ARTIFICIAL intelligence models may soon fall into a doom spiral as machine-generated gibberish floods the internet.
It is no secret that artificial intelligence models must train on large swathes of data in order to generate an output.
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Researchers warn artificial intelligence is approaching the concept of “model collapse” as models train on nonsensical, machine-generated data
However, that data must be “high-quality,” meaning accurate and reliable – and the tech giants know it, too.
ChatGPT developer OpenAI has partnered with newsmakers like Vox Media and News Corp to train its chatbots on fresh content.
But this may not be enough to slow the spread of synthetic data, which has flooded the internet since generative AI systems became widely accessible.
As companies like Google and Meta comb search engines and social media for training data, it is inevitable that they will encounter AI-generated content.
When this information is compiled into a dataset for an AI model, the the result is the equivalent of inbreeding.
Systems become increasingly deformed as they learn from inaccurate, machine-generated content and spit falsities out.
This information then winds up in a dataset for a different model, and the process repeats, leading to a total meltdown.
Researcher Jathan Sadowski has been documenting the phenomenon on X, formerly Twitter, for over a year.
He coined the term “Habsburg AI” in February 2023, taking the name from a notoriously inbred royal dynasty.
Sadowski defines it as “a system that is so heavily trained on the outputs of other generative AI’s that it becomes an inbred mutant.”
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The phenomenon takes many names. Other researchers know it as model autophagy disorder, or MAD.
The term “autophagy” comes from the Greek “self-devouring,” aptly capturing the way a system trains itself on AI-synthesized content like a snake eating its own tail.
Researchers at Rice and Stanford University were among the first to discover that models decline in the quality and diversity of their output without a constant stream of quality data.
Complete autophagy occurs when a model is trained solely on its own responses, but machines can also train on data published by other AI programs.
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Some experts have likened the phenomenon to digital “inbreeding,” where models train on their own outputs or the outputs of others and becoming increasingly disfigured
“Training large-language models on data created by other models…causes ‘irreversible defects in the resulting models,” Sadowski tweeted, referencing an article in the journal Nature.
Digital inbreeding harkens back to the idea of “model collapse,” where systems grow increasingly incoherent due to an influx of AI-generated content.
While the idea was once just theory, experts believe it is becoming increasingly likely as more and more synthetic data appears.
NewsGuard, a platform that rates the credibility of news sites, has been tracking the increase of “AI-enabled misinformation” online.
By the end of 2023, the group identified 614 unreliable AI-generated news and information websites, dubbed “UAINS.”
That number has since swelled to 1,036.
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The risk lies in the human consumption of information as misleading, AI-generated content continues to flood the internet
The websites span over a dozen languages and have generic names like “Ireland Top News” and “iBusiness Day” that appear like legitimate outlets.
Chatbots and other generative AI models may train on this information, regurgitating falsities about news events, celebrity deaths, and more in their responses.
While some netizens could care less about the future of AI, the phenomenon, if unchecked, could have disastrous impacts on human users.
As media literacy declines and AI-generated content floods the internet, users may struggle to distinguish between factual information and machine-generated nonsense.
What are the arguments against AI?
Artificial intelligence is a highly contested issue, and it seems everyone has a stance on it. Here are some common arguments against it:
Loss of jobs – Some industry experts argue that AI will create new niches in the job market, and as some roles are eliminated, others will appear. However, many artists and writers insist the argument is ethical, as generative AI tools are being trained on their work and wouldn’t function otherwise.
Ethics – When AI is trained on a dataset, much of the content is taken from the Internet. This is almost always, if not exclusively, done without notifying the people whose work is being taken.
Privacy – Content from personal social media accounts may be fed to language models to train them. Concerns have cropped up as Meta unveils its AI assistants across platforms like Facebook and Instagram. There have been legal challenges to this: in 2016, legislation was created to protect personal data in the EU, and similar laws are in the works in the United States.
Misinformation – As AI tools pulls information from the Internet, they may take things out of context or suffer hallucinations that produce nonsensical answers. Tools like Copilot on Bing and Google’s generative AI in search are always at risk of getting things wrong. Some critics argue this could have lethal effects – such as AI prescribing the wrong health information.
Researchers are patently aware of the risk, but it is unclear just how far AI developers are willing to go to prevent “inbreeding.”
After all, synthetic data is freely available and much cheaper to source. Some proponents argue it doesn’t fall victim to the same moral and ethical quandaries as human-generated content.
“So weird that everybody else has to change what they’re doing to support the spread and integration of AI into our lives – but apparently the AI systems and tech start-ups don’t need to change at all,” Sadowski quipped in one post.
“They are perfect. We are the problems.”
