You are saying a lot of things that sound good to you without much grounding. You claiming this is a “widespread and significant issue” is going to need some backing up, because I may be cautious about not claiming more knowledge than I have, but I know enough to tell you it’s not particularly well understood, nobody is in a position to predict the workarounds and it’s by no means the only major issue. The social media answer would be to go look it up, but it’s the weekend and I refuse to let you give me homework. I have better things to do today.
That’s the problem with being cautious about things. Not everybody has to. Not everybody knows they should or when. I don’t know if you’re dunning kruger incarnate or an expert talking down to me (pretty sure it’s not the second, though).
And I’m pretty sure of that because yeah, it is an infinite doomsday slippery slope scenario. That I happen to know well enough to not have to be cautious about not having done all the reading.
I mean, your original scenario is that. You’re sort of walking it back here where it’s just some effect, not the endgame. And because now you’re not saying “if AI actually replaces programmers wholesale” anymore the entire calculation is different. It goes back to my original point: What data will AI use to train? The same data they have now. Because it will NOT in fact replace programmers wholesale and the data is not fungible, so there still will be human-generated code to train on (and whatever the equivalent high enough quality hybrid or machine-generated code is that clears the bar).
AI has a problem with running out of (good) data to train on, but that only tells you there is a hard limit to the current processes, which we already knew. Whether current AI is as good as it’s going to get or there is a new major breaktrough in training or model design left to be discovered is anybody’s guess.
If there is one, then the counter gets reset and we will see how far that can take the technology, I suppose. If there is not, then we know how far we’ve taken it and we can see how far it’s growing and how quickly it’s plateauing. There is no reason to believe it will get worse, though.
Will companies leap into it too quickly? They already have. We’re talking about a thing that’s in the past. But the current iteration of the tech is incapable of removing programmers from the equation. At most it’s a more practical reference tool and a way to blast past trivial tasks. There is no doomsday loop to be had unless the landscape shifts signfiicantly, despite what AI shills have been trying to sell people. This is what pisses me off the most about this conversation, the critics are buying into the narrative of the shills aggressively in ways that don’t really hold up to scrutiny for either camp.
I appreciate that you didn’t mean to say what you said, but words mean things. I can only respond to what you say, not what you meant.
Especially here, where the difference entirely changes whether you’re right or not.
Because no, “less human code” doesn’t mean “less AI training”. It could mean a slowdown in how fast you can expand the training dataset, but again, old code doesn’t disappear just because you used it for training before. You don’t need a novel training dataset to train. The same data we have plus a little bit of new data is MORE training data, not less.
And less human code is absolutely not the same thing as “new human code will stop being created”. That’s not even a slip of the tongue, those are entirely different concepts.
There is a big difference between arguing that the pace of improvement will slow down (which is probably true even without any data scarcity) and saying that a lack of new human created code will bring AI training to a halt. That is flat out not a thing.
That this leads to “less developments and advancements in programming in general” is also a wild claim. How many brilliant programmers need to get replaced by AI before that’s true? Which fields are generating “developments and advancements in programming”? Are those fields being targeted by AI replacements? More or less than other fields? Does that come from academia or the private sector? Is the pace of development slowing down specifially in that area? Is AI generating “developments and advancements” of its own? Is it doing so faster or slower than human coders? Not at all?
People say a lot of stuff here. Again, on both sides of the aisle. If you know the answers to any of those questions you shouldn’t be arguing on the Internet, you should be investing in tech stock. Try to do something positive with the money after, too.
I’d say it’s more likely you’re just wildly extrapolating from relatively high level observations, though.
Hm. That’s rolling the argument back a few steps there. None of the stuff we’ve talked about in the past few posts has anything to do with the impact of AI-on-AI training.
I mean, you could stretch the idea and argue that there is a filtering problem to be solved or whatever, but that aside everything I’m saying would still be true if AI training exploded any time it’s accidentally given a “Hello world” written by a machine.
But that point is not the same as LLMs degrading when trained on its own data.
Again, it may be the same as the problem of “how do you separate AI generated data from human generated data”, so a filtering issue.
But it’s not the same as the problem of degradation due to self-training. Which I’m fairly sure you’re also misrepresenting, but I REALLY don’t want to get into that.
But hey, if you don’t want to keep talking about this that’s your prerogative. I just want to make it very clear that the reasons why that’s… just not a thing have nothing to do with training on AI-generated data. Your depiction is a wild extrapolation even if you were right about how poisonous AI-generated data is.
You are saying a lot of things that sound good to you without much grounding. You claiming this is a “widespread and significant issue” is going to need some backing up, because I may be cautious about not claiming more knowledge than I have, but I know enough to tell you it’s not particularly well understood, nobody is in a position to predict the workarounds and it’s by no means the only major issue. The social media answer would be to go look it up, but it’s the weekend and I refuse to let you give me homework. I have better things to do today.
That’s the problem with being cautious about things. Not everybody has to. Not everybody knows they should or when. I don’t know if you’re dunning kruger incarnate or an expert talking down to me (pretty sure it’s not the second, though).
And I’m pretty sure of that because yeah, it is an infinite doomsday slippery slope scenario. That I happen to know well enough to not have to be cautious about not having done all the reading.
I mean, your original scenario is that. You’re sort of walking it back here where it’s just some effect, not the endgame. And because now you’re not saying “if AI actually replaces programmers wholesale” anymore the entire calculation is different. It goes back to my original point: What data will AI use to train? The same data they have now. Because it will NOT in fact replace programmers wholesale and the data is not fungible, so there still will be human-generated code to train on (and whatever the equivalent high enough quality hybrid or machine-generated code is that clears the bar).
AI has a problem with running out of (good) data to train on, but that only tells you there is a hard limit to the current processes, which we already knew. Whether current AI is as good as it’s going to get or there is a new major breaktrough in training or model design left to be discovered is anybody’s guess.
If there is one, then the counter gets reset and we will see how far that can take the technology, I suppose. If there is not, then we know how far we’ve taken it and we can see how far it’s growing and how quickly it’s plateauing. There is no reason to believe it will get worse, though.
Will companies leap into it too quickly? They already have. We’re talking about a thing that’s in the past. But the current iteration of the tech is incapable of removing programmers from the equation. At most it’s a more practical reference tool and a way to blast past trivial tasks. There is no doomsday loop to be had unless the landscape shifts signfiicantly, despite what AI shills have been trying to sell people. This is what pisses me off the most about this conversation, the critics are buying into the narrative of the shills aggressively in ways that don’t really hold up to scrutiny for either camp.
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I appreciate that you didn’t mean to say what you said, but words mean things. I can only respond to what you say, not what you meant.
Especially here, where the difference entirely changes whether you’re right or not.
Because no, “less human code” doesn’t mean “less AI training”. It could mean a slowdown in how fast you can expand the training dataset, but again, old code doesn’t disappear just because you used it for training before. You don’t need a novel training dataset to train. The same data we have plus a little bit of new data is MORE training data, not less.
And less human code is absolutely not the same thing as “new human code will stop being created”. That’s not even a slip of the tongue, those are entirely different concepts.
There is a big difference between arguing that the pace of improvement will slow down (which is probably true even without any data scarcity) and saying that a lack of new human created code will bring AI training to a halt. That is flat out not a thing.
That this leads to “less developments and advancements in programming in general” is also a wild claim. How many brilliant programmers need to get replaced by AI before that’s true? Which fields are generating “developments and advancements in programming”? Are those fields being targeted by AI replacements? More or less than other fields? Does that come from academia or the private sector? Is the pace of development slowing down specifially in that area? Is AI generating “developments and advancements” of its own? Is it doing so faster or slower than human coders? Not at all?
People say a lot of stuff here. Again, on both sides of the aisle. If you know the answers to any of those questions you shouldn’t be arguing on the Internet, you should be investing in tech stock. Try to do something positive with the money after, too.
I’d say it’s more likely you’re just wildly extrapolating from relatively high level observations, though.
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Hm. That’s rolling the argument back a few steps there. None of the stuff we’ve talked about in the past few posts has anything to do with the impact of AI-on-AI training.
I mean, you could stretch the idea and argue that there is a filtering problem to be solved or whatever, but that aside everything I’m saying would still be true if AI training exploded any time it’s accidentally given a “Hello world” written by a machine.
deleted by creator
But that point is not the same as LLMs degrading when trained on its own data.
Again, it may be the same as the problem of “how do you separate AI generated data from human generated data”, so a filtering issue.
But it’s not the same as the problem of degradation due to self-training. Which I’m fairly sure you’re also misrepresenting, but I REALLY don’t want to get into that.
But hey, if you don’t want to keep talking about this that’s your prerogative. I just want to make it very clear that the reasons why that’s… just not a thing have nothing to do with training on AI-generated data. Your depiction is a wild extrapolation even if you were right about how poisonous AI-generated data is.