5 ms·

Is this the same "materially better" that the present version gives you when you tell it the auto-generated code it produced for you is a pile of shit, and it p

Is this the same "materially better" that the present version gives you when you tell it the auto-generated code it produced for you is a pile of shit, and it presents you with a new version that it promises "fixes the problems", but instead introduces more. AI is and always will be supremely confident at being supremely wrong. Its the nature of the beast. Some things are just best left to real-life humans.

epgui 3y ago
I mean, it imitates humans. Have you met humans? If you want something real smart, what you need is a Casio calculator.
bheadmaster 3y ago
Humans, in isolation, also tend to be supremely confident at being supremely wrong. Having others around and hearing their criticism of our opinions is a crucial factor of converging towards the truth. Perhaps a system of mutually-criticising LLMs is what is needed to avoid confidently incorrect answers? Give them all a task to find errors in each others' outputs, and only return the output to the user when all of them agree on correctness.
- traceroute66 3y ago
  > Humans, in isolation, tend to also be supremely confident at being supremely wrong At least most humans will know that most modern programming languages have built-in basic math functionality. When I asked GPT-4 to write me a basic program in Go, it tried to tell me that a basic math function was not available in Go stdlib and proceeded to write some DIY horror-show of code as a substitute. On the same occasion it also took great delight in importing obsolete libraries, and using deprecated functions. And for the icing on the cake, the code it generated did not even compile ! Every time I corrected it, it returned with fresh code and a supremely confident assertion that "this fixes the problems identified". It was wrong, of course. Every, single, time. All basic errors that even a Junior programmer fresh out of school would not make.
  - Closi 3y ago
    What was the basic program you asked for? Hard to evaluate if this is a fair test without knowing what you asked of it.
    - traceroute66 3y ago
      > Hard to evaluate if this is a fair test without knowing what you asked of it. Oh, right, the old "can't possibly be the AI, must be the user" claim. The same excuse certain car manufactuers use when their car gets confused by shadows in the sun. Give me a break.
      - Karunamon 3y ago
        I grow skeptical of these angry anti-AI posts when they lack such easily-provided evidence. There is a share button on every chat on ChatGPT. You could just share the conversation here and spare us all a lot of pointless back-and-forth. https://chat.openai.com/share/371863ec-edbd-4454-8b19-3820352e1f0f https://chat.openai.com/share/371863ec-edbd-4454-8b19-382035... Here is an example of the kind of scripting I do regularly with ChatGPT. I cannot speak to its capabilities with Go, but it is quite proficient at Python.
        sgarland 3y ago
        OK, but (I mean this sincerely and in kindness) this isn’t a particularly challenging task. Useful, yes, and the type of thing Python is marvelous at, but not overly difficult. If you start getting into more esoteric edges of Python, like SharedMemory, ChatGPT quickly falls apart. Or, more relevant to your example, using carriage return to overwrite text for a progress indicator – works great, very simple. Until you try to use it through subprocess. To ChatGPT’s credit, it eventually came up with using pty, which with some massaging, I got to work. I’m not saying it isn’t useful – far from it. It’s just that on anything modestly complicated, you sometimes have to spend more time fiddling with the prompt than if you just sat down and wrote the code. Another example that comes to mind was implementing a B+tree in pure Python. I know how they work, and wanted to see if ChatGPT could figure it out. You’d think so, right? But no, it kept getting stuck on node splits.
Closi 3y ago
> AI is and always will be supremely confident at being supremely wrong. Its the nature of the beast. Some things are just best left to real-life humans. Sounds like you are also supremely confident at making predictions! AI's are already less confident and more cautious than their previous iterations, so I'm not sure how you can confidently conclude it's entirely unsolvable.
- traceroute66 3y ago
  > Sounds like you are also supremely confident at making predictions! This is not my first rodeo as they say. I've seen it before. I've seen incredibly intelligent people before going all-in on the latest AI hype. And it always ends the same way, badly. The hype dies down and then returns again when the next iteration comes around.
  - Closi 3y ago
    "I don't think it worked in the past, therefore it can't work in the future" isn't a particularly compelling argument considering the number of breakthroughs in the last 3-5 years.
infecto 3y ago
Its funny, you see comments like this and I think people confidently give very specific hurdles to them for what these models should be able to do. I have a rather large spend across the universe of models and I think compared to a year ago, its amazing what is possible. If we continue anywhere close to this speed, it will be amazing what will be possible a year from now.
- Jensson 3y ago
  > If we continue anywhere close to this speed, it will be amazing what will be possible a year from now. People said that a year ago and look today, still gpt-4 but now its turbo. So if we keep that pace in a year we will be at roughly gpt-4 but turbo++, better but not a giant leap.
  - infecto 3y ago
    Massive cost decreases. Price increases. Forced json. Seems pretty incredible to me. Gpt4 on release a year ago was completely different.
freitzkriesler2 3y ago
Agreed. Algorithmic slight of hand. I'm getting tired of these toys.
delegate 3y ago
It's easy to confuse text generation with thinking. I found that if I treat it as a program that can generate text, it's a lot easier to write the prompt that generates code that's more or less what I need. It can do the boring stuff pretty easily, like generate boilerplate code or settings files. It can explain technical issues better than googling. It also helps that I use a functional language with immutable data structures and the code it generates is either good or good enough to give me an idea on how to move forward. Once you lower your expectations and use it for what it is, it's a very powerful tool.
throwaway4aday 3y ago
I wouldn't say always. The way the "confidently wrong" answers make sense to me are in the context of asking people trick questions that seem to have an obvious answer but actually require more deliberate thought to get right. What happens in that case is you are engaging the part of the brain that has simply memorized a fact or developed a quick but not always accurate heuristic which it then applies to the question that seems to meet the criteria and it results in a confidently wrong answer given by a real meat and bones human. LLMs right now are essentially a big collection of facts and heuristics with the ability to find the often right association between input and the applicable output. Everyone is working on building the other thing that does the careful step by step reasoning and error checking, the so called System 2, and if brains can tell us anything about the implementation of such a system it is that it's made of the same stuff that System 1 is but arranged and controlled in such a way that it produces more reliable answers. *edit: I should specify that when I say LLMs are a collection of facts and heuristics I mean they are a collection of those things encoded as language which itself has been encoded as vectors of floats which in turn have modified the weights of the network to produce yet another encoding. I don't mean that the facts and heuristics are stored in a lookup table or as procedures.

epgui 3y ago

I mean, it imitates humans. Have you met humans? If you want something real smart, what you need is a Casio calculator.

bheadmaster 3y ago

Humans, in isolation, also tend to be supremely confident at being supremely wrong. Having others around and hearing their criticism of our opinions is a crucial factor of converging towards the truth. Perhaps a system of mutually-criticising LLMs is what is needed to avoid confidently incorrect answers? Give them all a task to find errors in each others' outputs, and only return the output to the user when all of them agree on correctness.

traceroute66 3y ago
> Humans, in isolation, tend to also be supremely confident at being supremely wrong At least most humans will know that most modern programming languages have built-in basic math functionality. When I asked GPT-4 to write me a basic program in Go, it tried to tell me that a basic math function was not available in Go stdlib and proceeded to write some DIY horror-show of code as a substitute. On the same occasion it also took great delight in importing obsolete libraries, and using deprecated functions. And for the icing on the cake, the code it generated did not even compile ! Every time I corrected it, it returned with fresh code and a supremely confident assertion that "this fixes the problems identified". It was wrong, of course. Every, single, time. All basic errors that even a Junior programmer fresh out of school would not make.
- Closi 3y ago
  What was the basic program you asked for? Hard to evaluate if this is a fair test without knowing what you asked of it.
  - traceroute66 3y ago
    > Hard to evaluate if this is a fair test without knowing what you asked of it. Oh, right, the old "can't possibly be the AI, must be the user" claim. The same excuse certain car manufactuers use when their car gets confused by shadows in the sun. Give me a break.
    - Karunamon 3y ago
      I grow skeptical of these angry anti-AI posts when they lack such easily-provided evidence. There is a share button on every chat on ChatGPT. You could just share the conversation here and spare us all a lot of pointless back-and-forth. https://chat.openai.com/share/371863ec-edbd-4454-8b19-3820352e1f0f https://chat.openai.com/share/371863ec-edbd-4454-8b19-382035... Here is an example of the kind of scripting I do regularly with ChatGPT. I cannot speak to its capabilities with Go, but it is quite proficient at Python.
      - sgarland 3y ago
        OK, but (I mean this sincerely and in kindness) this isn’t a particularly challenging task. Useful, yes, and the type of thing Python is marvelous at, but not overly difficult. If you start getting into more esoteric edges of Python, like SharedMemory, ChatGPT quickly falls apart. Or, more relevant to your example, using carriage return to overwrite text for a progress indicator – works great, very simple. Until you try to use it through subprocess. To ChatGPT’s credit, it eventually came up with using pty, which with some massaging, I got to work. I’m not saying it isn’t useful – far from it. It’s just that on anything modestly complicated, you sometimes have to spend more time fiddling with the prompt than if you just sat down and wrote the code. Another example that comes to mind was implementing a B+tree in pure Python. I know how they work, and wanted to see if ChatGPT could figure it out. You’d think so, right? But no, it kept getting stuck on node splits.

Closi 3y ago

> AI is and always will be supremely confident at being supremely wrong. Its the nature of the beast. Some things are just best left to real-life humans. Sounds like you are also supremely confident at making predictions! AI's are already less confident and more cautious than their previous iterations, so I'm not sure how you can confidently conclude it's entirely unsolvable.

traceroute66 3y ago
> Sounds like you are also supremely confident at making predictions! This is not my first rodeo as they say. I've seen it before. I've seen incredibly intelligent people before going all-in on the latest AI hype. And it always ends the same way, badly. The hype dies down and then returns again when the next iteration comes around.
- Closi 3y ago
  "I don't think it worked in the past, therefore it can't work in the future" isn't a particularly compelling argument considering the number of breakthroughs in the last 3-5 years.

infecto 3y ago

Its funny, you see comments like this and I think people confidently give very specific hurdles to them for what these models should be able to do. I have a rather large spend across the universe of models and I think compared to a year ago, its amazing what is possible. If we continue anywhere close to this speed, it will be amazing what will be possible a year from now.

Jensson 3y ago
> If we continue anywhere close to this speed, it will be amazing what will be possible a year from now. People said that a year ago and look today, still gpt-4 but now its turbo. So if we keep that pace in a year we will be at roughly gpt-4 but turbo++, better but not a giant leap.
- infecto 3y ago
  Massive cost decreases. Price increases. Forced json. Seems pretty incredible to me. Gpt4 on release a year ago was completely different.

freitzkriesler2 3y ago

Agreed. Algorithmic slight of hand. I'm getting tired of these toys.

delegate 3y ago

It's easy to confuse text generation with thinking. I found that if I treat it as a program that can generate text, it's a lot easier to write the prompt that generates code that's more or less what I need. It can do the boring stuff pretty easily, like generate boilerplate code or settings files. It can explain technical issues better than googling. It also helps that I use a functional language with immutable data structures and the code it generates is either good or good enough to give me an idea on how to move forward. Once you lower your expectations and use it for what it is, it's a very powerful tool.

throwaway4aday 3y ago

I wouldn't say always. The way the "confidently wrong" answers make sense to me are in the context of asking people trick questions that seem to have an obvious answer but actually require more deliberate thought to get right. What happens in that case is you are engaging the part of the brain that has simply memorized a fact or developed a quick but not always accurate heuristic which it then applies to the question that seems to meet the criteria and it results in a confidently wrong answer given by a real meat and bones human. LLMs right now are essentially a big collection of facts and heuristics with the ability to find the often right association between input and the applicable output. Everyone is working on building the other thing that does the careful step by step reasoning and error checking, the so called System 2, and if brains can tell us anything about the implementation of such a system it is that it's made of the same stuff that System 1 is but arranged and controlled in such a way that it produces more reliable answers. *edit: I should specify that when I say LLMs are a collection of facts and heuristics I mean they are a collection of those things encoded as language which itself has been encoded as vectors of floats which in turn have modified the weights of the network to produce yet another encoding. I don't mean that the facts and heuristics are stored in a lookup table or as procedures.