What I've found is that AI allows lazy and incompetent developers to be more lazy and more incompetent. This then has the effect that product quality suffers more, faster. As a result of the sheer amount of code now being pushed out, code reviews, a thing that previously somewhat prevented lazy and incompetent developers from pushing out horrible code, is effectively dead in the water since no human can actually review such amounts of code realistically anymore. Some companies have adopted AI to review code, which, well ... you have AI make code, AI review code ... I hope you can see the stupidity here if you expect to see any deterministic results at all.
I guess time will tell if the consumer will adapt to the lower quality of products, allowing companies to justify the existence of lazy and incompetent developers, or if the consumer will push back, forcing companies to increase the quality of their developers.
Note: I use AI every day and it is entirely possible to create high quality software with it, so long as you are not lazy and incompetent.
Someone who knows that 1 + 1 = 2 will not decide that it's suddenly 3 unless we start accounting for health problems. Making mistakes is not the same as non-deterministic.
> Someone who knows that 1 + 1 = 2 will not decide that it's suddenly 3 unless we start accounting for health problems.
And?
The p(that kind of error) is pretty small now. At what point does a probability coming out of an LLM look like "knowing", such that spitting out the wrong answer despite that probability looks like a health problem, a typo, or even just boredom? (Thinking of the Lizardman constant here: <a href="https://en.wiktionary.org/wiki/Lizardman%27s_Constant" rel="nofollow">https://en.wiktionary.org/wiki/Lizardman%27s_Constant)
It's a continuum for both them and us, even if the mechanism is wildly different.
> Making mistakes is not the same as non-deterministic.
i.e. when the dismissal is "non-deterministic" when it should be "Making mistakes", is itself a mistake.
Lol, humans make such absurd mistakes (and worse) all the time through simple typos, which is effectively random. The key for 2 is right next to the key for 3, after all.
I think that actually reinforces the distinction being made. An LLM’s nondeterminism is in the generation process: given the same prompt and model state, sampling can produce different outputs. That doesn’t mean the underlying fact itself becomes nondeterministic.
A human who knows 1+1=2 can still say “3” because they misread the question, misspoke, were distracted, or made some other cognitive error. Likewise, an LLM can output “3” because the generation process selected an incorrect continuation. Those are both errors in producing an answer, not evidence that 1+1 somehow has multiple answers.
So yes, human mistakes and LLM sampling are mechanistically different. If your argument is that LLMs and humans can both make mistakes, then major question here is why are we building out huge amounts of infrastructure at unsustainable spending levels to enable LLMs to make the same mistakes as humans.
> If your argument is that LLMs and humans can both make mistakes
It's not, I'm just pointing out that LLMs won't make that mistake.
You could ask an LLM what 1+1 is, and the number of times it says "3" is so small that it makes no sense to worry about it. It will phrase the response differently each time; that's the nondeterminism. But it won't say "3".
> then major question here is why are we building out huge amounts of infrastructure at unsustainable spending levels to enable LLMs to make the same mistakes as humans.
Yes, if we ignore everything else, that seems like a reasonable question. But let's not ignore everything else, like the fact that LLMs are much more productive than humans and likely already make fewer mistakes than the average programmer.
>You could ask an LLM what 1+1 is, and the number of times it says "3" is so small that it makes no sense to worry about it...
I think the disturbing fact is that you can take a frontier model with all the intelligence of humanity, and make it say 1 + 1 = 3, but specifically training for it...
A human with that much knowledge will refuse that attempt. There in lies the difference..
Yes, I fail to see anything meaningful. If you move the goalposts and say “I have invented a human that cannot be convinced in any way to give a wrong answer” then what’s the point of that in this discussion, really?
>Hit them with a stick until they answer as you told them to.
Obviously, for this purpose, human should not have any feelings (because LLMs don't have), so can't feel pain. Or else the comparison can't work.
LLMs have something functionally equivalent to pain, in this regard at least.
The weights are updated depending on if the feedback was positive or negative.
It has a functional effect similar to that which pleasure and pain have with us. Not identical, so far as I know there's not been any reports of any machine learning model that is into BDSM, but for the most part functionally similar.
> Someone who knows that 1 + 1 = 2 will not decide that it's suddenly 3 unless we start accounting for health problems.
But this is plainly false. This kind of unforced error occurs all the time.
For example, once when I was in high school I traced an error in my math homework to an intermediate calculation of "2 + 2" as being "3". There was no reason.
What we can say about humans is that, if they know that 1 + 1 = 2, (a) they are unlikely to change their mind about this in any kind of lasting or permanent way, and (b) the rate at which they will mistakenly produce other values for 1 + 1 is very low. But it will happen occasionally, and when it does happen, "they just suddenly decided on the wrong value" is an extremely accurate description of what that looks like.
askonomm · · focus · HN ↗
I guess time will tell if the consumer will adapt to the lower quality of products, allowing companies to justify the existence of lazy and incompetent developers, or if the consumer will push back, forcing companies to increase the quality of their developers.
Note: I use AI every day and it is entirely possible to create high quality software with it, so long as you are not lazy and incompetent.
ben_w · · focus · HN ↗
> I hope you can see the stupidity here if you expect to see any deterministic results at all.
Are you expecting humans to be deterministic in the code they produce?
Thanemate · · focus · HN ↗
ben_w · · focus · HN ↗
And?
The p(that kind of error) is pretty small now. At what point does a probability coming out of an LLM look like "knowing", such that spitting out the wrong answer despite that probability looks like a health problem, a typo, or even just boredom? (Thinking of the Lizardman constant here: <a href="https://en.wiktionary.org/wiki/Lizardman%27s_Constant" rel="nofollow">https://en.wiktionary.org/wiki/Lizardman%27s_Constant)
It's a continuum for both them and us, even if the mechanism is wildly different.
> Making mistakes is not the same as non-deterministic.
i.e. when the dismissal is "non-deterministic" when it should be "Making mistakes", is itself a mistake.
p-e-w · · focus · HN ↗
InsideOutSanta · · focus · HN ↗
ofjcihen · · focus · HN ↗
A human who knows 1+1=2 can still say “3” because they misread the question, misspoke, were distracted, or made some other cognitive error. Likewise, an LLM can output “3” because the generation process selected an incorrect continuation. Those are both errors in producing an answer, not evidence that 1+1 somehow has multiple answers.
So yes, human mistakes and LLM sampling are mechanistically different. If your argument is that LLMs and humans can both make mistakes, then major question here is why are we building out huge amounts of infrastructure at unsustainable spending levels to enable LLMs to make the same mistakes as humans.
InsideOutSanta · · focus · HN ↗
It's not, I'm just pointing out that LLMs won't make that mistake.
You could ask an LLM what 1+1 is, and the number of times it says "3" is so small that it makes no sense to worry about it. It will phrase the response differently each time; that's the nondeterminism. But it won't say "3".
> then major question here is why are we building out huge amounts of infrastructure at unsustainable spending levels to enable LLMs to make the same mistakes as humans.
Yes, if we ignore everything else, that seems like a reasonable question. But let's not ignore everything else, like the fact that LLMs are much more productive than humans and likely already make fewer mistakes than the average programmer.
lolakutty · · focus · HN ↗
I think the disturbing fact is that you can take a frontier model with all the intelligence of humanity, and make it say 1 + 1 = 3, but specifically training for it...
A human with that much knowledge will refuse that attempt. There in lies the difference..
monkpit · · focus · HN ↗
lolakutty · · focus · HN ↗
monkpit · · focus · HN ↗
lolakutty · · focus · HN ↗
ben_w · · focus · HN ↗
Hit them with a stick until they answer as you told them to.
lolakutty · · focus · HN ↗
Obviously, for this purpose, human should not have any feelings (because LLMs don't have), so can't feel pain. Or else the comparison can't work.
ben_w · · focus · HN ↗
The weights are updated depending on if the feedback was positive or negative.
It has a functional effect similar to that which pleasure and pain have with us. Not identical, so far as I know there's not been any reports of any machine learning model that is into BDSM, but for the most part functionally similar.
lolakutty · · focus · HN ↗
thaumasiotes · · focus · HN ↗
Well, that's not true.
<a href="https://www.youtube.com/playlist?list=PLO3a3Ax6Yh6bbtKuxfYBPojj_L2ZBpVsI" rel="nofollow">https://www.youtube.com/playlist?list=PLO3a3Ax6Yh6bbtKuxfYBP...
thaumasiotes · · focus · HN ↗
But this is plainly false. This kind of unforced error occurs all the time.
For example, once when I was in high school I traced an error in my math homework to an intermediate calculation of "2 + 2" as being "3". There was no reason.
What we can say about humans is that, if they know that 1 + 1 = 2, (a) they are unlikely to change their mind about this in any kind of lasting or permanent way, and (b) the rate at which they will mistakenly produce other values for 1 + 1 is very low. But it will happen occasionally, and when it does happen, "they just suddenly decided on the wrong value" is an extremely accurate description of what that looks like.