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Mythologizing AI makes it more likely that we’ll fail to operate it well (2023)

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Re: Mythologizing AI makes it more likely that we’ll fail to operate it well (2023)

#71

Earlier quoted context omitted.

We're on Hacker News. Do I really have to point out the existence of social engineering to you? Or that scamming old people out of their life savings is a profitable enough activity that there are entire call centers dedicated to the task? Humans keep overestimating just how high the bar of "human-like intelligence" is.

Drawing the conclusion that "humans fail" and "models fail", so they must be similar, is very wrong. You could have humans calculate 2+2 all day and get a surprisingly high error rate. That reveals a flaw in how humans operate. LLMs fail for entirely different reasons. Their mistakes don't imply they're human-like at all. It's not about the error rate.

You're saying that a class of mistakes points out a "major (possibly fundamental) flaw". I'm pointing out some very similar classes of mistakes in humans - well known, well documented and widely exploited. They just keep paying the "IRS" in gift cards, buying lottery tickets and getting the captain's age wrong.

If you're using the existence of flaws in LLMs to deny the claim of intelligence to them, then why do "generally intelligent" humans exhibit some impressively similar-looking flaws?

And, if we're talking about that conspicuous similarity - do they actually fail "for entirely different reasons"? Or do you just want the reasons to be "entirely different" - and not the same reasons viewed at a different angle?

Because the similarities between humans falling for trick questions or scams, and LLMs falling for adversarial questions or prompt injections don't look coincidental to me at all.

One of the oldest patterns in scamming is overwhelming and confusing the victim. Numerous prompt injection methods seek to overwhelm and confuse an LLM - if an LLM can't keep track of things, can't grasp what's going on, it's far more likely to lose track of what's a prompt and what's data, overlook past instructions or go past its behavioral guardrails.

And humans who fall for trick questions like "1kg of feathers" or "captain's age" due to shallow attention and naive pattern matching? They fail in surprisingly similar ways to how LLMs fail on SimpleBench tasks that are filled with overwhelming adversarial distractors. Many "trick questions" are tricky to humans and LLMs alike - to the point that it's unlikely to be coincidental.

Re: Mythologizing AI makes it more likely that we’ll fail to operate it well (2023)

#72

Earlier quoted context omitted.

That's how it works though. The moment we have "AI" and see something working, it immediately ceases to be magic because "it's just a program after all." Aligning on a true definition of Artificial Intelligence is a very vexing problem.

We could've slapped a chat interface on calculators and called them "AI" because they can do superhuman math instantly. Most technical people would've thought that was stupid. LLMs are the same kind of category mistake.

Let's talk about category mistakes. You've been here since 2007, according to your other reply. You understand that calculators have as much to do with mathematics as telescopes have to do with cosmology. Right?

If someone unskilled at math brings a calculator to an international math competition, they will not succeed at solving many problems. Most likely, they will solve none at all. But if they bring a frontier LLM (and succeed at concealing it from the organizers), they can walk away with a gold medal. Such a feat requires intelligence... and if the contestant didn't provide the intelligence himself/herself, where'd it come from?

That means that analogies involving calculators are completely useless when the topic is AI. Calculators are not, and can never be, intelligent. LLMs are nothing even remotely like calculators.

Re: Mythologizing AI makes it more likely that we’ll fail to operate it well (2023)

#73

Earlier quoted context omitted.

Maybe just a very rigorous version of the Turing test? Modern LLMs can superficially simulate conversation but it's trivial to force them into revealing their non-human like intelligence. They've been "patched" since but all models fail basic tests like "Should I walk or drive to the car wash which is 100 feet away" by recommending you walk. So you'd just ask questions that require theory of mind, abstract and common…

I would walk

Me too. At least it doesn't say I need to wash my car.

Re: Mythologizing AI makes it more likely that we’ll fail to operate it well (2023)

#74

Earlier quoted context omitted.

We could've slapped a chat interface on calculators and called them "AI" because they can do superhuman math instantly. Most technical people would've thought that was stupid. LLMs are the same kind of category mistake.

Let's talk about category mistakes. You've been here since 2007, according to your other reply. You understand that calculators have as much to do with mathematics as telescopes have to do with cosmology. Right? If someone unskilled at math brings a calculator to an international math competition, they will not succeed at solving many problems. Most likely, they will solve none at all. But if they bring a frontier LL…

> If someone unskilled at math brings a calculator to an international math competition, they will not succeed at solving many problems.

> But if they bring a frontier LLM (and succeed at concealing it from the organizers), they can walk away with a gold medal.

Of course you could win all kinds of math competitions with a concealed calculator. Maybe you'd need a fancy one, like a little SBC running Python. Anything complex and timed would be easy to win. You'd look like a genius to anyone who didn't know you had it.

> Such a feat requires intelligence... and if the contestant didn't provide the intelligence himself/herself, where'd it come from?

From computer software running on computer hardware, just like a calculator.

Calculating trillions of digits of pi also requires intelligence far beyond human capacity.

Computers displaying intelligence doesn't imply human-like intelligence. This is the source of confusion.

Re: Mythologizing AI makes it more likely that we’ll fail to operate it well (2023)

#75
post #10
post #8

I think the biggest pushback this article will get here is the date. Although all he's saying is basically, "It's a tool, not a silver bullet". But the article is 3 years old and people will note that the models have been updated since then.

Sure, but they're still LLMs and still do the same things largely the same way they did 3 years ago. There are some architectural changes, and maybe these will merit a re-assessment over time, but fundamentally it's still the same basic technological approach refined and scaled up.

And I don't disagree, but the posting of the article feels more like bait of a sort.

But I've noticed that if you mention anything that could be seen as slightly critical of LLMs, you'll get people out of the woodwork suggesting that the state of the art has made your criticism invalid.

Re: Mythologizing AI makes it more likely that we’ll fail to operate it well (2023)

#76

"A.I."[1] like "technology"[2] is a term colloquially reserved for things that don't work yet. Once something works, we have to call it something else. 1. "Every time we figure out a piece of it, it stops being called AI; it becomes just computation." - Ray Kurzweil 2. "Technology n. - Something that doesn't work yet." - Douglas Adams

To be clear the root cause of this phenomenon is that the task was solved using methods that obviously have nothing to do with intelligence, so "AI" doesn't apply at all.

Re: Mythologizing AI makes it more likely that we’ll fail to operate it well (2023)

#77

Earlier quoted context omitted.

Drawing the conclusion that "humans fail" and "models fail", so they must be similar, is very wrong. You could have humans calculate 2+2 all day and get a surprisingly high error rate. That reveals a flaw in how humans operate. LLMs fail for entirely different reasons. Their mistakes don't imply they're human-like at all. It's not about the error rate.

You're saying that a class of mistakes points out a "major (possibly fundamental) flaw". I'm pointing out some very similar classes of mistakes in humans - well known, well documented and widely exploited. They just keep paying the "IRS" in gift cards, buying lottery tickets and getting the captain's age wrong. If you're using the existence of flaws in LLMs to deny the claim of intelligence to them, then why do "gene…

> If you're using the existence of flaws in LLMs to deny the claim of intelligence to them...

That's not the point at all. It's the fact that they fail in ways completely unlike humans.

You also have the burden of proof reversed. Its on you to prove these LLM agents are human-like intelligences if that's your claim. No one can prove this because it's false.

Re: Mythologizing AI makes it more likely that we’ll fail to operate it well (2023)

#78

Earlier quoted context omitted.

Maybe just a very rigorous version of the Turing test? Modern LLMs can superficially simulate conversation but it's trivial to force them into revealing their non-human like intelligence. They've been "patched" since but all models fail basic tests like "Should I walk or drive to the car wash which is 100 feet away" by recommending you walk. So you'd just ask questions that require theory of mind, abstract and common…

If an alien lands on Earth and learns English, would you deem it non-intelligent if you can tell it apart from a human in conversation? I think we should consider slime mold intelligent, and realise that it's a spectrum. Path finding is AI. There are probably forms of intelligence we have yet to discover.

If an alien landed we could decide whether it seems to have a human-like intelligence or not. It could be incredibly intelligent but very non-human-like.

Re: Mythologizing AI makes it more likely that we’ll fail to operate it well (2023)

#79

We're choosing to call LLMs (and the little "harness" programs that query them in loops and execute their output) "AI", even though it doesn't make much sense. I absolutely love this technology but these aren't autonomous intelligences. They're little programs executing Bash scripts from JSON output. Our ideas about AI were naive. We thought passing a basic Turing test would require human-like intelligence. It turned…

Oh, you're talking about "AGI"! In the 90's we started using the term, you should catch up!

Sorry to tell a fellow Jacob that you're the one who is out of date. The kids are calling everything "AI" and they mean "AGI", and that's the complaint.

Re: Mythologizing AI makes it more likely that we’ll fail to operate it well (2023)

#80
post #63

Earlier quoted context omitted.

That's a bad and tired example as it confuses people just as much as it does (or rather, did?) LLMs.

Yeah it's a trick question, the human error rate for it was about 30% (higher depending on the country). The thing there though is that, if a human were given time to think about it, they'd probably go "hang on a minute", and with the LLMs that didn't seem to happen. They just kept confidently reasoning down the absurd path. That reminds me, I recently had an AI write a ton of tests proving the "correctness" of a fea…

> Yeah it's a trick question, the human error rate for it was about 30% (higher depending on the country).

Error rate doesn't prove anything. The nature of the errors is what matters.

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