4 ms·

>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 a

by byby 3y ago

>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.

mjburgess 3y ago
> Fully Correct, Out of Thin Air 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.
- mchaver 3y ago
  I agree. LLMs are very impressive, but it isn't helpful to think of them of magic. LLMs are a great tool to explore and remix the body of human knowledge on the internet (limited to what it has been trained on). 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.
  - byby 3y ago
    > I agree. LLMs are very impressive, but it isn't helpful to think of them of magic. LLMs are a great tool to explore and remix the body of human knowledge on the internet (limited to what it has been trained on). 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.
    - mjburgess 3y ago
      > the experts self admit they don't fully understand how LLMs can produce such output Well I take myself to be an expert in this area, and I think it's fairly obvious how they work. Many of these so-called "Experts" are sitting on the boards of commercial companies with vested interests in presenting this technology as revolutionary. Indeed, much of what has been said recently in the media is little more than political and economic power plays disguised as philosophical musings. A statistical AI system is a function `answer = f(question; weights)`. The `answer` obtains apparent "emergent" properties such as "suitability for basic reasoning tasks" when used by human operators. But the function does not actually have those properties. It's a trick -- the weights are summaries of unimaginable number of similar cases, and the function is little more than "sample from those cases and merge". Properties of the output of this function obtain trivially in the way that all statistical functions generate increasingly useful output: by having increasingly relevant weights. If you model linear data with just y = ax then as soon as you shift to "y = ax + b" you'll see the "emergent property" that the output is now sensitive to a background bias, b. Emergence is an ontological phenomenon concerning how `f` would be reaslised by a physical system. In this case any physical system implementing `f` shows no such emergence. Rather the output of `f` has a "shift in utility" as the properties of the data its training on, as summarised by the weights, "shifts in utilty". In other words, if you train a statistical system on everything ever written by billions of people over decades, then you will in fact see "domains of applicability" increases, just as much as when you shift from a y=ax model to a y=ax+b. To make this as simple as I can: statistical AI is just a funnel. ChatGPT is a slightly better funnel, but moreso, it's had the ocean pass through it. Much of its apparent properties are illusary, and much of the press around it puts in cases where it appears to work and claims "look it works!". This is pseudoscience -- if you want to test a hypothesis of ChatGPT, find all the cases where it doesnt work -- and you will find that in the cases where it does there was some "statistical shortcut" taken
mchaver 3y ago
I think this is one of the killer applications of LLMs, a friendly Stack Overflow where you can ask any programming question you want with out fear of being reprimanded. Of course, this capability in LLM is probably due to the terseness of Stack Overflow and the large database of code in Github. 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.
- byby 3y ago
  > fear of being reprimanded. 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.
  - mchaver 3y ago
    It is a frequent complaint I have seen from new users. I do think for the purpose of Stack Overflow it does make sense to weed out questions that have already been answered and remove poorly formed ones. It's just that ChatGPT for programming questions often works better than trying to look it up in Stack Overflow so now I recommend it as an additional tool. You can ask questions and refine them without bothering random people on the internet.
deleted 3y ago
[deleted]
hutzlibu 3y ago
"The problem is this was never a stackoverflow question and there was never an answer for it." 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.
- byby 3y ago
  >Maybe not word for word, but similar enough Find one. Dated before 2021. In fact, according to the theory that it's statistical parroting there should be multiple examples of for loops printing out numbers being converted to unit testable functions because AI needs multiple examples of it to form the correct model. Find one. And it doesn't have to be from stack overflow either. Just a question and answer data point.
ahtihn 3y ago
> Fully Correct 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.
- byby 3y ago
  It is. There is literally zero other way to make that function unit testable. What are you gonna compare that data with in a test if it's thrown into IO? 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.
  - ahtihn 3y ago
    That doesn't change the fact that it's not a valid refactoring. If you can't make it unit testable without changing behavior, then it should tell you that. 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.
    - byby 3y ago
      Alright fine, I can concede to this. ChatGPT should not have given me the best alternative but it should have given me the exact technically correct answer. You're right.

by byby 3y ago

mjburgess 3y ago

> Fully Correct, Out of Thin Air 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.

mchaver 3y ago
I agree. LLMs are very impressive, but it isn't helpful to think of them of magic. LLMs are a great tool to explore and remix the body of human knowledge on the internet (limited to what it has been trained on). 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.
- byby 3y ago
  > I agree. LLMs are very impressive, but it isn't helpful to think of them of magic. LLMs are a great tool to explore and remix the body of human knowledge on the internet (limited to what it has been trained on). 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.
  - mjburgess 3y ago
    > the experts self admit they don't fully understand how LLMs can produce such output Well I take myself to be an expert in this area, and I think it's fairly obvious how they work. Many of these so-called "Experts" are sitting on the boards of commercial companies with vested interests in presenting this technology as revolutionary. Indeed, much of what has been said recently in the media is little more than political and economic power plays disguised as philosophical musings. A statistical AI system is a function `answer = f(question; weights)`. The `answer` obtains apparent "emergent" properties such as "suitability for basic reasoning tasks" when used by human operators. But the function does not actually have those properties. It's a trick -- the weights are summaries of unimaginable number of similar cases, and the function is little more than "sample from those cases and merge". Properties of the output of this function obtain trivially in the way that all statistical functions generate increasingly useful output: by having increasingly relevant weights. If you model linear data with just y = ax then as soon as you shift to "y = ax + b" you'll see the "emergent property" that the output is now sensitive to a background bias, b. Emergence is an ontological phenomenon concerning how `f` would be reaslised by a physical system. In this case any physical system implementing `f` shows no such emergence. Rather the output of `f` has a "shift in utility" as the properties of the data its training on, as summarised by the weights, "shifts in utilty". In other words, if you train a statistical system on everything ever written by billions of people over decades, then you will in fact see "domains of applicability" increases, just as much as when you shift from a y=ax model to a y=ax+b. To make this as simple as I can: statistical AI is just a funnel. ChatGPT is a slightly better funnel, but moreso, it's had the ocean pass through it. Much of its apparent properties are illusary, and much of the press around it puts in cases where it appears to work and claims "look it works!". This is pseudoscience -- if you want to test a hypothesis of ChatGPT, find all the cases where it doesnt work -- and you will find that in the cases where it does there was some "statistical shortcut" taken

mchaver 3y ago

I think this is one of the killer applications of LLMs, a friendly Stack Overflow where you can ask any programming question you want with out fear of being reprimanded. Of course, this capability in LLM is probably due to the terseness of Stack Overflow and the large database of code in Github. 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.

byby 3y ago
> fear of being reprimanded. 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.
- mchaver 3y ago
  It is a frequent complaint I have seen from new users. I do think for the purpose of Stack Overflow it does make sense to weed out questions that have already been answered and remove poorly formed ones. It's just that ChatGPT for programming questions often works better than trying to look it up in Stack Overflow so now I recommend it as an additional tool. You can ask questions and refine them without bothering random people on the internet.

deleted 3y ago

[deleted]

hutzlibu 3y ago

"The problem is this was never a stackoverflow question and there was never an answer for it." 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.

byby 3y ago
>Maybe not word for word, but similar enough Find one. Dated before 2021. In fact, according to the theory that it's statistical parroting there should be multiple examples of for loops printing out numbers being converted to unit testable functions because AI needs multiple examples of it to form the correct model. Find one. And it doesn't have to be from stack overflow either. Just a question and answer data point.

ahtihn 3y ago

> Fully Correct 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.

byby 3y ago
It is. There is literally zero other way to make that function unit testable. What are you gonna compare that data with in a test if it's thrown into IO? 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.
- ahtihn 3y ago
  That doesn't change the fact that it's not a valid refactoring. If you can't make it unit testable without changing behavior, then it should tell you that. 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.
  - byby 3y ago
    Alright fine, I can concede to this. ChatGPT should not have given me the best alternative but it should have given me the exact technically correct answer. You're right.