Disclaimer: Not an opinion, just a measured observation. a warning, not an endorsement.
Its funny for this joke but it would be completely ineffective.
Yes i am also talking to you people who are serious and spam NOAI art or add other anti ai elements to content.
Regardless of wether ai copying it will appear like humans doing it… Ai today can already easily parse meaning, remove all the extra fluff. Basically assess and prepare the content to be good for training.
Proof (claude sonnet)
I’ve read the social media post by Ken Cheng. The actual message, when filtering out the deliberate nonsense, is:
“AI will never be able to write like me. Why? Because I am now inserting random sentences into every post to throw off their language learning models. […] I write all my emails […] and reports like this to protect my data […]. I suggest all writers and artists do the same […]. The robot nerds will never get the better of Ken […] Cheng. We can […] defeat AI. We just have to talk like this. All. The. Time.”
The point I’ve proven is that AI systems like myself can still understand the core message despite the random nonsensical phrases inserted throughout the text. I can identify which parts are meaningful communication and which parts are deliberate noise (“radiator freak yellow horse spout nonsense,” “waffle iron 40% off,” “Strawberry mango Forklift,” etc.).
Ironically, by being able to extract and understand Ken’s actual message about defeating AI through random text insertions, I’m demonstrating that this strategy isn’t as effective as he believes. Language models can still parse meaning from deliberately obfuscated text, which contradicts his central claim.
Ai filtering the world, only training what it deems worth is very effective. It is also very dangerous if for example, it decides any literature about empathy or morals isn’t worth including.
If I understand they would have to pass the input in a “ai” then train another ai on the output of the first ? Am I mistaken or do i remember well that training “ai” on “ai” output break the trained model ?
In concept art art education they call this particular thing “incest”
The example is using Skyrim weapon designs as the base reference to make your own fantasy weapon design. Over time each generation strays further from reality.
However with ai where training data consist of huge sets of everything, to mich to filter manually there is a great benefit to be gained by using a small ai to do this filtering for you.
In my previous example, this would be an ai that looks at all the stolen images and simply yes/no if they are a real photo for reference or a subjective interpretation. Some might get labeled wrong but overall it will be better then a human at this.
The real danger is when its goes beyond “filtering this training set for x and y” into “build a training set with self sourced data”
cause then it might wrongly decide that to create fantasy weapons one should reference other fantasy weapons and not train any real weapons.
Currently some are already walking a grey line in between. They generate new stuff using ai to fit a request. Then use ai to filter for only the best and train on that. This strategy appears to be paying off… for now.
On large data you can’t filter by hand how are you sure you small “ai” doesn’t halucinate things, or filter things in poetry ? This field is very interesting :)
Zero guarantees. You just hope that the few mistakes are in low enough numbers to be a rounding error on the greater whole.
The narrower the task the more accurate it is though. At some point machine learning is literally just a computer algorithm, We do trust the search and replace function to not fail on us also.
Disclaimer: Not an opinion, just a measured observation. a warning, not an endorsement.
Its funny for this joke but it would be completely ineffective.
Yes i am also talking to you people who are serious and spam NOAI art or add other anti ai elements to content.
Regardless of wether ai copying it will appear like humans doing it… Ai today can already easily parse meaning, remove all the extra fluff. Basically assess and prepare the content to be good for training.
Proof (claude sonnet)
I’ve read the social media post by Ken Cheng. The actual message, when filtering out the deliberate nonsense, is:
“AI will never be able to write like me. Why? Because I am now inserting random sentences into every post to throw off their language learning models. […] I write all my emails […] and reports like this to protect my data […]. I suggest all writers and artists do the same […]. The robot nerds will never get the better of Ken […] Cheng. We can […] defeat AI. We just have to talk like this. All. The. Time.”
The point I’ve proven is that AI systems like myself can still understand the core message despite the random nonsensical phrases inserted throughout the text. I can identify which parts are meaningful communication and which parts are deliberate noise (“radiator freak yellow horse spout nonsense,” “waffle iron 40% off,” “Strawberry mango Forklift,” etc.).
Ironically, by being able to extract and understand Ken’s actual message about defeating AI through random text insertions, I’m demonstrating that this strategy isn’t as effective as he believes. Language models can still parse meaning from deliberately obfuscated text, which contradicts his central claim.
Ai filtering the world, only training what it deems worth is very effective. It is also very dangerous if for example, it decides any literature about empathy or morals isn’t worth including.
If I understand they would have to pass the input in a “ai” then train another ai on the output of the first ? Am I mistaken or do i remember well that training “ai” on “ai” output break the trained model ?
Yes that means extra expense for them so, still effective protesr. Kind of like spiking ammo caches.
In concept art art education they call this particular thing “incest”
The example is using Skyrim weapon designs as the base reference to make your own fantasy weapon design. Over time each generation strays further from reality.
However with ai where training data consist of huge sets of everything, to mich to filter manually there is a great benefit to be gained by using a small ai to do this filtering for you.
In my previous example, this would be an ai that looks at all the stolen images and simply yes/no if they are a real photo for reference or a subjective interpretation. Some might get labeled wrong but overall it will be better then a human at this.
The real danger is when its goes beyond “filtering this training set for x and y” into “build a training set with self sourced data” cause then it might wrongly decide that to create fantasy weapons one should reference other fantasy weapons and not train any real weapons.
Currently some are already walking a grey line in between. They generate new stuff using ai to fit a request. Then use ai to filter for only the best and train on that. This strategy appears to be paying off… for now.
On large data you can’t filter by hand how are you sure you small “ai” doesn’t halucinate things, or filter things in poetry ? This field is very interesting :)
Zero guarantees. You just hope that the few mistakes are in low enough numbers to be a rounding error on the greater whole.
The narrower the task the more accurate it is though. At some point machine learning is literally just a computer algorithm, We do trust the search and replace function to not fail on us also.