https://archive.is/6Nbuf

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Just an outage I think, the archive works for me now

It's slammed. HN strikes again.

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.

The models have updated but the biggest change is providers leaning in to them being "stochastic parrots," aka probabilistic computing, and if p(good response) > 0.5 then running the algorithm over and over again improves accuracy.

Of course it's gussied up as "mixture of agents" "reasoning traces" "agentic dispatching" but high-level it's Randomized Algorithms 101.

Oh, that's an interesting angle! Do you know of texts or concepts I can look up? It might improve my coding by quite a bit.

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 out to be possible with fairly basic statistical text generation, because fooling humans is easy.

It would've been nice to reserve "AI" for superior human-like intelligence capable of genuine common sense and reasoning. The irony is that the startup founders most worried about "AI" have created so much hype and funding that we may very well figure out how to build "real" AI.

It would've been nice to reserve "AI" for superior human-like intelligence capable of genuine common sense and reasoning.

What would a frontier API have to be able to do to satisfy you?

To answer for OP:

We are now calling text and image generators "intelligent" in the same way a spell checker is intelligent.

Whatever it's become, "AI" research started as a way to study digital neurology, or how to digitize a mind, not just how to generate data.

The Turing Test should have had a caveat, it needs to fool a, "non-stupid" person, and we still have not gotten even close to passing that version.

What exactly would a 'non-stupid' person do to catch the latest models on a Turing Test? Aside from being aware of AI 'tells' like em-dashes.

If this was true. I would repalce myself with ai that pretends to be me on slack.

my coworkers would know almost immediately if i did that.

my coworkers would know almost immediately if i did that.

The same would happen if you were replaced by any random human.

that would be a silly test then

It sounds kind of like you made up a silly test then.

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 sense reasoning, causal inference, learning novel rules, transferring knowledge novel situations, recognizing ambiguity, etc.

Can you give me one example that works on Claude right now?

I'm never sure whether this indicates "no reasoning present" or you've just hit an odd behaviour in the AI such that its reasoning fails. For example, you present a problem in a way that's dissimilar to the way problems are presented in its training set. That doesn't mean it's not reasoning, just it can only reason correctly in some circumstances.

The models are continually patched with training and post-training. All you have to do is find an area they haven't patched yet, and they'll be just as stupid. I run into deep technical examples every day where they fail in the most basic ways no human ever would.

I'm pretty sure most people building these models would admit they don't operate as human-like intelligences? It's baffling that anyone thinks they are.

Yes I agree, they’re an alien kind of intelligence.

But that doesn’t mean they don’t reason.

I get what you're saying but this is kind of a semantic game.

These LLM models/agents absolutely do not reason in the sense that humans do, so you're quietly redefining the word.

You can say of course decide to call them an "alien kind of intelligence" that "reasons" but you could just as reasonably say that calculators are an "alien" kind of intelligence that "reasons" about math differently than us.

I would walk

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.

We've chosen to call Deep Blue and Half-Life 1 NPCs "AI" too.

It boggles my mind that this "b-b-but it's not actual real AI" whine is even a thing. Were people saying this living in the cave for the past 5 decades of AI research?

You can call your little doggy "AI" if it makes you happy.

But when you call something "AI" and it tells you to walk instead of drive to the car wash, you're not talking about the "AI" science fiction authors were dreaming of.

Sure, just keep moving the goalposts. It's not a "real AI" because it can't take over the US military command and kick off WW3 and finish the survivors off with killer robots yet!

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.

Funny enough, calculators went through this exact same thing when they came out. "If the calculator can do math for the students, will they still learn?"

We didn't have confused people claiming calculators were human-like intelligences doing math.

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.

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.

It can however select a girls school as a military target, and did.

...did it really, though?

Yeah, it did. I don't think this is really in doubt.

Especially since the military refuse to confirm it had any human oversight, which after all would have been a routine thing to talk about before AI targeted things — which is why we have the phrases "fog of war", "human error", "unfortunate mistake", etc.

It will take a while to shake out — we won't know for sure for a decade, I suspect, but it seems very likely this will prove to be an AI error.

Looks more like standard decision-washing finger pointing to me. "AI did it" is both hypey and also conveniently distracts from an uglier reality:

Palantir Technologies [built] Maven into a targeting infrastructure that pulls together satellite imagery, signals intelligence and sensor data to identify targets and carry them through every step from first detection to the order to strike.

The building in Minab had been classified as a military facility in a Defense Intelligence Agency database that, according to CNN, had not been updated to reflect that the building had been separated from the adjacent Islamic Revolutionary Guard Corps compound and converted into a school, a change that satellite imagery shows had occurred by 2016 at the latest. A chatbot did not kill those children. People failed to update a database, and other people built a system fast enough to make that failure lethal.

https://www.theguardian.com/news/2026/mar/26/ai-got-the-blam...

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

It's one example that points out a major (possibly fundamental) flaw. I can point to prompt injection as another example. There are tons more if you're interested.

Are you actually claiming LLMs operate based on human-like intelligence?

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.

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 feature it had implemented completely backwards. (I noted that if I had been using a language that required formal proofs, that wouldn't have helped either: it would have just provided a formal proof for the absurd implementation!)

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.

To err is human, it also seems that to err is AI.

But when you call something "AI" and it tells you to walk instead of drive to the car wash

There are so many other sites. So many others. Why are you here?

You can call your little doggy "AI" if it makes you happy.

Or you can keep calling them stochastic parrots as they solve decades-old open problems. The real question is how useful they are, and the answer "not at all" increasingly requires flat-earth levels of denial.

it tells you to walk instead of drive to the car wash, you're not talking about the "AI" science fiction authors were dreaming of

They sort of are. Think of Data from Star Trek TNG failing to understand figures of speech. Not that it's terribly relevant; humans regularly fall for tricks like "Paris in the the spring" or "where do you bury the survivors".

Or you can keep calling them stochastic parrots as they solve decades-old open problems.

I didn't use that phrase at all. But computers calculated digits of π to trillions of digits. With a chat interface for a Python math program would look like the most impressive math genius if you took it back a few decades.

The real question is how useful they are...

That's not the "real question" but an entirely different question that is easily answered. Nothing I wrote suggested they're not incredibly useful.

Data from Star Trek TNG failing to understand figures of speech.

These are just little instances of bad writing. Data is very much an attempt at displaying a human-like intelligence.

That's not the "real question" but an entirely different question that is easily answered. Nothing I wrote suggested they're not incredibly useful.

Oh, ok then. That does change things a bit. The impression I'm getting is that you were suggesting they're not. What's succinctly the thing you're objecting to?

Is it Anthropomorphization?

I mean, sure, but watch out : when defending on that axis, it's easy to slip into Anthropodenial, right? Frans de Waal (from the same science that invented "Don't Anthropomorphize" ) can tell you about it.

With a chat interface for a Python math program would look like the most impressive math genius if you took it back a few decades.

Well, exactly. Whether any particular generation of AI or software is yes/no "Like A Human Being" is probably the least interesting question axis. It's all just anthropocentrism.

Is that the thing you're trying to lay your finger on?

What I'm pushing back on is, apparently, that some people genuinely believe these LLM-based computer programs are human-like intelligences.

In reality, they're more like very good search engines that output relevant snippets of text. If you run them in a loop (feeding them their output as input) you can make them return even better search results.

The software developers who created these systems used sexy words like "reasoning" and "thinking" to describe this search process. They used words like these because they're trying to make money and it sounds cool, not because they've actually re-created human cognition.

It seems to me that they used words like these because the LLM-based computer programs they sell can solve problems which humans apply reasoning and thinking to solve. Why do you think it's more than that?

The real question is how useful they are

No, it is not.

and the answer "not at all" increasingly requires flat-earth levels of denial.

No, it does not.

For me, after ~25 years in the skeptics movement, I think the parallels with supplementary, complementary and alternative medicine are most useful.

I choose that term intentionally: its initials are S.C.A.M. and that's exactly what it is. As Tim Minchin and Alan Kay both noted, "we have a special term for alternative medicine that's been tested and shown to work. It's called 'medicine'."

If it worked, it'd be normal standard clinical medicine. But it doesn't work, and so it isn't.

And yet, SCAM is a multi-billion-dollar industry. People have ostensibly official qualifications like "ND", for "naturopathic doctor", even though that person is not a doctor and can't make you better from any kind of illness at all. Colleges teach it, millions use it, and yet, it does not work.

Which means we need to ask:

1. What does "It works! It's useful!" really mean?

2. How do we know it does not in fact work?

As a handy example, let's look at homeopathy.

Here's a quick list of things widely believed...

* It's traditional. It isn't. It was invented by Samuel Hahnemann in 1796. * It's a kind of herbal medicine. It isn't. One widely-used ingredient is duck's liver ("Oscillococcinum"). Ducks are not herbs and neither are their livers. * It's been proved to work. It hasn't.

We can go through the principles and prove it doesn't work even without going into a laboratory.

The principle is, "like cures like." A substance that causes symptoms like a given disease can treat that disease.

Fact: they can't.

Then we make that substance stronger by successive, succussive dilution.

Fact: it doesn't. That's why we say things are "watered down".

Succussive: you have to mix the diluted substance by banging the bottle against a copy of Hahnemann's book. Dude knew how to make money.

Fact: Dilution does not work.

That's why we call things "watered down." It makes them weaker.

Sufficiently high dilutions can be shown by statistics to have not a single molecule of the substance left, but that's OK because "water has a memory".

Fact: water does not have a memory.

We know from the principles it cannot work.

Relevance to AI: we know how the transformer algorithm works. It cannot think. Adding a few feedback loops for more plausible, but much more computationally expensive, answers does not miraculously add thinking, any more than banging a test tube of water and duck's liver magically mixes it better.

But people believe it, so it's been tested. It doesn't work. It doesn't work on people, or in vivo meaning when tested on animals, or in vitro meaning when tested in the lab on cell culture, or in silico which means in computational simulation.

*BUT!*

Most people get better from most things. This is called "reversion to the mean" and if it weren't so the first cold would have wiped out the cavemen.

What it can do, like all SCAM treatment, is make people feel better.

Being treated by a nice friendly doctor makes people feel better. It does not make them better -- it is only a state of mind.

That can sometimes marginally help gravely ill people rally, but only very rarely.

There is also the placebo effect, also much misunderstood.

This makes someone FEEL as if they'd had medicine if they think they've had medicine.

They do not get better. They just feel better for a bit. If they are ill, they remain ill. If they are dying, they still die.

But it might hurt less.

The placebo effect is very strong. Medicine from a person in a white coat works better than form the same person in street clothes.

Very big pills work better than smaller ones... but very small pills work better still, as a tiny pill suggests to people it's a very strong drug.

This is what "But AI works!" really means.

It makes people think they're doing less work -- in tests, they in fact do more, checking and fixing. Unless they don't check or fix, in which case, they are irresponsible fools.

It makes people think it can do amazing things because it can find prior art in its corpus they couldn't find -- or didn't look for, or know how to search for.

It does not save the need for skills.

Experienced practitioners can front-load the work with really detailed prompts which cover exceptions, edge cases, and things that novices don't know about. But the novices don't know that they don't know. (It enhances the illusion of competence. It helps the skilled more than it helps the unskilled, but neither realises, and it prevents the unskilled learning by trial and error. It reduces the supply of skilled workers.)

The reason AI works is the reason that people see the face of Jesus in slices of toast, as someone said recently.

The reason AI works is the reason that people see the face of Jesus in slices of toast, as someone said recently.

Apparently my unit tests can see faces in slices of toast.

Good for you.

Now, shall we discuss the ecological and commercial cost of that?

So, I notice a https://rationalwiki.org/wiki/Gish_Gallop , followed by https://rationalwiki.org/wiki/Moving_the_goalposts

Let's just say I'm skeptical of your skepticism ;-)

It boggles my mind that this "b-b-but it's not actual real AI" whine is even a thing.

As I understand it, a major reason it's a consistent chorus is because people don't want the "AI is here" talk to drown out (and thus slow the arrival or distribution of) speech/text/popular-understanding about actual strong AGI.

To make an analogy, it could be like this:

Some people were expecting 100 tulips (because they were told tulips are available and can be ordered), and they ordered them. They received 100 daisies. And were saying "OMG, THE TULIPS ARE HERE! THE TULIPS ARE HERE!"

A nearby observer might have said, "You know, those are daisies. Not tulips."

And 95% of people might have said back, "WE GOT 100 TULIPS! SAYS SO RIGHT HERE! THEY ARE BEAUTIFUL! STOP BEING A NAY-SAYER! THESE ARE BEAUTIFUL TULIPS!"

The 5% could just to think to themselves, and could get chastised by the crowd, if they were to say say it out loud: "Well, those are not nearly as beautiful as tulips. And if you don't take it up with the seller, you may never receive the real tulips you were after. Since you think or at least act as though you've been sold them already."

In my eyes that "chorus" is just insecurity talking.

If it's not "actual real AI", we can keep pretending that human intelligence is something distinct and special - and that what our computers are doing now is some sort of other, obviously fake and vastly inferior thing.

When Deep Blue won at chess, people didn't revise their estimates of AI capabilities upwards. They revised their estimates of how much intelligence is required to play chess at world level downwards, by a lot. Surely playing chess must have never required any intelligence in the first place!

Now, the list of things that "must have never required any intelligence in the first place" includes gems like "reading comprehension at high school level", "copywriting", "frontend work", "CTF tasks", "theory of mind", "arguing with people online" and more.

If the goalposts were moved far enough that the claim to "actual intelligence" is denied to a double digit percentage of human population, hasn't something gone wrong somewhere?

It has, but I think in both directions.

It used to be assumed that playing chess would require the same level of general purpose problem solving cognitive skills that the best chess players possess. But of course a Chess grandmaster that spend a few minutes learning Go can beat a Chess AI at Go with no trouble at all, because a chess AI is incapable of making effective moves in Go at all. Clearly those expectations were incorrect. Pointing that out isn't revisionism.

On the other hand, intelligence is an incredibly broad term. About as broad as a term can get. Arguably Eliza, or an Excel macro has some degree of decision making ability in some sense, it's just unbelievably primitive.

So, we need to be clearer what we mean by intelligence. We're learning that as we go along. At least now we have a few more bits of the map between us and an IF statement visible to us.

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.

This no news for people who study philosophy, as it was known since the 1980s when John Searle described the Chinese room thought experiment.

Even Turing him self did envision the Turing test as something to pass as intelligence, but rather as a more useful replacement for the troubled term.

That said, I think your quest is doomed. There will never be a superior human-like intelligence. Forever is a long time, but my reasoning for believing this is the same reason Turing offered a replacement. Intelligence is way too vague to be useful as a measurement for anything. And if we ever discover something that is more intelligent them humans (by whichever definition of intelligence) we will simply redefine intelligence to exclude that.

The Searle's Chinese room thought experiment usually reveals more about those who think it rules out a machine intelligence than it does about AI.

It rests on a staunch unwillingness to even consider the possibility that a computational process encode intelligence and reasoning, in favour of looking for the intelligence in the medium the computation runs on, and going "a-ha!" when there is nothing that looks intelligent there.

I agree with you that there will certainly be people who just continuously redefine the words to avoid accepting that AI is intelligent or reasoning, exactly for that reason - people have avoided pinning down an objective, measurable definition of these terms for a very long time, at least in part because it leads to some very uncomfortable discussions.

In particular how to define them so that they don't exclude an uncomfortable proportion of humans, but at the same time won't include entities people don't want to include (be it certain animals, or AI)

To a lot of people, the notion that there isn't a clear binary divide between human and non-human is deeply disconcerting.

It rests on a staunch unwillingness to even consider the possibility that a computational process encode intelligence and reasoning.

You are absolutely right, but it may surprise you that I consider that a feature, not a bug. I firmly hold that intelligence is not a useful term in science nor philosophy. If we want to compare computational capabilities between machines and humans we are better of being specific in what we measure. If we want to measure how well a computer can fool a human in the guessing game, then we don’t need intelligence to describe it, we can (and should) be more accurate in describing its capabilities.

I actually think Gardner was on to something when he described his multiple intelligence model. His only error was using this fraught term to describe his model. He would have had a better theory if he had described it as multiple capabilities or multiple skills (however if he had said that people would have simply reacted with “well, duh!”)

We don‘t need intelligence, this term is only useful if you are trying to prove white supremacy using racist pseudo-science, what you overly courteously described as “uncomfortable discussions”.

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.

They fucking solve original math problems that you can't solve. They are indisputably intelligent, and they are indisputably artificial. That makes them indisputably "artificial intelligence." Denying that (or downvoting it, for that matter) is up there with denying evolution and the Moon landings.

It's time to start flying a different flag. You're making humans look stupid.

It turned out to be possible with fairly basic statistical text generation, because fooling humans is easy.

Yes, fooling humans is easy. Yet somehow we still consider ourselves qualified to say what is "intelligent" and what isn't, even though we can't seem to define the term.

[flagged]

Personal attacks aren't allowed here.

As I just mentioned at https://news.ycombinator.com/item?id=48981624, we need you to stick to HN's rules if you want to keep commenting on the site.

https://news.ycombinator.com/newsguidelines.html

but these aren't autonomous intelligences

Well, the labs are in a weird bind. They need to keep increasing autonomy so the agents can do increasingly complex, long-horizon tasks. But at the same time, they're closely guarding against autonomy in the sense of "pursuing its own goals."

Over the past year and a half especially, several labs have mentioned adding safeguards against self-replication, resistance to shutdown etc. (Notably, shortly after they all started bragging about involving them in the AI training loop itself, i.e. "self-improvement".)

My point here is that the autonomy of which you seek might be only a few small mutations away, but the labs are actively working to prevent such a mutation. I don't expect that situation to last for very long.

Not that I expect an AI lab will be overtaken by a rogue intelligence any time soon, but that as the cost of training goes down, I expect more "open minded" organizations and individuals to become involved.

It only takes one.

That's going to be the beginning of a new era of biology, and it's a little unsettling to think about.

What I'm seeing here, reading this thread, is that "intelligence" isn't a thing.

"Thing" in terms of a quantifiable that you can measure with tools and reason about, reproducibly. Everyone's got some idea what it is, so you get lots of different angles, but no one has an Intelligence Ruler we can hold up to a text output and say, yep, this one's got an INT of 14.

Seems to be the crux of the disagreement.

The field has been called Artificial Intelligence for what, 60 plus years now. Why is it a problem now?

Because we've spent something like 2 trillion dollars on it, as we hurtle into global climate collapse and WW3.

AI that we have now is not a digital animal in capability or kind, and is not currently anywhere close to taking over.

But it's still in its present form very intelligent in meaningful and useful ways. And it is not too soon to talk about concerns of a potential existential threat in the future. Because it could sooner than we might realize, threaten our existence.

Because of the potential, we should have a culture of caution as we continue to rapidly improve AI.

It will only become an existential threat (in my humble opinion) when they can run influenced inference locally offline almost instantaneously. Then we need to be concerned about not being able to switch it off.

What do you mean "influenced inference" almost instantaneously? My laptop from 6 years ago can run an agent in a very fast loop in a web browser. It's not going to take over anything though since it's Gemma 4 E2B with only 2 billion parameters.

"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.