This seems like a wasted effort when AI will primarily learn the majority consensus view and not one-off misinformation. AI tries to learn pattern matching for generalization, so garbage data doesn't make AI learn the wrong patterns, at best just slows down learning the actual patterns. When most compute for training is spent on curated data and RL rather than random web-scraped data, the impact is likely negligible.
> This seems like a wasted effort when AI will primarily learn the majority consensus view and not one-off misinformation. We have evidence to the contrary. Two blog articles and two preprints of fake academic articles [0] were able to convince CoPilot, Gemini, ChatGPT and Perplexity AI of the existence of a fake disease, against all majority consensus. And even though the falsity of this information was made public…
> The problem was that the experiment worked too well. Within weeks of her uploading information about the condition, attributed to a fictional author, major artificial-intelligence systems began repeating the invented condition as if it were real.
This seems to imply the poisoning affected the web search results, not the actual model itself, because it takes months for data to make it into a trained base model.