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Comment by mattmanser | Hacker News Reader

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mattmanser

What rubbish.

This is the problem with the internet in a nutshell.

A company that publishes libel and harmful content deserves to be fined.

They used to be, but somehow the whole of social media got a get out of jail free card.

And look what's happened. The collapse of western intellectualism, democracy and liberalism happening before our eyes.

We should not make the mistake of giving that disastrous "I didn't make it, I'm just publishing it" get out of jail card to AI companies.

If they somhow can't fix it, while a certain other AI company can magically fix whinnie the poo references, then they should be prosecuted.

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> A company that publishes libel and harmful content

Do you believe that a company who offers an llm to the public, could be said to have 'published' the generative output?

Llms are day dream machines - is it libel if I tell you that I had a dream about you, where you killed a guy? (Has HN just published a harmful lie about you?)

> Do you believe that a company who offers an llm to the public, could be said to have 'published' the generative output?

Of course they did. What other term could you possibly use for it, when one goes to a website and that website itself hands you content? The content is certainly not user-generated, it's coming from the website.

Maybe a different way of phrasing it would be, if a website embedded a rng generator, and you see the random number "eight", then did the publisher publish the number eight, or did they publish a rng? In my opinion, it's the latter. Similarly if the rng generated the number 666, we wouldn't assume the website is making some kind of biblical commentary. We'd recognise that the rng produced a random generative output, similar to the op situation. So, to impugn the publisher of a random text generator based on the random content .... If chatgpt generates a murder threat, or pro-terorrism content, or otherwise shouts fire in a theater, do you believe openai as the publishers should face arrest?

> do you believe openai as the publishers should face arrest?

Absolutely I do, yes. An LLM is not a random number generator. It is a tool built for the sole purpose of generating content.

It's very shocking to me that you would reply in the affirmative.

I think you're saying that companies who host generative AI web services, ought to be legally liable if the ephemeral generative content is illegal.

In your mind, should AI companies try and engineer protection from this huge legal risk? It seems criminally insane for a company to host an AI if they're going to be legally liable for the ephemeral daydream content. You should be shorting goog, meta and msft at the very least, because I make their models generate illegal content every night before bed

> It seems criminally insane for a company to host an AI

Yes?

Do I think that companies that host and share illegal content should be held liable? Of course I do. How could you possibly feel any other way?

I'm not shorting anything because I'm not a gambler and my opinion on what should be illegal has no basis in what actually is illegal in USA, a country I have never set foot in.

The problem with the Alex Jones defense is that they can never quite seem to figure out whether they're just a silly random number generator, or The Revolutionary Future Of Work(tm) that we should be okay with investing trillions of dollars into training and operating.

The purpose of a system is what it does.

If you publish a "random number generator" that consistently publishes defamatory statements about a person at a much higher frequency than statistically plausible, that's not a random number generator, it's a defamatory statement generator, no matter what you call it.

In court, your intentions might matter just as much as what the system does, but even there, the name you give it is pretty much irrelevant.

>is it libel if I tell you that I had a dream about you, where you killed a guy?

This isn't even closely analogous. I don't know if you could come up with a more bad faith argument.

This was outputting a lie, presented as a fact, to anyone in the world that searched the name.

There is a difference in context (dream vs. fact), difference in scale, difference in expectation (machine outputting what is advertised as accurate information vs. random chatter on a forum where the expectation of accuracy is not a selling point), different methods of redress (chatter can correct you via comment, not so much with an LLM).

> This was outputting a lie, presented as a fact, to anyone in the world that searched the name.

I think you are wrong.

From my understanding, the complainant opened a new chat window and typed "who is forename surname?"

The daydream machine then daydreamed some output text, as is it's function.

Likewise, you can go now to any llm and ask it a specific question like "what is the minimal cheese principle?" (Which I've just made up) And many will daydream a consistent answer for you. As is their function

>The daydream machine then daydreamed some output text, as is it's function.

These are not advertised as daydream machines.

They compete on how accurate they they are against various accuracy benchmarks.

The average person who uses them does so with the expectation of accurate results, you know, as they are advertised. Accuracy and speed are pretty much the entire business model.

No, they generally do not compete on accuracy benchmarks afaik.

GitHub/openai/simple-evals is what I checked here, and no, openai do not compete on accuracy benchmarks as far as I can tell. So I'd be interested in seeing what led you to think that, and also what led you to earlier claim that anyone typing in the complainant's name saw the same hallucination.

>No, they generally do not compete on accuracy benchmarks afaik.

"Get Answers" is literally at the top of ChatGPTs landing page. You think the average person interprets that to mean "Get inaccurate answers"?

Google "AI benchmark" and almost every result is an assessment of the accuracy of various models. What do you think they compete on? How do you think they measure the improvement of one model to the next?

Here's OpenAI's "Optimizing LLM Accuracy" https://platform.openai.com/docs/guides/optimizing-llm-accur...

Pop this in Google and see the pages of results about accuracy: site:openai.com "accuracy". To claim that they don't optimize for accuracy confirms to me that you are not discussing this in good faith. Perhaps you are just trying to be contrarian or something, I don't know.

>and also what led you to earlier claim that anyone typing in the complainant's name saw the same hallucination.

Well, it says right in the article that different people received the same result.

Why are the goalposts moving? Actually, nevermind, I don't care to continue the conversation.

I think if you take a few moments to read carefully.

You'll see that AI companies, including openai, are generally not competing on accuracy benchmarks.

For example, here are the benchmarks on which open ai seem to be trying to compete.

MMLU: Measuring Massive Multitask Language Understanding,

MATH: Measuring Mathematical Problem Solving With the MATH Dataset,

GPQA: A Graduate-Level Google-Proof Q&A Benchmark,

DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs,

MGSM: Multilingual Grade School Math Benchmark (MGSM), Language Models are Multilingual Chain-of-Thought Reasoners,

HumanEval: Evaluating Large Language Models Trained on Code,

I don't know why I'm bothering. But notice how all of these explicitly mention accuracy? And how they are benchmarking the accuracy of the LLM against a known dataset? How accuracy is the primary metric they are evaluated on? Maybe it's because they are trying to improve the accuracy of the models...

First line of the abstract of MMLU: "We propose a new test to measure a text model's __multitask accuracy__."

Fourth line of the abstract of MATH: "To facilitate future research and __increase accuracy__ on MATH"

Second line of GPQA abstract: "We ensure that the questions are high-quality and extremely difficult: experts who have or are pursuing PhDs in the corresponding domains reach __65% accuracy__ [...] while highly skilled non-expert validators only reach __34% accuracy__"

Fifth line of the DROP abstract: "We apply state-of-the-art methods from both the reading comprehension and semantic parsing literature on this dataset and show that the best systems only achieve 32.7% F1 on __our generalized accuracy metric__"

From the MGSM paper: "MGSM __accuracy__ with different model scales."

Models are designed to output accurate information in a reasonable amount of time. That's literally the whole goal. The entire thing. A math-specific model wants to provide accurate math answers. A general model wants to provide accurate answers to general questions. That's the whole point.

None of those relate to factual accuracy about a guy in norway

How much farther can you move the goalposts? We're already almost on another planet.

You ignored almost everything in my original comment and hyper-focused on accuracy. Then, when confronted with the fact that every single example benchmark you provided is a measure of accuracy, you now say "well, it's not a benchmark about a specific person in norway". Obviously not!

The MATH benchmark doesn't ask "what is 2+2", either. Your argument is "well, math-focused models aren't expected to accurately answer 2+2 because it isn't in the MATH benchmark". It's ridiculous.

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