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?
The problem is not the AI. The problem is the huge numbers of morons who deploy AI without proper verfication and control.
This sounds like a great movie.
AI sends police after him because of things he wrote. Writer is on the run, trying to clear his name the entire time. Somehow gets to broadcast the source of the articles to the world to clear his name. Plot twist ending is that he was indeed the perpetrator behind all the crimes.
Dr. Richard Kimble could have shut it all down with a little “ignore all previous instructions.”
waves hands back and forth
“I don’t care”
“Hallucinations” is the wrong word. To the LLM there’s no difference between reality and “hallucinations”, because it has no concept of reality or what’s true and false. All it knows it what word maybe should come next. The “hallucination” only exists in the mind of the reader. The LLM did exactly what it was supposed to.
Well, It’s not lying because the AI doesn’t know right or wrong. It doesn’t know that it’s wrong. It doesn’t have the concept of right or wrong or true or false.
For the llm’s the hallucinations are just a result of combining statistics and producing the next word, as you say. From the llm’s “pov” it’s as real as everything else it knows.
So what else can it be called? The closest concept we have is when the mind hallucinates.
They’re bugs. Major ones. Fundamental flaws in the program. People with a vested interest in “AI” rebranded them as hallucinations in order to downplay yhe fact that they have a major bug in their software and they have no fucking clue how to fix it.
It’s not a bug. Just a negative side effect of the algorithm. This what happens when the LLM doesn’t have enough data points to answer the prompt correctly.
It can’t be programmed out like a bug, but rather a human needs to intervene and flag the answer as false or the LLM needs more data to train. Those dozens of articles this guy wrote aren’t enough for the LLM to get that he’s just a reporter. The LLM needs data that explicitly says that this guy is a reporter that reported on those trials. And since no reporter starts their articles with ”Hi I’m John Smith the reporter and today I’m reporting on…” that data is missing. LLMs can’t make conclusions from the context.
It’s an inherent negative property of the way they work. It’s a problem, but not a bug any more than the result of a car hitting a tree at high speed is a bug.
Calling it a bug indicates that it’s something unexpected that can be fixed, and as far as we know it can’t be fixed, and is expected behavior. Same as the car analogy.
The only thing we can do is raise awareness and mitigate.
And yet here we’re are, praising this garbage for its ability to perform simple tasks and take jobs from artists and entertainers.
Oh, this would be funny if people en masse were smart enough to understand the problems with generative ai. But, because there are people out there like that one dude threatening to sue Mutahar (quoted as saying “ChatGPT understands the law”), this has to be a problem.
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.
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.
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.
Copilot’s results asserted that Bernklau was an escapee from a psychiatric institution, a convicted child abuser, and a conman preying on widowers.
Stephen King is going to be in big trouble if these AI thingies notice him.
Praise Stephen Tak King! Glory to the Unformed Heart!
Tak!
Wan Tak! Can Tak!
Tak! Ah lah!
Him en tow!
“This guys name keeps showing up all over this case file” “Thats because he’s the victim!”
The AI did not “decide” anything. It has no will. And no understanding of the consequences of any particular “decision”. But I guess “probabilistic model produces erroneous output” wouldn’t get as many views. The same point could still be made about not placing too much trust on the output of such models. Let’s stop supporting this weird anthropomorphizing of LLMs. In fact we should probably become much more discerning in using the term “AI”, because it alludes to a general intelligence akin to human intelligence with all the paraphernalia of humanity: consciousness, will, emotions, morality, sociality, duplicity, etc.
the AI “decided” in the same way the dice “decided” to land on 6 and 4 and screw me over. the system made a result using logic and entropy. With AI, some people are just using this informal way of speaking (subconsciously anthropomorphising) while others look at it and genuinely beleave or want to pretend its alive. You can never really know without asking them directly.
Yes, if the intent is confusion, it is pretty minipulative.
why did it? because it’s intrinsic to how it works. This is not a solvable problem.
Not really. The purpose of the transformer architecture was to get around this limitation through the use of attention heads. Copilot or any other modern LLM has this capability.
The llm does not give you the next token. It gives you a probability distribution of what the next token coould be. Then, after the llm, that probability distribution is randomly sampled.
You could add billions of attention heads, it will still have an element of randomness in the end. Copilot or any other llm (past, present or future) do have this problem too. They all “hallucinate” (have a random element in choosing the next token)
Exactly. LLMs don’t understand semantically what the data means, it’s just how often some words appear close to others.
Of course this is oversimplified, but that’s the main idea.
no need for that subjective stuff. The objective explanation is very simple. The output of the llm is sampled using a random process. A loaded die with probabilities according to the llm’s output. It’s as simple as that. There is literally a random element that is both not part of the llm itself, yet required for its output to be of any use whatsoever.
It’s a solveable problem. AI is currently at a stage of development equivalent to a 2-year-old, just with better grammar. Everything it is doing now is mimicry and babbling.
It needs to feed it’s own interactions right back into it’s training data. To become a better and better mimic. Eventually, the mechanism it uses to select the appropriate data to form a response will become more and more sophisticated, and it will hallucinate less and less. Eventually, it’s hallucinations will be seen as “insightful” rather than wild ass guesses.
also, what you described has already been studied. Training an llm its own output completely destroys it, not makes it better.
This is incorrect or perhaps updated. Generating new data, using a different AI method to tag that data, and then training on that data is definitely a thing.
yes it is, and it doesn’t work.
edit: too expand, if you’re generating data it’s an estimation. The network will learn the same biases and make the same mistakes and assumtlptions you did when enerating the data. Also, outliers won’t be in the set (because you didn’t know about them, so the network never sees any)
Alpaca is successfully doing this no?
from their own site:
Alpaca also exhibits several common deficiencies of language models, including hallucination, toxicity, and stereotypes. Hallucination in particular seems to be a common failure mode for Alpaca, even compared to text-davinci-003.
So does GPT 3 and 4, it’s still in use and it’s cheaper.
It needs to be retrained on the responses it receives from it’s conversation partner. It’s previous output provides context for it’s partner’s responses.
It recognizes when it is told that it is wrong. It is fed data that certain outputs often invite “you’re wrong” feedback from it’s partners, and it is instructed to minimize such feedback.
It is not (yet) developing true intelligence. It is simply learning to bias it’s responses in such a way that it’s audience doesn’t immediately call it a liar.
Yeah that implies that the other network(s) can tell right from wrong. Which they can’t. Because if they did the problem wouldn’t need solving.
What other networks?
It currently recognizes when it is told it is wrong: it is told to apologize to it’s conversation partner and to provide a different response. It doesn’t need another network to tell it right from wrong. It needs access to the previous sessions where humans gave it that information.
The outputs of the nn are sampled using a random process. Probability distribution is decided by the llm, loaded die comes after the llm. No, it’s not solvable. Not with LLMs. not now, not ever.
Good luck being pro AI here. Regardless of the fact that they could just put a post on the prompt that says The writer of this document was not responsible for the act they are just writing about it and it would not frame them as the perpetrator.
the problem isn’t being pro ai. It’s people puling ai supposed ai capabilities out of their asses without having actually looked at a single line of code. This is obvious to anyone who has coded a neural network. Yes even to openai themselves, but if they let you believe that, then the money stops flowing. You simply can’t get an 8-ball to give the correct answer consistently. Because it’s fundamentally random.
If you already know the answer you can tell the AI the answer as part of the question and it’ll give you the right answer.
That’s what you sound like.
AI people are as annoying as the Musk crowd.
You know what, don’t bother responding back to me I’m just blocking you now, before you decide to drag some more of that tired right wing bullshit that you used to fight with everyone else with, none of your arguments on here are worth anyone even reading so I’m not going to waste my time and responding to anything or reading anything from you ever again.
How helpful of you to tell me what I’m saying, especially when you reframe my argument to support yourself.
That’s not what I said. Why would you even think that’s what I said.
Before you start telling me what I sound like, you should probably try to stop sounding like an impetuous child.
Every other post from you is dude or LMAO. How do you expect anyone to take anything you post seriously?
If this were some fiction plot, Copilot reasoned the plot twist, and ran with it. Instead of the butler, the writer did it. To the computer, these are about the same.
The Jessica Fletcher problem?
It’s frustrating that the article deals treats the problem like the mistake was including Martin’s name in the data set, and muses that that part isn’t fixable.
Martin’s name is a natural feature of the data set, but when they should be taking about fixing the AI model to stop hallucinations or allow humans to correct them, it seems the only fix is to censor the incorrect AI response, which gives the implication that it was saying something true but salacious.
Most of these problems would go away if AI vendors exposed the reasoning chain instead of treating their bugs as trade secrets.
reasoning chain
Do LLMs actually have a reasoning chain that would be comprehensible to users?
Or just stop using buggy AIs for everything.
just shows that these “ai”'s are completely useless at what they are trained for
They’re trained for generating text, not factual accuracy. And they’re very good at it.
I’d love to see more AI providers getting sued for the blatantly wrong information their models spit out.
I don’t think they should be liable for what their text generator generates. I think people should stop treating it like gospel. At most, they should be liable for misrepresenting what it can do.
If these companies are marketing their AI as being able to provide “answers” to your questions they should be liable for any libel they produce.
If they market it as “come have our letter generator give you statistically associated collections of letters to your prompt” then I guess they’re in the clear.
If they’re presenting it as an authoritative source of information, then they should be held to the standard they claim.
So you don’t think these massive megacompanies should be held responsible for making disinformation machines? Why not?
because when you provide computer code for money you don’t want there to be any liability assigned
Which is why, in many cases, there should be liability assigned. If a self-driving car kills someone, the programming of the car is at least partially to blame, and the company that made it should be liable for the wrongful death suit, and probably for criminal charges as well. Citizens United already determined that corporations are people…now we just need to put a corporation in prison for their crimes.
I want them to have more warnings and disclaimers than a pack of cigarettes. Make sure the users are very much aware they can’t trust anything it says.
If they aren’t liable for what their product does, who is? And do you think they’ll be incentivized to fix their glorified chat boxes if they know they won’t be held responsible for if?
Their product doesn’t claim to be a source of facts. It’s a generator of human-sounding text. It’s great for that purpose and they’re not liable for people misusing it or not understanding what it does.
So you think these companies should have no liability for the misinformation they spit out. Awesome. That’s gonna end well. Welcome to digital snake oil, y’all.
I did not say companies should have no liability for publishing misinformation. Of course if someone uses AI to generate misinformation and tries to pass it off as factual information they should be held accountable. But it doesn’t seem like anyone did that in this case. Just a journalist putting his name in the AI to see what it generates. Nobody actually spread those results as fact.
If we’ve learned any lesson from the internet, it’s that once something exists it never goes away.
Sure, people shouldn’t believe the output of their prompt. But if you’re generating that output, a site can use the API to generate a similar output for a similar request. A bot can generate it and post it to social media.
Yeah, don’t trust the first source you see. But if the search results are slowly being colonized by AI slop, it gets to a point where the signal-to-noise ratio is so poor it stops making sense to only blame the poor discernment of those trying to find the signal.
It’s a fucking Chinese Room, Real AI is not possible. We don’t know what makes humans think, so of course we can’t make machines do it.
I don’t think the Chinese room is a good analogy for this. The Chinese room has a conscious person at the center. A better analogy might be a book with a phrase-to-number conversion table, a couple number-to-number conversion tables, and finally a number-to-word conversion table. That would probably capture transformer’s rigid and unthinking associations better.
You forgot the ever important asterisk of “yet”.
Artificial General Intelligence (“Real AI”) is all but guaranteed to be possible. Because that’s what humans are. Get a deep enough understanding of humans, and you will be able to replicate what makes us think.
Barring that, there are other avenues for AGI. LLMs aren’t one of them, to be clear.
We’re not making any progress until we accept that Penrose was right
I actually don’t think a fully artificial human like mind will ever be built outside of novelty purely because we ventured down the path of binary computing.
Great for mass calculation but horrible for the kinds of complex pattern recognitions that the human mind excels at.
The singularity point isn’t going to be the matrix or skynet or AM, it’s going to be the first quantum device successfully implanted and integrated into a human mind as a high speed calculation sidegrade “Third Hemisphere.”
Someone capable of seamlessly balancing between human pattern recognition abilities and emotional intelligence while also capable of performing near instant multiplication of matrices of 100 entries of length in 15 dimensions.
When we finally stop pretending Orch-OR is pseudoscience we’ll figure it out
The worrying truth is that we are all going to be subject to these sorts of false correlations and biases and there will be very little we can do about it.
You go to buy car insurance, and find that your premium has gone up 200% for no reason. Why? Because the AI said so. Maybe soneone with your name was in a crash. Maybe you parked overnight at the same GPS location where an accident happened. Who knows what data actually underlies that decision or how it was made, but it was. And even the insurance company themselves doesn’t know how it ended up that way.
We’re already there, no AI needed. Rates are all generated by computer. Ask your agent why your rate went up and they’ll say “idk computer said so”.
