- Joined
- Jul 30, 2017
"Mad at the Internet" - a/k/a My Psychotherapy Sessions
- Thread starter Null
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- Joined
- Nov 14, 2012
i dunno homie i haven't spoken to any of them in like 6 years. she bought it when it was a huge fad people kept talking about.
i know you are desperate to think women want to be raped but it's not really accurate. women like the idea of men treasuring them, fighting over them, or being so attracted to them they lose inhibition. They don't like being violently raped by niggers in alleyways, despite what /pol/ wants to extrapolate from meaningless booksale figures.
- Joined
- Apr 8, 2018
If you make fun of someone's nappy hair or tell a brotha that he needs to lose the corn rows before he can work for you in the state of Texas well you are going to jail! Look out bigots! The crippled governor Greg Abbot just signed the CROWN act!
Create a
Respectful and
Open
World for
Natural hair
For whatever reason I can't download the video on the tweet. He just says a bunch of bullshit about how great this is and how this will help people who are discriminated against because of their hair which is mainly people of color. Daniel Perry
the US Army sergeant who who shot and killed a tubby ginger antifa asshole who approached Daniel's car with an AK-47 at a fiery but mostly peaceful protest during the summer of love that was 2020
still hasn't been pardoned which Hotwheels Abbot swore he was gonna get right on.
Only two things come from Texas
Texans are so embarrassingly cucked it's not even funny. Their bravado is on the level of New Yawkers who claim they're tough because they come from one of the boroughs despite all their public butt fucking from mayors and governors who do things like outlaw outdoor soda consumption because it's unhealthy.
Create a
Respectful and
Open
World for
Natural hair
For whatever reason I can't download the video on the tweet. He just says a bunch of bullshit about how great this is and how this will help people who are discriminated against because of their hair which is mainly people of color. Daniel Perry
the US Army sergeant who who shot and killed a tubby ginger antifa asshole who approached Daniel's car with an AK-47 at a fiery but mostly peaceful protest during the summer of love that was 2020
Only two things come from Texas
- Joined
- Aug 3, 2021
Pizza day is officially canceled for a startup that wasted $500 million trying to get a robot to make pizza in a truck.
https://www.restaurantbusinessonline.com/technology/zume-shuts-down-its-robot-powered-pizza-business
More details via wikipedia
If that sounds utterly fucking baffling at how this was supposed to add value to the customer vs normally ordering a pizza to be delivered, I'm with you there.
- Joined
- May 6, 2020
It's funny because aside from the consensual rape, the dude she's with is a billionaire who immediately starts buying her shit and treating her like his queen. The reason why he's into fucked up shit is because his mother was a crack whore who overdosed. So basically the author would probably agree with the sentiment that sexual deviancy results from childhood trauma. I wonder if she includes homos in that.
- Joined
- Dec 16, 2019
the "women want to be raped fantasied" meme is just that; they misinterpreted "women want their men to act like men and take them" as "women want to be raped" because women aren't really turned on by legalistic continual-consent inquiries.
- Joined
- Jan 2, 2019
The entire story is based on a Twilight fanfiction the author wrote as a teenage girl btw. The BDSM guy is Edward and the woman is Bella. AND I'M NOT KIDDING!
- Joined
- Feb 13, 2020
@blur here's more, an article he wrote, https://archive.is/K8fjy
There are more articles but I cannot find them because Zerohedge archives shit and demands money to see it, all of the articles are on the MATI site, which is currently offline
There are more articles but I cannot find them because Zerohedge archives shit and demands money to see it, all of the articles are on the MATI site, which is currently offline
- Joined
- Jul 30, 2017
The point is: Yeah people might be convinced to buy a book because of a fad or "all the buzz" etc - but unless you want to argue that there is a super massive OCD complex among the populace, if someone buys book 1 for the hype and doesn't like it, they are not going to buy more books in the series. If someone picked up Harry Potter 1 to see what all the jazz is about and then hated it, they aren't going to be buying Harry Potter 2-7. That's why you see series of anything always have drop offs - people will check out the first book/issue/movie/episode and then only those who liked it will continue to buy more from there.
As I posted, FSoG wasn't just bestseller of the decade - all three books took the top three spots. You could excuse the first one winning by hype and marketing (and that certainly helped to push it to the top), but what's your excuse for people coming back and getting more of it, two more times? I find when people do something 3 or more times that's usually a strong indication of something.
Who said anything about rape??? The girl isn't even raped in FSoG, a big plot point of the whole thing is signing a consent contract.
The point is usually that romance novels involve - now pay attention here - rape to an outside observer because consent isn't vocalized. Oh sure the reader knows it isn't rape because they are shown the woman's internal monologue and her giving mental consent. Which I don't know about your world, about around here on Earth, nobody has the ability to detect because mind-reading is still science fiction. This is not new either.
The real gag is that what women want is always contradictory. Like in FSoG, Christian is the CEO of multiple massive corporations, yet has time to obsess over a small-town girl. Never mind that in real life to maintain the level of wealth he would have at that age would be so time consuming he would get to spend - at best - 30 minutes with the MC a week.
She even mentions writing it all on her blackberry.
The book series by Cassandra Claire (which has gotten a movie AND a TV adaption) also started life as a Harry Potter incest fan fic and I'm not kidding either.
Last edited:
- Joined
- Dec 17, 2022
I crawled and archived the site a couple of months ago. You can find the archives of the articles/writings here:
No loss condition — why YouTube, Twitter, and Facebook own you
Re-opening Europa
Brave New World
Section 230 isn’t the problem, Payment Networks are
Where the Sidewalk Ends: The Death of the Internet
Using US Courts to Burn Money, for Free, Forever
Buying Freedom with Alternative Money
- Joined
- Aug 28, 2019
- Joined
- Feb 22, 2022
Funniest shit I've seen all year:
Commentary YouTuber and possible chaser Bowblax was exposed for having a secrat alt account where he could say some very not family-friendly things.
But also... a complete sped.
Keffals did not take kindly to this however.
An argument ensued, seemingly resolved in a stream with Bowblax arguing with Keffals in Keffal's biggest lackey Tipster's stream.
Oops.
This all culminates in a bloodsports stream with AugieRFC arguing with Keffals where they bring on Tipster to which Augie kind of goes and- spits out his truth.
And when Augie brings on Leafy, they just leave, then Augie just brings on his entire friend group to just dog on Keffals and Tipster for a couple of hours.
https://rumble.com/v2tt21w-keffals-sas-bowblax-in-front-of-tipster.html
It's too long to post here so here's the entire story here:
Commentary YouTuber and possible chaser Bowblax was exposed for having a secrat alt account where he could say some very not family-friendly things.
But also... a complete sped.
Keffals did not take kindly to this however.
An argument ensued, seemingly resolved in a stream with Bowblax arguing with Keffals in Keffal's biggest lackey Tipster's stream.
Oops.
This all culminates in a bloodsports stream with AugieRFC arguing with Keffals where they bring on Tipster to which Augie kind of goes and- spits out his truth.
And when Augie brings on Leafy, they just leave, then Augie just brings on his entire friend group to just dog on Keffals and Tipster for a couple of hours.
https://rumble.com/v2tt21w-keffals-sas-bowblax-in-front-of-tipster.html
It's too long to post here so here's the entire story here:
- Joined
- Jan 12, 2022
- Joined
- Jul 30, 2017
Josh literally asked:
Don't ask the robot questions you don't want the answer to.
- Joined
- Jan 12, 2022
Your point is bad and you should feel bad.Don't ask the robot questions you don't want the answer to.
- Joined
- Jul 30, 2017
- Joined
- Jul 30, 2017
In other news:
https://venturebeat.com/ai/the-ai-f...ollapse-as-ai-trains-on-ai-generated-content/
Robots talking to robots could be bad.
But, as those following the burgeoning industry and its underlying research know, the data used to train the large language models (LLMs) and other transformer models underpinning products such as ChatGPT, Stable Diffusion and Midjourney comes initially from human sources — books, articles, photographs and so on — that were created without the help of artificial intelligence.
Now, as more people use AI to produce and publish content, an obvious question arises: What happens as AI-generated content proliferates around the internet, and AI models begin to train on it, instead of on primarily human-generated content?
A group of researchers from the UK and Canada have looked into this very problem and recently published a paper on their work in the open access journal arXiv. What they found is worrisome for current generative AI technology and its future: “We find that use of model-generated content in training causes irreversible defects in the resulting models.”
“Over time, mistakes in generated data compound and ultimately force models that learn from generated data to misperceive reality even further,” wrote one of the paper’s leading authors, Ilia Shumailov, in an email to VentureBeat. “We were surprised to observe how quickly model collapse happens: Models can rapidly forget most of the original data from which they initially learned.”
In other words: as an AI training model is exposed to more AI-generated data, it performs worse over time, producing more errors in the responses and content it generates, and producing far less non-erroneous variety in its responses.
As another of the paper’s authors, Ross Anderson, professor of security engineering at Cambridge University and the University of Edinburgh, wrote in a blog post discussing the paper: “Just as we’ve strewn the oceans with plastic trash and filled the atmosphere with carbon dioxide, so we’re about to fill the Internet with blah. This will make it harder to train newer models by scraping the web, giving an advantage to firms which already did that, or which control access to human interfaces at scale. Indeed, we already see AI startups hammering the Internet Archive for training data.”
Ted Chiang, acclaimed sci-fi author of “Story of Your Life,” the novella that inspired the movie Arrival, and a writer at Microsoft, recently published a piece in The New Yorker postulating that AI copies of copies would result in degrading quality, likening the problem to the increased artifacts visible as one copies a JPEG image repeatedly.
Another way to think of the problem is like the 1996 sci-fi comedy movie Multiplicity starring Michael Keaton, wherein a humble man clones himself and then clones the clones, each of which results in exponentially decreasing levels of intelligence and increasing stupidity.
“Original data generated by humans represents the world more fairly, i.e. it contains improbable data too,” Shumailov explained. “Generative models, on the other hand, tend to overfit for popular data and often misunderstand/misrepresent less popular data.”
Shumailov illustrated this problem for VentureBeat with a hypothetical scenario, wherein a machine learning model is trained on a dataset with pictures of 100 cats — 10 of them with blue fur, and 90 with yellow. The model learns that yellow cats are more prevalent, but also represents blue cats as more yellowish than they really are, returning some green-cat results when asked to produce new data. Over time, the original trait of blue fur erodes through successive training cycles, turning from blue to greenish, and ultimately yellow. This progressive distortion and eventual loss of minority data characteristics is model collapse. To prevent this, it’s important to ensure fair representation of minority groups in datasets, in terms of both quantity and accurate portrayal of distinctive features. The task is challenging due to models’ difficulty learning from rare events.
This “pollution” with AI-generated data results in models gaining a distorted perception of reality. Even when researchers trained the models not to produce too many repeating responses, they found model collapse still occurred, as the models would start to make up erroneous responses to avoid repeating data too frequently.
“There are many other aspects that will lead to more serious implications, such as discrimination based on gender, ethnicity or other sensitive attributes,” Shumailov said, especially if generative AI learns over time to produce, say, one race in its responses, while “forgetting” others exist.
It’s important to note that this phenomenon is distinct from “catastrophic forgetting,” where models lose previously learned information. In contrast, model collapse involves models misinterpreting reality based on their reinforced beliefs.
The researchers behind this paper found that even if 10% of the original human-authored data is used to train the model in subsequent generations, “model collapse still happens, just not as quickly,” Shumailov told VentureBeat.
The researchers highlight two specific ways. The first is by retaining a prestige copy of the original exclusively or nominally human-produced dataset, and avoiding contaminating with with AI-generated data. Then, the model could be periodically retrained on this data, or refreshed entirely with it, starting from scratch.
The second way to avoid degradation in response quality and reduce unwanted errors or repetitions from AI models is to introduce new, clean, human-generated datasets back into their training.
However, as the researchers point out, this would require some sort of mass labeling mechanism or effort by content producers or AI companies to differentiate between AI-generated and human-generated content. At present, no such reliable or large-scale effort exists online.
“To stop model collapse, we need to make sure that minority groups from the original data get represented fairly in the subsequent datasets,” Shumailov told VentureBeat, continuing:
“In practice it is completely non-trivial. Data needs to be backed up carefully, and cover all possible corner cases. In evaluating performance of the models, use the data the model is expected to work on, even the most improbable data cases. Note that this does not mean that improbable data should be oversampled, but rather that it should be appropriately represented. As progress drives you to retrain your models, make sure to include old data as well as new. This will push up the cost of training, yet will help you to counteract model collapse, at least to some degree.”
These findings have significant implications for the field of artificial intelligence, emphasizing the need for improved methodologies to maintain the integrity of generative models over time. They underscore the risks of unchecked generative processes and may guide future research to develop strategies to prevent or manage model collapse.
“It is clear, though, that model collapse is an issue for ML and something has to be done about it to ensure generative AI continues to improve,” Shumailov said.
I do love that heading title in the article: ‘Filling the internet with blah’
New motto of the kiwifarms? (though I'm still fond of "the internet's comment section")
https://venturebeat.com/ai/the-ai-f...ollapse-as-ai-trains-on-ai-generated-content/
Robots talking to robots could be bad.
The AI feedback loop: Researchers warn of ‘model collapse’ as AI trains on AI-generated content
The age of generative AI is here: only six months after OpenAI‘s ChatGPT burst onto the scene, as many as half the employees of some leading global companies are already using this type of technology in their workflows, and many other companies are rushing to offer new products with generative AI built in.But, as those following the burgeoning industry and its underlying research know, the data used to train the large language models (LLMs) and other transformer models underpinning products such as ChatGPT, Stable Diffusion and Midjourney comes initially from human sources — books, articles, photographs and so on — that were created without the help of artificial intelligence.
Now, as more people use AI to produce and publish content, an obvious question arises: What happens as AI-generated content proliferates around the internet, and AI models begin to train on it, instead of on primarily human-generated content?
A group of researchers from the UK and Canada have looked into this very problem and recently published a paper on their work in the open access journal arXiv. What they found is worrisome for current generative AI technology and its future: “We find that use of model-generated content in training causes irreversible defects in the resulting models.”
‘Filling the internet with blah’
Specifically looking at probability distributions for text-to-text and image-to-image AI generative models, the researchers concluded that “learning from data produced by other models causes model collapse — a degenerative process whereby, over time, models forget the true underlying data distribution … this process is inevitable, even for cases with almost ideal conditions for long-term learning.”“Over time, mistakes in generated data compound and ultimately force models that learn from generated data to misperceive reality even further,” wrote one of the paper’s leading authors, Ilia Shumailov, in an email to VentureBeat. “We were surprised to observe how quickly model collapse happens: Models can rapidly forget most of the original data from which they initially learned.”
In other words: as an AI training model is exposed to more AI-generated data, it performs worse over time, producing more errors in the responses and content it generates, and producing far less non-erroneous variety in its responses.
As another of the paper’s authors, Ross Anderson, professor of security engineering at Cambridge University and the University of Edinburgh, wrote in a blog post discussing the paper: “Just as we’ve strewn the oceans with plastic trash and filled the atmosphere with carbon dioxide, so we’re about to fill the Internet with blah. This will make it harder to train newer models by scraping the web, giving an advantage to firms which already did that, or which control access to human interfaces at scale. Indeed, we already see AI startups hammering the Internet Archive for training data.”
Ted Chiang, acclaimed sci-fi author of “Story of Your Life,” the novella that inspired the movie Arrival, and a writer at Microsoft, recently published a piece in The New Yorker postulating that AI copies of copies would result in degrading quality, likening the problem to the increased artifacts visible as one copies a JPEG image repeatedly.
Another way to think of the problem is like the 1996 sci-fi comedy movie Multiplicity starring Michael Keaton, wherein a humble man clones himself and then clones the clones, each of which results in exponentially decreasing levels of intelligence and increasing stupidity.
How ‘model collapse’ happens
In essence, model collapse occurs when the data AI models generate ends up contaminating the training set for subsequent models.“Original data generated by humans represents the world more fairly, i.e. it contains improbable data too,” Shumailov explained. “Generative models, on the other hand, tend to overfit for popular data and often misunderstand/misrepresent less popular data.”
Shumailov illustrated this problem for VentureBeat with a hypothetical scenario, wherein a machine learning model is trained on a dataset with pictures of 100 cats — 10 of them with blue fur, and 90 with yellow. The model learns that yellow cats are more prevalent, but also represents blue cats as more yellowish than they really are, returning some green-cat results when asked to produce new data. Over time, the original trait of blue fur erodes through successive training cycles, turning from blue to greenish, and ultimately yellow. This progressive distortion and eventual loss of minority data characteristics is model collapse. To prevent this, it’s important to ensure fair representation of minority groups in datasets, in terms of both quantity and accurate portrayal of distinctive features. The task is challenging due to models’ difficulty learning from rare events.
This “pollution” with AI-generated data results in models gaining a distorted perception of reality. Even when researchers trained the models not to produce too many repeating responses, they found model collapse still occurred, as the models would start to make up erroneous responses to avoid repeating data too frequently.
“There are many other aspects that will lead to more serious implications, such as discrimination based on gender, ethnicity or other sensitive attributes,” Shumailov said, especially if generative AI learns over time to produce, say, one race in its responses, while “forgetting” others exist.
It’s important to note that this phenomenon is distinct from “catastrophic forgetting,” where models lose previously learned information. In contrast, model collapse involves models misinterpreting reality based on their reinforced beliefs.
The researchers behind this paper found that even if 10% of the original human-authored data is used to train the model in subsequent generations, “model collapse still happens, just not as quickly,” Shumailov told VentureBeat.
Ways to avoid ‘model collapse’
Fortunately, there are ways to avoid model collapse, even with existing transformers and LLMs.The researchers highlight two specific ways. The first is by retaining a prestige copy of the original exclusively or nominally human-produced dataset, and avoiding contaminating with with AI-generated data. Then, the model could be periodically retrained on this data, or refreshed entirely with it, starting from scratch.
The second way to avoid degradation in response quality and reduce unwanted errors or repetitions from AI models is to introduce new, clean, human-generated datasets back into their training.
However, as the researchers point out, this would require some sort of mass labeling mechanism or effort by content producers or AI companies to differentiate between AI-generated and human-generated content. At present, no such reliable or large-scale effort exists online.
“To stop model collapse, we need to make sure that minority groups from the original data get represented fairly in the subsequent datasets,” Shumailov told VentureBeat, continuing:
“In practice it is completely non-trivial. Data needs to be backed up carefully, and cover all possible corner cases. In evaluating performance of the models, use the data the model is expected to work on, even the most improbable data cases. Note that this does not mean that improbable data should be oversampled, but rather that it should be appropriately represented. As progress drives you to retrain your models, make sure to include old data as well as new. This will push up the cost of training, yet will help you to counteract model collapse, at least to some degree.”
What the AI industry and users can do about it going forward
While all this news is worrisome for current generative AI technology and the companies seeking to monetize with it, especially in the medium-to-long term, there is a silver lining for human content creators: The researchers conclude that in a future filled with gen AI tools and their content, human-created content will be even more valuable than it is today — if only as a source of pristine training data for AI.These findings have significant implications for the field of artificial intelligence, emphasizing the need for improved methodologies to maintain the integrity of generative models over time. They underscore the risks of unchecked generative processes and may guide future research to develop strategies to prevent or manage model collapse.
“It is clear, though, that model collapse is an issue for ML and something has to be done about it to ensure generative AI continues to improve,” Shumailov said.
I do love that heading title in the article: ‘Filling the internet with blah’
New motto of the kiwifarms? (though I'm still fond of "the internet's comment section")
- Joined
- Jan 2, 2019
Damn women are dumb as FUCK wanting a guy with a good job to support them and give them security but also spend time with them. Meanwhile the barely legal harem anal destruction stepsister big titty goth gf mommydommy BBC gangbang fantasies of men are very true to life and attainable.
- Joined
- Sep 28, 2022
Something I haven't really heard people talk about was the recent hit Daily Wire took on Youtube because the globohomo is striking back against people waking up to their bullshit:
Two of their members have been suspended for a week, Jordan Peterson received his first strike, Matt Walsh is fully demonetized now.
Two of their members have been suspended for a week, Jordan Peterson received his first strike, Matt Walsh is fully demonetized now.