You are confusing who is the party that is injured--as many in this thread are. We are not talking about the consumer who buys a non-fairtrade t-shirt, when he would rather had a fairtrade one. It's about the t-shirt producers who is legitimately fairtrade but whose business is now in the shitter because of a lying AI.

LLMs can’t lie. They are incapable of telling either the truth or lying. To the extent that they are in any way useful, it’s recognizing that they are text generators attached to crawlers and other tools that can with the right inputs produce useful generated text that may also incidentally be correct or incorrect.

Businesses might (well, will) suffer because people are misusing AI, but it is a misuse to do anything with it without an additional verification step.

To be clear here, I have no issue with Google taking it on the chin in cases like this, but what the comment I was originally responding to had this:

> errors can be so subtle that it is not possible to recognize them unless you spend an hour researching every fact presented. at that point, what's the benefit of AI? nobody is going to do that.

And my point is this: if it matters, verification is not optional. If it doesn’t matter, then fine, skip the verification step, but if you’re taking whatever text is generated by a GPT at face value without understanding what that is or being able to determine the source inputs for the “claims” it outputs, then you’re part of the problem because sometimes the source is just a GPT-generated web page, and that’s obviously not trustworthy. Sometimes it’s a MediaWiki site page that doesn’t actually exist, but because it’s MediaWiki it’s not going to return a 404. Using a tool requires understanding it including its failure modes, and in the case of LLMs that means: trust nothing, verify everything.

This is not such a decision though; it's a first instance decision at a lower court.

Still - a judge on another case will probably factor this one into their decision, but if they have a radically different view or the case is sufficiently different, they could just as well come to a different conclusion.

But probably long after most of the network is gone

The proof is here: https://www.overleaf.com/project/69dd1d8437eba662fda82929

Much more interesting than the proof would be to see the exact prompts used by Liam Price to generate the proof.

I have no exact prompt to share, but he does write:

> Appreciate the insight! If it's at all of interest, this was a one-shot (supposed) solution in about 80 mins, unlike some other problems like 851 that took over 20 continuations totalling perhaps 15-20 hours of reasoning time.

Source: https://www.erdosproblems.com/forum/thread/1196#post-5365

In the article just before that code:

The loop is of paticular interest to us. Abridged:

I wouldn't either. The difference for female and males reeks of the law of small numbers.

It's comment catnip for HN though.

What's your recommendation for 'some basic business books'?

If you'd read the wikipedia article, you'd know that actual research shows that Betteridge's law is not true. The majority of articles with a yes/no question in the heading, answer the question with yes in the body.

Only accepting bugs with a fix is not a solution. Because who is going to vet the patches? Are you going to accept a Chinese patch for some obscure security issue? This is how real security problems are introduced.

Why not? The three letters are not going to send their backdoored patches under a pseudonym people like you would find suspicious. They would send it (and very likely are doing that already) under the name of "James Smith".

You really should check out much much code in e.g. the Linux kernel is written outside of "the West". It's not the 90s anymore.

I don't believe you.

You prompted chatgpt to create movie based titles and then passed it off as regular output.

With the prompt "suggest 10 titles for this article" + the article text, I get the following _normal_ titles.

1. When the Brain Misreads the World: How Uncertainty Shapes Thought and Behavior

2. CogLinks: A Virtual Brain That Teaches Us How the Mind Adapts

3. The Neural Balancing Act: How the Brain Decides Under Uncertainty

4. Modeling Mental Flexibility: Simulating How the Brain Learns and Adapts

5. Inside the Decision Machine: How New Models Reveal the Brain’s Hidden Algorithms

6. Uncertainty, Meaning, and Misfires: Understanding the Neural Roots of Psychiatric Disorders

7. When Circuits Go Off Course: What a Virtual Brain Teaches Us About Mental Illness

8. The Thalamic Switchboard: Linking Flexibility and Habit in the Human Mind

9. From Neurons to Algorithms: Building a Bridge Between Brain Biology and Psychiatry

10. Toward Algorithmic Psychiatry: Simulating Brain Circuits to Decode Mental Disorders