Theres a forming deeply uninformed / misinformed loud group of people who desperately want to hate on AI being used for anything, even in scenarios where its actually perfectly good at its job.
Even when extremely seasoned professionals with waaaay more experience are stating “its a perfectly fine tool” these folks somehow cannot wrap their head around the possibility someone like fucking Linus Torvalds might know better than them.
Imagine having the utter balls to assert you know better than Torvalds about whether a technology is good or not 😐
Meanwhile those of us actually in the field have known for over a year now that the tools are pretty good now and very useful.
The fact that some people don’t want to see AI being used to generate code does not make them dumb or misinformed. They may be informed, and simply reached a different conclusion than you.
Also, you have to consider that some of the most popular tools are controlled by a select few and that concentration can be used to control markets and users, reinforce biases, and shape what gets encouraged.
Finally, the use of AI can easily feed into a maximalist view of “produce more and faster” that has direct consequences like layoffs, increased cost of hardware, increased resource usage, increased environmental impact.
The majority of naysayers are touting deeply misinformed falsifities.
Its not a matter of guessing their intent, theyre quite outspoken on why they think its bad.
And the majority of them tout talking points that are years out of date at best.
Some valid points get brought up, but most of the points tend to be in the “these armchair devs have zero goddamn clue about how things actually are, they just are bandwagoning into an angry mob”
All major operating systems and software has been around before AI. Sure, AI might have some uses, even good ones but not enough to warrant all the negative effects it and all those datacenters running the models have.
Some people just don’t care about the negative effects and Torvalds is obviously one of them.
I hate AI as in, I hate Microsoft, Google, OpenAI, etc. for pushing it everywhere, being an oligopoly, and using it for mass surveillance. But AI as a tool is fantastic for some tasks
To be fair, many have had their workplace enshittified by AI, or managers with AI psychosis.
Or fired over it.
Maybe they have family that’s gone down the ChatGPT rabbit hole. I do.
…I think it’s understandable for people to be pissed, and take an absolutionist position, as the leads who see it as a tool seem to be few and far between.
Every single generative tool is built on theft. No one has a training database that only contains code they were given legal permission to use for that purpose. Moreover, if these tools were trained on any GNU GPL code, then the tool and all of its output should also be GNU GPL:
if you distribute a derivative work or modification, you must provide the source code to those recipients under the same or equivalent license terms
Copying GNU GPL code without licensing your derivative code as GNU GPL and publishing it where it’s publicly accessible, and then using your derivative code to generate profit for a corporation, is definitely theft. Just because it’s open source doesn’t mean you can just do whatever you want with it, the original programmer still has rights over the code they wrote.
This is like saying anyone who has watched a Disney movie now owes Disney a check any time they illustrate anything, even entirely unrelated, because they’ve learned from their commercial products in the past. If they don’t pay Disney, then their creations are based on theft.
Fundamentally that’s a purely ethical decision about whether a machine doing something should be treated with the same understanding the same as a human would. But we don’t consider remembering something with a neurochemical storage the samd as taking a picture of something even if they’re basically the same.
That’s splitting hairs on definitions, with no change in meaning. It’s still not copying the data, and is far closer to a person learning than to a picture taken by a camera.
It is actually not like a person learning at all. The only way you could believe this is if you have no grasp of the mathematics that are the basis of the multi-dimensional statistical analysis which is neural network training, and haven’t bothered to do any reading on it.
There’s a reason I referenced curve fitting.
Here is a better explanation than I could give, by someone who knows better than me:
It actually is, but you want it to be literally the same. Given the options of what it can be compared to, learning is the most appropriate, which is exactly why it’s named Machine Learning. The only reason you could believe otherwise is if you have no concept of language, and believe that it must be literally identical to a person learning in order to use the same verbiage.
It does not; which is the reason why it’s called learning. It’s the closest, and most accurate approximation, regardless of the mathematical operations on which it’s based.
Digital reproduction is digital reproduction no matter how many extra steps are added to the reproduction process. It’s just an algorithm that sorts through a collection of stored data to find specific pieces of data which best fit the keywords supplied by the user, then regurgitates the results that are the best match based on correlation.
In spite of common meaning overlap and popular metaphors, the human brain is not a computer. Ask any neurologist.
Thank you for the opinion. If there’s a trial, and if you’re called as an expert witness, I’m sure they’ll consider your opinion, and have a fun debate on whether a human brain being similar to a computer is relevant in any way.
In the meantime, the technology exists, is useful and the results are no more theft than creating piñatas from newspaper clippings is theft from the newspaper.
Given that you’ve presumably looked at open source code in the past, and got more proficient (however marginally) as a result, does all your future code now belong to the GPL?
This comparison is invalid. Training a neural network algorithm is not equivalent to human learning. We are talking about data stored in machine learning models owned and controlled by multi-billion dollar corporations.
It has already been demonstrated multiple times that original training data can be reproduced completely from models, so yes, they are data storage systems. When they reproduce code which they have previously stored, even only in part, that is a derivative work. Adding extra steps to the transcribing process doesn’t make it any less a copy of the original.
Only when the data in question is so obscure to the point that the original source is the only place in which the data exists; which is neither common, nor useful to regurgitate, in practice.
It’s like if someone asked an artist to draw Trump, and then they did. It’s not that they studied what he looks like, but they’re familiar with that walking pusbag and there’s only the one, so it’s going to be a recognizable drawing.
Training a neural network algorithm is not equivalent to human learning
That’s a stretch, since we don’t know exactly how human learning works. Yes we have more than just that one mechanism at work obviously, but that’s not to say we’re not using the same or similar method as part of our learning process.
original training data can be reproduced completely from models
So can a few savants, more if you take into account those with special training, etc. The ability to do so is obviously latent to our brain.
When they reproduce code which they have previously stored, even only in part, that is a derivative work.
I don’t know about you, but I for one have not produced anything ‘original’ my entire coding career by that metric. I feel confident in saying the vast majority of programmers have not either.
Don’t mistake me, I’m well aware LLMs are not intelligent. But I disagree with the idea that their method of learning and their actions is inherently different from what the average person does.
That’s a stretch, since we don’t know exactly how human learning works.
It is not a stretch. We may not know exactly how human learning works, but we do know exactly how machine learning works, and we know that it is not like how human learning works. It is absolutely possible to differentiate things even without complete knowledge.
For instance, I am not a biologist. I do not have complete knowledge of the workings of a horse or a snake. However, I do know that a horse and a snake are different.
I don’t know about you, but I for one have not produced anything ‘original’ my entire coding career by that metric. I feel confident in saying the vast majority of programmers have not either.
This is a bad argument. The output of a generative model is a copy-and-paste function from a library of ingested code samples with a fairly competent keyword search attached to it. Code writing bots are just script kiddie crutches.
If all you did was copy and paste from GNU GPL code, then your output would also be bound by the same license.
But I disagree with the idea that their method of learning and their actions is inherently different from what the average person does.
Then you don’t understand even the basics of the mathematics that makes them actually work. It’s a purely algorithmic process. It’s an outgrowth of multidimensional analysis and optimization, that’s all.
Here is a better explanation than I could give, by someone who knows better than me:
Yes we have more than just that one mechanism at work obviously, but that’s not to say we’re not using the same or similar method as part of our learning process.
You seem to have entirely ignored the gist of my first point. We may be using a more well rounded method which utilises the same technique LLMs use as a part of our overall learning ability.
If all you did was copy and paste from GNU GPL code, then your output would also be bound by the same license.
We’re back to the same argument that’s been around since the start of the current AI boom, about whether or not people produce art and everything else the same way. At this point it’s very obviously a philosophical argument in general, and from your other comments in this post you’ve offered nothing but semantics as to how they’re different. We’re going to have to agree to disagree there.
Then you don’t understand even the basics of the mathematics that makes them actually work.
I understand how LLMs work. We DON’T understand how humans work, and unless you’ve got a human theory of mind in your back pocket, insisting that they’re inherently different instead of possibly being part of our mental toolkit is premature.
Especially in regards to the nonsense that transformers LLMs will somehow lead to AGI. That’s fiction, sold by con artists like Altman. That bubble is going to pop.
Meanwhile, algorithmic engagement optimization is basically the root of the world’s problems right now.
I’m interested in reading your thoughts about it. Here are mine, in the best english I can muster.
I’m not saying AI is harmless. I’m saying social media has already demonstrated a more immediate and widespread ability to destabilize society. It has transformed how billions of people consume information, created echo chambers, rewarded outrage, accelerated misinformation, increase polarization, and eroded trust in shared facts. Again, AI may eventually become more dangerous, but social media has already created the enviroment in which those dangers can spread. If you think as AI as a weapon, then social media is the distribution system, and we as a society have already given that distribution system to billions of people.
Theres a forming deeply uninformed / misinformed loud group of people who desperately want to hate on AI being used for anything, even in scenarios where its actually perfectly good at its job.
Even when extremely seasoned professionals with waaaay more experience are stating “its a perfectly fine tool” these folks somehow cannot wrap their head around the possibility someone like fucking Linus Torvalds might know better than them.
Imagine having the utter balls to assert you know better than Torvalds about whether a technology is good or not 😐
Meanwhile those of us actually in the field have known for over a year now that the tools are pretty good now and very useful.
The fact that some people don’t want to see AI being used to generate code does not make them dumb or misinformed. They may be informed, and simply reached a different conclusion than you.
Also, you have to consider that some of the most popular tools are controlled by a select few and that concentration can be used to control markets and users, reinforce biases, and shape what gets encouraged.
Finally, the use of AI can easily feed into a maximalist view of “produce more and faster” that has direct consequences like layoffs, increased cost of hardware, increased resource usage, increased environmental impact.
The majority of naysayers are touting deeply misinformed falsifities.
Its not a matter of guessing their intent, theyre quite outspoken on why they think its bad.
And the majority of them tout talking points that are years out of date at best.
Some valid points get brought up, but most of the points tend to be in the “these armchair devs have zero goddamn clue about how things actually are, they just are bandwagoning into an angry mob”
Did AI also tell you this was a word?
All major operating systems and software has been around before AI. Sure, AI might have some uses, even good ones but not enough to warrant all the negative effects it and all those datacenters running the models have.
Some people just don’t care about the negative effects and Torvalds is obviously one of them.
I hate AI as in, I hate Microsoft, Google, OpenAI, etc. for pushing it everywhere, being an oligopoly, and using it for mass surveillance. But AI as a tool is fantastic for some tasks
To be fair, many have had their workplace enshittified by AI, or managers with AI psychosis.
Or fired over it.
Maybe they have family that’s gone down the ChatGPT rabbit hole. I do.
…I think it’s understandable for people to be pissed, and take an absolutionist position, as the leads who see it as a tool seem to be few and far between.
Blaming AI for this, instead of the shitty people who made the decisions, is still very dumb.
I agree that aforementioned companies/managers/etc are shitty.
Thats not AIs fault lol, and isn’t a reflection of it.
Blaming AI as tge issue is just kneejerk mob mentality.
All true.
I’m just saying I can understand how people got in that mindset. Everything feels shitty, and it all seems like it’s because of “AI.”
And I think the hate should redirected to the actual perpetrators: the Tech Bros.
I can agree with that.
Every single generative tool is built on theft. No one has a training database that only contains code they were given legal permission to use for that purpose. Moreover, if these tools were trained on any GNU GPL code, then the tool and all of its output should also be GNU GPL:
https://en.wikipedia.org/wiki/GNU_General_Public_License
None of the companies developing these tools are obeying the terms of the licenses. The trained models are the product of theft.
You cannot be a moral person and approve of the use of these tools, they are diametrically opposed.
Copying is not theft.
Copying GNU GPL code without licensing your derivative code as GNU GPL and publishing it where it’s publicly accessible, and then using your derivative code to generate profit for a corporation, is definitely theft. Just because it’s open source doesn’t mean you can just do whatever you want with it, the original programmer still has rights over the code they wrote.
It literally isn’t.
Hard disagree.
This is like saying anyone who has watched a Disney movie now owes Disney a check any time they illustrate anything, even entirely unrelated, because they’ve learned from their commercial products in the past. If they don’t pay Disney, then their creations are based on theft.
Fundamentally that’s a purely ethical decision about whether a machine doing something should be treated with the same understanding the same as a human would. But we don’t consider remembering something with a neurochemical storage the samd as taking a picture of something even if they’re basically the same.
That’s because taking a picture is an idetic copy. Machine learning is NOT copying, it’s learning - hence the name.
This is a misunderstanding based on confusion between technical and colloquial terminology.
A machine learning model “learns” information in the same way that a curve fitting algorithm “learns” the shape of a data set.
This is not the same as the colloquial meaning of human learning. It is a mathematical process.
That’s splitting hairs on definitions, with no change in meaning. It’s still not copying the data, and is far closer to a person learning than to a picture taken by a camera.
It is actually not like a person learning at all. The only way you could believe this is if you have no grasp of the mathematics that are the basis of the multi-dimensional statistical analysis which is neural network training, and haven’t bothered to do any reading on it.
There’s a reason I referenced curve fitting.
Here is a better explanation than I could give, by someone who knows better than me:
Large Language Models explained briefly by 3Blue1Brown
It actually is, but you want it to be literally the same. Given the options of what it can be compared to, learning is the most appropriate, which is exactly why it’s named Machine Learning. The only reason you could believe otherwise is if you have no concept of language, and believe that it must be literally identical to a person learning in order to use the same verbiage.
It does not; which is the reason why it’s called learning. It’s the closest, and most accurate approximation, regardless of the mathematical operations on which it’s based.
Digital reproduction is digital reproduction no matter how many extra steps are added to the reproduction process. It’s just an algorithm that sorts through a collection of stored data to find specific pieces of data which best fit the keywords supplied by the user, then regurgitates the results that are the best match based on correlation.
In spite of common meaning overlap and popular metaphors, the human brain is not a computer. Ask any neurologist.
Thank you for the opinion. If there’s a trial, and if you’re called as an expert witness, I’m sure they’ll consider your opinion, and have a fun debate on whether a human brain being similar to a computer is relevant in any way.
In the meantime, the technology exists, is useful and the results are no more theft than creating piñatas from newspaper clippings is theft from the newspaper.
Given that you’ve presumably looked at open source code in the past, and got more proficient (however marginally) as a result, does all your future code now belong to the GPL?
This comparison is invalid. Training a neural network algorithm is not equivalent to human learning. We are talking about data stored in machine learning models owned and controlled by multi-billion dollar corporations.
It has already been demonstrated multiple times that original training data can be reproduced completely from models, so yes, they are data storage systems. When they reproduce code which they have previously stored, even only in part, that is a derivative work. Adding extra steps to the transcribing process doesn’t make it any less a copy of the original.
Only when the data in question is so obscure to the point that the original source is the only place in which the data exists; which is neither common, nor useful to regurgitate, in practice.
It’s like if someone asked an artist to draw Trump, and then they did. It’s not that they studied what he looks like, but they’re familiar with that walking pusbag and there’s only the one, so it’s going to be a recognizable drawing.
That’s a stretch, since we don’t know exactly how human learning works. Yes we have more than just that one mechanism at work obviously, but that’s not to say we’re not using the same or similar method as part of our learning process.
So can a few savants, more if you take into account those with special training, etc. The ability to do so is obviously latent to our brain.
I don’t know about you, but I for one have not produced anything ‘original’ my entire coding career by that metric. I feel confident in saying the vast majority of programmers have not either.
Don’t mistake me, I’m well aware LLMs are not intelligent. But I disagree with the idea that their method of learning and their actions is inherently different from what the average person does.
It is not a stretch. We may not know exactly how human learning works, but we do know exactly how machine learning works, and we know that it is not like how human learning works. It is absolutely possible to differentiate things even without complete knowledge.
For instance, I am not a biologist. I do not have complete knowledge of the workings of a horse or a snake. However, I do know that a horse and a snake are different.
This is a bad argument. The output of a generative model is a copy-and-paste function from a library of ingested code samples with a fairly competent keyword search attached to it. Code writing bots are just script kiddie crutches.
If all you did was copy and paste from GNU GPL code, then your output would also be bound by the same license.
Then you don’t understand even the basics of the mathematics that makes them actually work. It’s a purely algorithmic process. It’s an outgrowth of multidimensional analysis and optimization, that’s all.
Here is a better explanation than I could give, by someone who knows better than me:
Large Language Models explained briefly by 3Blue1Brown
You seem to have entirely ignored the gist of my first point. We may be using a more well rounded method which utilises the same technique LLMs use as a part of our overall learning ability.
We’re back to the same argument that’s been around since the start of the current AI boom, about whether or not people produce art and everything else the same way. At this point it’s very obviously a philosophical argument in general, and from your other comments in this post you’ve offered nothing but semantics as to how they’re different. We’re going to have to agree to disagree there.
I understand how LLMs work. We DON’T understand how humans work, and unless you’ve got a human theory of mind in your back pocket, insisting that they’re inherently different instead of possibly being part of our mental toolkit is premature.
Social media is a much bigger and dangerous threat to mankind than AI will ever be.
+1
Especially in regards to the nonsense that transformers LLMs will somehow lead to AGI. That’s fiction, sold by con artists like Altman. That bubble is going to pop.
Meanwhile, algorithmic engagement optimization is basically the root of the world’s problems right now.
I’d say they’re in the same tier. It will take time to determine which is worse.
This is an extremely dangerous misconception.
I’m interested in reading your thoughts about it. Here are mine, in the best english I can muster.
I’m not saying AI is harmless. I’m saying social media has already demonstrated a more immediate and widespread ability to destabilize society. It has transformed how billions of people consume information, created echo chambers, rewarded outrage, accelerated misinformation, increase polarization, and eroded trust in shared facts. Again, AI may eventually become more dangerous, but social media has already created the enviroment in which those dangers can spread. If you think as AI as a weapon, then social media is the distribution system, and we as a society have already given that distribution system to billions of people.
Linus isn’t gonna let you hit little bro
Fuck AI
What a super useful addition to the convo.
Maybe think a little bit harder before hitting the reply button in the future, troll.
As opposed to your comment here?
Maybe ask ChatGPT to think harder next time.
Have you forgotten about Bitkeeper?
Bravo! 💯