When German journalist Martin Bernklautyped his name and location into Microsoft’s Copilot to see how his articles would be picked up by the chatbot, the answers horrified him. Copilot’s results asserted that Bernklau was an escapee from a psychiatric institution, a convicted child abuser, and a conman preying on widowers. For years, Bernklau had served as a courts reporter and the AI chatbot had falsely blamed him for the crimes whose trials he had covered.
The accusations against Bernklau weren’t true, of course, and are examples of generative AI’s “hallucinations.” These are inaccurate or nonsensical responses to a prompt provided by the user, and they’re alarmingly common. Anyone attempting to use AI should always proceed with great caution, because information from such systems needs validation and verification by humans before it can be trusted.
But why did Copilot hallucinate these terrible and false accusations?
And to help educate the ignorant masses:
Generative AI and LLMs start by predicting the next word in a sequence. The words are generated independently of each other and when optimized: simultaneously.
The reason that it used the reporter’s name as the culprit is because out of the names in the sample data his name appeared at or near the top of the list of frequent names so it was statistically likely to be the next name mentioned.
AI have no concepts, period. It doesn’t know what a person is, or what the laws are. It generates word salad that approximates human statements. It is a math problem, statistics.
There are actual science fiction stories built on the premise that AI reporting on the start of Nuclear War resulted in actual kickoff of the apocalypse, and we’re at that corner now.
Is this true? I know that’s how Marcov chains work, but I thought neural nets worked differently with larger tokens.
The only difference between a generic old fashioned world salad generator and GPT4 is the scale. You put multiple layers correcting for different factors on it and suddenly your Language Model turns into a Large Language Model.
So basically your large tokens are made up of smaller tokens, but its still just statistical approximation of the sample data with little to no emergent behavior or even memory of what its saying as it says it.
It also exponentially increases power requirements, as the world is figuring out.
I don’t disagree, I was just pointing out that “each word is generated independently of each other” isn’t strictly accurate for LLM’s.
It’s part of the reason they are so convincing to some people, they are able to hold threads semi-coherently throughout entire essay length paragraphs without obvious internal lapses of logic.
I think you’re seeing coherence where there is none.
Ask it to solve the riddle about the fox the chicken and the grains.
Even if it does solve the riddle without blurting out random nonsense, that’s just because the sample data solved the riddle billions of times before.
It’s just guessing words.
IIRC, this was the running theory in Fallout until the show.
Edit: I may be misremembering, it may have just been something similar.
I haven’t played the original series but in 3 and 4 it was pretty much confirmed the big companies like BlamCo! intentionally set things in motion, but also that Chinese nuclear vessels were already in place near America.
Ironically, Vault Tech wasn’t planning to ever actually use their vaults for anything except human expirimentation so they might have been out of the loop.
Yeah, it’s kinda been all over the place, but that’s where the show ended up going, except Vault Tech was very much in the loop. I can’t get spoiler tags to work, so I’ll leave out the details.
What I’m thinking of, though, was also in Fallout 4. I’ve been thinking on it, and I remember now that what I’m thinking of is that it’s implied that the AI from the Railroad quests fed fake info about incoming missiles to force America to fire. I still don’t remember any specifics, though, and I could be misremembering. It’s been a good few years after all, lol.