said something like this: "If your VIC-20 tells you something like ?SYNTAX ERROR, don't worry. You haven't broken it. Your VIC-20 is trying to help you correct your mistakes." 8-bit 6502 at 1 MHz. 5 KiB of RAM. And still more helpful than a frontier model when it comes to getting your shit right.

_pdp_ 7mo ago
You are correct. One minor note. The skill issue isn't about failing to prompt it correctly, but rather failing to understand what it actually does. There's an entire crop of professionals who believe we can Harry Potter our way out of any situation with the right magic words.

rmunn 7mo ago

The question to which 42 was the answer was, of course, "How many roads must a man walk down, before you call him a man?"

slopinthebag 7mo ago

> AI can give you correct answers all day long, but if you don't understand what you're building, you'll end up just like the people of Magrathea, staring at 42 and wondering what to do with it. Yeah it's so true. LLM's tell you what you want to hear based on the input you give it. If you know the domain, it's really powerful because it can output what you want it to output at an incredibly fast rate. If you don't, you're basically rolling the dice on what it gives you. The idea that programmers are now useless because it can output something is hilariously wrong. The quality of the input has a direct impact on the quality of the output, meaning people with domain expertise (i.e. software developers) is a crucial component to its utility. In other words, vibe coding is useless unless the viber has the correct vibes. Or in other other words, being a good programmer is fundamental to the technology producing useful results. As such, we aren't going to see a total replacement of software developers, but rather that good software developers will increase their productive output.