The game is designing software to requirements. It's writing literature for a new era. It's creating X for A audience with N vauge unspecified needs -- where X is a complex product made of many parts, involving many people, with shifting and changing problems/solutions/requirements.
The game was never writing the stack overflow answer -- that was already written.
The problem is this was never a stackoverflow question and there was never an answer for it.
Try finding it. The LLM is already playing the game because it came up with that answer which is Fully Correct, Out of Thin Air.
Look, clearly the LLM can't play the game as well as a trained adept human, but it's definitely playing the game.
>The game is designing software to requirements. It's writing literature for a new era. It's creating X for A audience with N vauge unspecified needs -- where X is a complex product made of many parts, involving many people, with shifting and changing problems/solutions/requirements.
It can do all of this. It can talk like you and parrot exactly what your saying and also go into more detail and re-frame your words more eloquently.
What you're not getting is that all the things you mentioned the LLM can do in actuality to varying degrees to the point where it is in the "game." and at times it does better than us. Likely, you haven't even tried asking it yet.
I think if you're an expert in an area, this effect is easier to see through. You know where the github repo is, where the library example is, which ebooks there area -- etc. and you're mostly at-ease not using them and just writing the solution yourself.
These systems are not "fully correct" and not "out of thin area". They are trained on everything ever digitised, including the entire internet. They, in effect, find similar historical cases to your query and merge them. In many cases, for specific enough queries, the text is verbatim from an original source.
This is less revolutionary than the spreadsheet; it's less than google search. It's a speed boost to what was always the most wrote element to what we do. Yes, that often took us the longest -- and so some might be afraid that's what labour is -- but it isnt.
We never "added value" to products via what may be automated. Value is always a matter of the desire of the buyer of the products of our labour (vs. the supply) -- and making those products for those buyers was always what they wanted.
This will be clear to everyone pretty quickly, as with all tech, it's "magic" on the first encounter -- until the limitations are exposed.
I actually work in an area where what took 3mo last year, I can now do in maybe 3 days due to ChatGPT. But when it comes to providing my customers with that content, the value was always in how I provided it and what it did for them.
I think this makes my skills more valuable, not less. Since the quality of products will be even more stratified by experts who can quickly assemble what the customer needs from non-experts who have to fight through AI dialogue to get something generic.
The user needs to keep in mind that it can give plenty of false information. To make good use of it, the user needs to be able to verify if the returned information is useful, makes sense, compare with first hand sources, etc. In the hands of expert that is really powerful. In the hands of a layman (on the subject in question), they can generate a lot of crap and misunderstand what it is saying. It is similar to the idea that Democracy can be a great tool, but it needs an educated and participatory populous or it may generate a lot of headaches.
Of course you shouldn't think of it as magic. But, the experts self admit they don't fully understand how LLMs can produce such output. It's definitely emergent behavior. We've built something we don't understand, and although it's not magic, it's one of the closest things to it that can exist. Think about it. What is the closest thing in reality to magic? Literally, building something we can't understand is it.
It's one thing to think of something as magic, it's another thing to try to simplify a highly complex concept into a box. When elon musk got his rockets to space why were people so floored by decades old technology that he simply made cheaper?
But when someone makes AI that can literally do almost anything you ask it to everyone just suddenly says it's a simple stochastic parrot that can't do much?
I think it's obvious. It's because a rocket can't replace your job or your identity. If part of your skillset and identity is "master programmer" and suddenly there's a machine that can do better than you, the easiest thing to stop that machine is to first deny reality.
Experts in the area, including Hinton, the father of modern AI, self admit they don't fully understand what's going on but they think that LLMs know what they are talking about.
>These systems are not "fully correct" and not "out of thin area". They are trained on everything ever digitised, including the entire internet. They, in effect, find similar historical cases to your query and merge them. In many cases, for specific enough queries, the text is verbatim from an original source.
I never said the systems are fully correct. I said that for my specific example the answer is fully correct and out of thin air. No such question and answer pair exists on the internet. Find it and prove me wrong.
>This will be clear to everyone pretty quickly, as with all tech, it's "magic" on the first encounter -- until the limitations are exposed.
Except many experts are saying the exact opposite of what you're saying. I'm just parroting the experts..
>I actually work in an area where what took 3mo last year, I can now do in maybe 3 days due to ChatGPT. But when it comes to providing my customers with that content, the value was always in how I provided it and what it did for them.
So if they knew you were just copying and pasting their queries to chatgpt would they still care about the "how"? I doubt it.
However, in its current state users still have to know how to program in order to make good use of it. It will still give you lots of errors, but being able to get something close to your goal can save you a lot of time. Someone who does not know how to program will not be able to use these to put together a complex, useful and reliable system. It might change in the future, but these things are hard to predict.
Don't worry about this. You can get over the fear. I'm in the top 10% of stackoverflow users in terms of points and it's all because my stupidest questions from decades back gathered thousands of points from other stupid idiots like me. Who cares. Literally the line graph keeps climbing with no effort from me all from my dumbest questions. Just ask and don't worry about the criticism, you'll get a bit, but not too much.
>However, in its current state users still have to know how to program in order to make good use of it. It will still give you lots of errors, but being able to get something close to your goal can save you a lot of time. Someone who does not know how to program will not be able to use these to put together a complex, useful and reliable system. It might change in the future, but these things are hard to predict.
Of course. I think the thing I was trying to point out is the breadth of what chatgpt can do. So if you ask it to do a really in depth and detailed task it's likely to do it with flaws. That's not the point I was trying to emphasize, not the fact that it can't do any task with great depth but the fact that it can do ANY task. It has huge breadth.
So to bring it line with the direction of this thread. People were thinking about making special LLMs that refactor code to be unit testable. I mean we don't have to make special LLMs to do that because you can already ask chatgpt to do it already. That's the point.
Your example is so trivial, that there are definitely similar code examples. Maybe not word for word, but similar enough, that this is not really mindblowing "making things out of thin air" for me. It seems like a standard coding class example, so not surprising, that it also can make the unit tests.
It's not though. It doesn't print the values anymore, so the behavior isn't the same.
Refactoring isn't allowed to change behavior.
By definition all unit testable functions have to return data that can be asserted. You throw that data to IO it's not unit testable.
IO is testable via an integration tests. But not unit tests. Which is what my query exactly specified. I specified unit tests.
Replacing a function that does `print("hello world")` with a function that does `return "hello world"` isn't a valid way to make it unit testable.
People say, "oh but clients will have to get good at listing specs, that'll never happen". Like bruh the clients will obviously be using LLMs to make the specs too. Eventually the whole B2B workflow will just be LLMs talking to each other or something of the sort.
