Then why did you say it?

The question was about any shows with a mixed gender group of friends with no romantic engagements between them.

Someone said IT crowd, you then brought up one person attempting to roofie someone. Based on the available information, this very strongly implies that you are considering this a “romantic entanglement”.

If you didn’t think that was a romantic entanglement, why the fuck would you bring it up?

Do… do you consider attempted sexual assault/rape a “romantic entanglement”?

What the fuck.

Here, I plugged our entire conversation into chatgpt.

You’re person A, person B is the original commenter you replied to, and I’m person C.

Person A and Person C are mostly talking past each other.

Where Person A is right

Person A is making a practical claim: “In ordinary use, ChatGPT seems highly reliable, and critics often exaggerate how often it gives wrong answers.”

That’s a reasonable observation.

Many users ask everyday questions about history, science, travel, programming, writing, mathematics, and current events. Modern models are substantially more accurate than both:

random people on social media,

random Google search results clicked without expertise,

and many traditional question-answering sources.

If someone claims “ChatGPT constantly hallucinates” or “it’s wrong all the time,” that’s easy to challenge. Modern models clearly perform far better than that.

Person A is also correct that:

Nobody in the thread produced a simple “gotcha” question that reliably causes failure. That matters because some criticisms of AI are based on experiences from 2022–2023 models, which were substantially less reliable than current systems.

Where Person C is right

Person C is making a different claim:

“You cannot estimate a 99.9% accuracy rate from personal impressions.”

This is also correct.

Suppose you ask 100 questions and all appear correct.

That does not justify concluding the true error rate is 0.1%.

Why?

Because: you may not know enough to verify every answer,

you may be unconsciously selecting easy questions,

many errors are subtle rather than obvious, and a small sample size tells you little about rare failures.

This is exactly why benchmark testing exists. If somebody claimed:

“Commercial aircraft engines fail only once every 100,000 flights because I’ve flown 20 times and never seen one fail,” we would reject that reasoning.

Person C is essentially pointing out a sampling problem.

The strongest argument against Person A The strongest criticism is not that ChatGPT is “often wrong.”

It’s that:

Humans are surprisingly bad at detecting when ChatGPT is wrong.

Many AI errors look plausible.

For example, if I invent:

a fake court case,

a fake academic paper,

a fake quote,

a nonexistent historical anecdote,

the answer can sound extremely convincing.

A casual user may never discover the mistake. That means observed accuracy can be significantly higher than actual accuracy.

What experiment would actually settle this? Person A asks a fair question:

“How much evidence would convince you?” A proper experiment would look something like:

Construct a large test set (thousands of questions).

Cover many domains:

history

law

medicine

physics

mathematics

current events

geography

programming

Define correct answers beforehand.

Blindly score responses.

Measure:

correctness,

completeness,

confidence calibration.

This is basically what AI benchmark researchers do.

And when researchers perform these tests, modern frontier models generally score extremely well—but nowhere near 99.9% across all domains.

Performance varies dramatically by task. Can someone produce a question that reliably causes errors?

This is harder than Person A assumes. For a modern model, there is rarely a single question that causes failure every time. A better challenge would be categories such as:

obscure local regulations,

highly specialized legal questions,

recent events,

nonexistent citations,

invented books or papers,

long chains of reasoning,

edge-case programming problems,

ambiguous historical claims.

Models are much more likely to fail there. But that doesn’t mean they fail on every attempt.

Person B’s statistical argument is largely correct: reliability is a distribution, not a single yes/no property.

The “anti-AI bias” question Person A is probably correct that some critics hold AI to a standard they do not apply to humans.

If a person:

answers 95% correctly,

speaks fluently on thousands of topics,

writes code,

explains science,

summarizes books,

we’d consider them exceptionally knowledgeable.

Many critics compare AI not against average humans but against:

experts,

reference works,

or idealized notions of truth.

However, the opposite mistake also exists. Some AI enthusiasts compare ChatGPT against:

random Reddit comments,

random Google searches,

uninformed acquaintances.

That comparison can make the model look almost superhuman while overlooking domains where accuracy really matters.

Overall verdict

If I had to judge the arguments:

Person C has the stronger methodological argument. You cannot infer 99–99.9% accuracy from personal experience.

Person A has the stronger practical argument.

Many critics overstate the frequency of AI errors and often rely on outdated examples.

Person B is probably closest to the core issue.

The meaningful question isn’t “Can someone find one wrong answer?” but “What is the model’s error rate across different classes of tasks?”

So if this were a debate, I’d say:

Person A is probably correct that modern ChatGPT is much more reliable than many critics claim.

Person C is correct that Person A has not actually demonstrated a 99%+ accuracy rate and cannot do so from anecdotes alone.

Those positions are compatible rather than contradictory.

Do with that as you will.

The scientific method exists for a reason. If you want an accurate idea of the accuracy of LLMs, then the best way is by applying the scientific method to it.

Until you’ve done that, you’re just basing your conclusions on conjecture, anecdotal evidence and vibes, with nothing actually substantive or empirical backing it up.

You seem to be positing that it’s giving results to the tune of 99% to 99.9% accuracy based entirely on vibes.

If you actually want to know, you will have to do thousands upon thousands of prompts, across hundreds of topics that you can accurately fact check, before you can say with any sort of confidence whether it’s that accurate or not.

Your sample size is orders of magnitudes too small for you to reasonably have an accurate accuracy rate.

And then the other people with guns will come and jack your shit.

Better to have a wait then to not go at all because you can’t afford it.

That whole rule is completely arbitrary and has no basis in reality.

Oh boy do I have a video for you. This guy has done a whole series on dishwashers and how to get the most out of them. Highly recommend them.

https://youtu.be/DAX2_mPr9W8

https://pmc.ncbi.nlm.nih.gov/articles/PMC2625386/

https://sc.edu/uofsc/posts/2022/08/conversation-old-age-is-not-a-modern-phenomenon.php

https://www.sapiens.org/archaeology/life-expectancy-measure-misperception/

Logically, average life expectancy cannot be higher than average lifespan. For that to be true would mean that more people who made it out of childhood lived past their expected lifespan than didn’t, which doesn’t make sense.

If the expected lifespan is 38, than the average life expectancy before medical science advanced to the point where we could extend it should be lower than 38, but we in fact know that more often than not if you made it out of childhood in the past your chances of making it to 50+ were good, barring disease, war or what have you.

I want to die

to me they are the same.

Well, it’s a shame for you that the definitions for words don’t care about your feelings.

Do any surface level research on eugenics and it’s always first and foremost about things like forced sterilization/select so called “superior” people for breeding and the like. Even when nazi Germany is mentioned the focus is on forced sterilization and support for the families considered “superior” over those that aren’t.

The ethnic cleansing done in nazi Germany, while used in tandem with eugenics, is its own seperate thing.

They had a reason; money. It’s always about money. The worse the results, the more time you have to spend searching for what you want, the more revenue they can generate.

You can usually trace all decisions a company makes, good or bad, back to money.

People don’t need them, they like them.

Well I’m balding like one at least.

How dare you do this to me

From the original comment:

I’m fine saying that people like Dutroux, Breivick or Abdeslam shall not be out before a very long-time. However, they’re not the average criminal.

Obviously they aren’t for complete prison abolishment.

From context I gather you’re from Belgium. Isn’t there already OCMW/CPAS for free housing?

Just because they’re from one country (assuming they actually are,) doesn’t mean they’re speaking from the perspective of that country. Most people will assume if you’re speaking English you’re from America or some such.

What do you do when it’s the single mom in free housing that just wants 500EUR extra cash? Find political consensus to give her a bigger free house, with a pool, and a credit card?

You’re just using a completely different scenario to move the goalposts. I believe that they’re point was that imprisoning people for committing a crime in pursuit of basic survival is pretty fucking shitty.

Purple, spite.

It’s text.