No one is surprised when the dog gets worms after eating poop it found in the yard. Why are we shocked that an AI that doesn’t know fact from fiction treats everything the same?

I think that's the problem though, I think the poop in the yard is a better example. Key is the researchers put that information in speculation. That's like if Anderson Cooper made up a fake news story, and posted it in an anonymous tweet to analyze how far it would spread, and then fox news picks it up and runs with the story all day.

That's the key problem, people are trusting LLMs to do their research for them, when LLMs just gather all the information they can get their hands on mindlessly.

That's the key problem, If they send a misinformative article, to a place for untested, unproven random speculation with a very low bar for who can submit... they can determine that LLMs are looking there. Key thing to note is, it's not their fake disease that's the threat. It's that if it found their fake article, then LLMs probably also scooped up a ton of other misinformed or dubious things.

Lets look at it this way, say it was a cake, but we threw it in the garbage, 2 weeks later we find the same cake... at jims bakery, same ID, same distinct marker we put on it.

What does that tell us, it tells us that Jims bakery is clearly sometimes, dumpster diving and putting things up that clearly are dangerous.

It's known that AI companies will harvest content without care for its veracity and train LLMs on it. These LLMs will then regurgitate that content as fact.

This isn't a particularly novel finding but the experiment illustrates it rather well.

The researchers you consider to have acted so immorally did add useless information to the knowledge pool – but it was unadvertised, immediately recognizable useless information that any sane reviewer would've flagged. They included subtle clues like thanking someone at Starfleet Academy for letting them use a lab aboard the USS Enterprise. They claimed to have gotten funding from the Sideshow Bob Foundation. Subtle.

By providing this easily traceable nonsense, they were able to turn the generally-but-informally known understanding that LLMs will repeat bullshit into a hard scientific data point that others can build on. Nothing world-changing but still valuable. They basically did what Alan Sokal did.

Instead of worrying about this experiment you should worry about all the misinformation in LLMs that wasn't provided (and diligently documented) by well-meaning researchers.

Seems like the logical conclusion would be then that people who train LLMs should be responsible for curating the data, not expecting that the data will just be sound. People have been lying on the internet since it was invented, the advent of LLMs isn't suddenly going to create an internet that doesn't occur in.

They are shitting out what you feed them. If you feed them garbage, you get garbage in return.

This is the missing conceptual understanding that probably 90% of LLM users lack. They really don't know how LLMs work, and treat them like AGI. Sadly this includes adult policy makers in our society too. Efforts like those of these these researchers act to educate the public. I'm hopeful this will spark some critical thinking on the part of regular, otherwise ignorant, LLM users.