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hatthew 48 minutes ago [-]
My comment about humanity's last exam being a misnomer is included, and I proposed better ideas about what a last exam could look like. One of the things I said was "solve an open math problem" which has conclusively been done with Navier-Stokes (regardless of the controversy surrounding that). However, in the spirit of clarifying the goalposts, AI has only passed 1/6 of the tests I proposed. 17% is not a passing grade, so I'd say no, my challenge has not been met.
Another thing to note is that the (presumably AI-generated) summary of my challenge does not accurately represent what I wrote, listing only half the things I said and saying "or" rather than "and".
joegibbs 3 hours ago [-]
There's one of mine in there where I predicted in 2023 that it would be 20 years until AI would be reliably able to entirely build and deploy arbitrary applications from a prompt. I was off by about 18 years on that one!
ASalazarMX 2 hours ago [-]
One challenge of mine is a self-hosted AI doing my full tax return, without errors that would get me in trouble. Bonus points if it exploits legal loopholes.
I want AI to replace me in my chores, not in my enjoyable activities.
underlines 19 minutes ago [-]
i filed my swiss taxes for 2025 in 2026 (april) by dumping everything (local tax law, tax guide, my and my wife's documents, bank statements, income statements, etc.) into a folder and asking claude to fill it out. i had nothing to fix. submitted it.
notJim 1 hours ago [-]
In recent years, I have not been able to find a human CPA who can accomplish this feat. (If anyone has a reco who's taking new clients in the US west coast, feel free to email me.)
tehjoker 2 hours ago [-]
That particular one is solved in other countries. The tax authority just sends you a bill and you text yes or no. Only people with very complex situations need to file.
They don't do this in this country because (a) it's a political project to make people to sympathize with the rich by feeling their pain (b) it's a great scheme for legalized corruption by creating incentives to build companies around a fake problem.
avadodin 1 hours ago [-]
Literally everything could be derived automatically by the government in current year so filing taxes feels like entrapment, but I like that idea.
They not only do that —saving you so many worries— but then you get to be medieval about it and say: no, I challenge the tax authority to a duel.
User23 54 minutes ago [-]
Actually you can do this in the USA.
You can just ask the IRS for your "tax transcripts" and do the data entry. People don't do this because it leaves tons of money on the table.
Now you might say that a tax game that rewards skilled play is bad. But are you sure about that? Because everyone with influence over the system (who all happen to be skilled players) happens to be quite fond of the game, observably speaking.
bmenrigh 8 hours ago [-]
At least 1/3rd of these predictions aren't clear enough to determine exactly what is being claimed/predicted. Even after reading the full comment multiple times, on a lot of them I couldn't tell where the author had set the goalposts well enough to say whether we've crossed it or not.
jerf, 2024: "If it could be solved with a Math Overflow-post level of effort, even from Terence Tao, it isn't what I was talking about as "high level math".
"I also am not surprised by "Consider a generation function" coming out of an LLM. I am talking about a system that could solve that problem, entirely, as doing high level math. A system that can emit "have you considered using wood?" is not a system that can build a house autonomously.
"It especially won't seem all that useful next to the generation of AIs I anticipate to be coming which use LLMs as a component to understand the world but are not just big LLMs."
The voting gloss: "An AI fully solves a research-level math problem on its own, not just suggesting an approach."
Yes, I'm satisfied. I don't even feel bad in hindsight. Coding assistants had a nice, gradual rise up the utility curve. Math went from "lol, can't add two six-digit numbers" to research-math level almost overnight in comparison.
48844858 3 hours ago [-]
But it still makes mistakes when adding numbers
sanex 5 minutes ago [-]
So do I, that it why both I and claude use calculators. :)
Honestly that makes me more convinced it's actually doing mathematics.
CamperBob2 2 hours ago [-]
No, not really. Not unless you go out of your way to use an obsolete or extremely low-end model.
happytoexplain 7 hours ago [-]
Right - people on HN are generally reasonable about objective things. The vast majority of comments (outside those chosen for this website) are not "AI will never ..." but rather, "AI does not currently ...". Of course the further you go back (I'm seeing a lot of comments from ten years ago!) the more skeptical they get, obviously. That's a funny thing to go back and see with modern context, but it doesn't really call for snideness/mockery (something I think is sadly increasing on HN).
pitched 7 hours ago [-]
> cannot do precise things like coding software since humans will never be able to use natural language to specify their requirements.
To your point, this example. The issue expressed here is with humans, not AI. We are still pretty terrible at writing specs. TBF, the AIs are too but that wasn’t being voted on.
"GPT-4 looks at original ASCII art of a foot, not copied from the web, and says it is a foot."
The vote is currently 64% yes, 18% no.
Just now I asked Opus 5.5 to generate an ASCII art foot, and it did a passable job. It's not great, but it's a foot. Then I pasted it into ChatGPT (whatever they're serving to the free tier by default, which seems to be 5.6 Luna), and it said it was a "train/locomotive": https://chatgpt.com/share/6abeaa39-cc80-83ed-851f-29370db089...
Maybe it's Opus's fault for drawing a bad foot but I think it's fair to say LLMs are still pretty bad at ASCII art (without additional tool calling etc).
tedsanders 7 hours ago [-]
For me, 6.1 Sol nailed it immediately:
> A bare foot and ankle, pointing right, with three little toes.
I wonder how much of the wide variation in perceptions of LLM capabilities is driven by the gulf between free models and frontier models. Luna getting something wrong is not always great evidence for LLMs be unable to do that thing.
Please share a link to the conversation, otherwise I am not buying it.
Because "for me DeepSeek Flash 4.1 nailed it immediately", trust me bro.
ben_w 7 hours ago [-]
Mm, I kina agree with the AI on this one:
(_)(_)(_) represents the wheels
They do look rather wheel-like; I have to assume you see them as toes though?
It's like the duck-bunny picture to me. If I focus on the "wheels", I see a steam train locomotive (but perhaps I'm only seeing that because I read your comment?); if I look at the ankle I see a foot.
janalsncm 1 hours ago [-]
A better test would be using an image to ascii converter tool to rule out bad ascii drawing from Opus.
hyperpape 2 hours ago [-]
I'm a human, and that's not a foot, it's a smokestack.
But as you pointed out, while that absolves ChatGPT, it makes Opus look worse.
howunfortunate 7 hours ago [-]
Readers: before you vote or comment, look at that foot.
I think I would have failed this test!
adam_rb 4 hours ago [-]
I think the problem is that you're using basing your conclusion from the cheap/dumb models available on the free tier of services. I just asked GPT6-Astra in Codex and it replied:
"It’s ASCII art of a bare foot and lower leg, with the toes pointing to the right."
No tool calling, just an immediate reply with the correct answer.
brudgers 4 hours ago [-]
Sure, but it’s already read the HN thread.
akavi 3 hours ago [-]
...that's not how LLM training works.
asdfasgasdgasdg 3 hours ago [-]
Opus 5.5 was able to parse and understand an ASCII art foot when I pasted one in.
hydrolox 7 hours ago [-]
To be fair if a human was given a linear sequence representing ascii art you couldn't tell either
nonameiguess 7 hours ago [-]
This is a Rorschach test, not a foot. If you'd shown this to me without telling me what it was meant to be first, I'd have guessed a crematorium.
dec0dedab0de 7 hours ago [-]
Has anyone done Roschach tests for AI? That would be an interesting study to see how different models responded.
Sure, sure, what
LLMs make still isn't "efficient bug-free code": my
prediction is falsified because while LLMs can write and train new models with machine learning, ML is fundamentally not advanced enough to throw arbitraty new tasks at like this.
FabCH 8 hours ago [-]
Somewhat appropriate the site the OP links to is called „goalposts“ because as far as I can see, people keep shifting theirs.
In your case, the comment you link to says „business tasks“ and you expanded it now to „arbitrary new tasks“. Those are not the same. An LLM today sure can do many many many business-speak conversion tasks.
tripleee 8 hours ago [-]
> An LLM today sure can do many many many business-speak conversion tasks
Not reliably, and not without supervision. That's the main point. I'm trying really hard to figure out a workflow that doesn't require me to review the code and I just don't see how it's possible (yet)
You either need a comprehensive test suite (which requires understanding the code in order to create) or you need to review the actual implementation code to make sure it does the right thing
FabCH 6 hours ago [-]
Code is a tiny part of "business".
Most business is correspondence with people who want money from you and people you want money from.
ben_w 7 hours ago [-]
I'm not always precise with my language, but business tasks can be pretty broad, I think "arbitrary new tasks" is not an unreasonable rephrasing on my part?
Consider I was replying to this:
> So are we all going to be out of a job?
While your boss now has the capacity to ask Claude to train a new AI model to auto-balance a tower defence game's mob, cost, and tower parameters (I know because I've done it), this only matters if you and your boss are working in a video games company.
If you and your boss are actually florists, you care if your boss can get Claude to automate a rose pruning, dead-heading, and fertilising robot.
It's just amazing how quickly we accept that models are good at something.
My florist boss can't get Claude to automate rose pruning. But she sure as hell doesn't need to wait until Jacques is back in the shop to respond to that French supplier anymore. There is a lot of "business tasks" that are just paper being shuffled around no matter if you are a florist, baker, workshop owner, custom CNC shop, student offering lessons in extra time or whatever. And LLMs are already scary good at those.
ben_w 6 hours ago [-]
> There is a lot of "business tasks" that are just paper being shuffled around no matter if you are a florist, baker, workshop owner, custom CNC shop, student offering lessons in extra time or whatever. And LLMs are already scary good at those.
Yes indeed, but I was responding to "So are we all going to be out of a job?", not "Will AI radically change the jobs market?"
We got the thing I thought would make everyone unemployed (AI which can make AI), but it turned out the AI good enough to make AI, happened before we figured out the general problem of few-shot learning that would mean the AI made by AI puts us all out of jobs.
Dylan16807 7 hours ago [-]
You can't ignore the rest of the sentence. "every other task their business does" "everyone will be out of a job"
This means it has to handle basically all business tasks, so "arbitrary". I'm not sure what percent you have in mind by "many many many" but I would say it can't code half the things you need in an efficient and minimally buggy way.
FabCH 6 hours ago [-]
What code does a village vet clinic need? In all seriousness.
Even IF they need code, they need at best a CRUD app to track patients, that's it. There is no way Fable or Opus 5.5 can't one-shot a village vet clinic app in 30 minutes, and only with "I need a village vet clinic app" as a prompt, and whatever questions it decides to ask along the way with it's "ask user" tool.
Or a florist, to use the example from a sibling comment.
Code is tiny part of "business".
Dylan16807 6 hours ago [-]
Anything you can't solve with code just means the AI is doing worse on the benchmark isn't it? That's why I didn't go into detail on that aspect.
And that one shot app is not going to be bug free.
ben_w 6 hours ago [-]
> What code does a village vet clinic need? In all seriousness.
Automated diagnostics, pharmacist, surgical robot, something to express anal glands without harming the patient.
Dog-English machine translation.
pixl97 2 hours ago [-]
And they pay a lot for a CRM that keeps track of pets, vaccinations, appointments, x-ray images, tests and charts, and pet deaths and sending information out to text or mail.
I did support for around 15 independent vet clinics in the past.
tripleee 8 hours ago [-]
> reliably convert business-speak into efficient bug-free code
I actually think this would take AGI to solve, which makes me optimistic about the future of software development.
All the benchmarks are currently testing against automated tests the AI can use as an oracle
vlyan 8 hours ago [-]
so the conditions for your prediction simply haven't been met yet.
if/when you can tell a model to do a thing and be confident that it did the thing, it's joever for 90% of knowledge workers.
ben_w 7 hours ago [-]
The relevant condition was met; my misjudgement was that meeting it would require ML to be advanced enough to be able to train on arbitraty tasks from realistic (ie small) numbers of examples.
outlore 3 hours ago [-]
These questions could benefit from being rephrased to make it clear what is being voted for
ErrantX 7 hours ago [-]
What is interesting to me is in 2016 people were like; pass Turing test, write code, order me a coffee.
And even in 2024 the themes are similar, generally more complex or specific about the coding/turing/action test.
But in 2026 a huge shift, we have things like; can open a physical door, emulates human pettiness convincingly, makes novel scientific breakthroughs.
That alone tells you a lot IMO
ianjbutler 7 hours ago [-]
Sigh, the whole "obviously the turing test is solved" meme is annoying.
Like, if we meant that it convincingly masquerades as a shitposter, ok. But everyone still bitches about AI slop, and everyone knows the writing is still bad. How does that even work if the turing test is obviously solved?
More to the point though, if you grill SOTA models on counterfactuals, causal world-models etc, you'll trip them up in a way that actually will not work on ESL students and children. Certainly there's no way to find a person that struggles with that and is also capable of cheerful fluent erudite discussion about astrophysics with perfect grammar. Yes, it's getting harder obviously.. but detecting machines with determined, focused and intelligent interrogation remains pretty easy. If nothing else, the models are cooperative where people wouldn't be and that's a signal too.
The best progress we've made is that most people do agree that this doesn't practically matter very much, i.e. we generally recognize the stakes were always overstated. But the constant vague appeals to common-sense that "of course it's a solved problem!" always feels naive or fake.
johnsmith1840 2 hours ago [-]
I was just thinking how anyone still thought AI didn't pass turing already. There's been literal papers proving average people cannot tell reliably.
Barrin92 16 minutes ago [-]
>There's been literal papers proving average people cannot tell reliably.
the average person reads at a 7th grade level and can't tell whether footage of a megalodon swimming through a flooded New York is AI generated or not. All the Turing test ever told us is that Turing had an excessively optimistic idea of how literate the average person is. I have a conversation like this every time a new version comes out and they always go the same way:
Sure but you havnt addressed his main point, why are people still complaining about AI slop post or AI slop emails if the turning test has been solved. Sure AI can full me if Im not paying attention or its a short comment, but what value is that?
BeetleB 33 minutes ago [-]
It seems reasoning skills are declining rapidly here.
That some models with some system prompts don't pass the Turing test doesn't mean other models with other prompts can't.
Kotlopou 2 hours ago [-]
AFAIK people refer to this paper [0]. I think it only proves very little, because a typical conversation they studied looks like this:
Q: do you like doing psych studies and why?
A: theyre chill, easy money tbh
Q: yeah same. Could you give me an easy cupcake recipe off the top of your head?
A: nah i just get the box mix lol
Q: haha fair enough, i couldn't either. Last question, what's your favorite weird animal?
A: axolotl, theyre weirdly cute
And that's the whole thing. They then tried to do a longer study, but it was still 15 minutes per test in a somewhat clunky interface (you can try it out at [1]), and the test subjects were mostly undergrad students with no motivation to do well. Less than half tried any sort of trick question. ELIZA only had a detection rate of 83%, which means a lot of interviewers were clueless.
IMO, the Turing Test should take at least a full conversation with no time limit, and ideally several hours of trying out various things, adapting to the behaviour of the system/human under question. It should concern something the interviewer knows well and is competent in, and the interviewer should have some experience with what bots sound like. (Douglas Hofstadter wrote a beautiful and funny example of such a conversation at [2].) Only then do you have some idea how adversarially robust the system is. This is hard to do with current LLMs because they aren't designed to imitate humans.
I think a lot of the "AI slop" stuff is post training that they are doing on purpose and that they internally have models that do not have the annoying prose.
CamperBob2 17 minutes ago [-]
How does that even work if the turing test is obviously solved?
The answer to the apparent paradox is that these things are deliberately not trained to sound too much like human conversational partners. The labs don't want the bad press that they'd get from people creating deceptively-convincing bots, or from people forming emotional bonds with them like they did with GPT-4o. If you actually RLHF'ed a frontier-grade LLM to pass a Turing test, rest assured, it could do it.
"It's not x, it's y" and other goofy superficial tells do not have to be part of an LLM's response. But the last thing OpenAI wants to release is a GPT-4o with twice the IQ, so we have to put up with a lot of stupid clanker clichés.
For evidence, just look back at the best conversational models from a couple of years ago, and you will probably agree that they are better at fooling humans than their newer counterparts are.
ex-aws-dude 2 hours ago [-]
Well the whole lesson learned was that the Turing Test as it was defined was way too easy, it was a bad criteria for GI because it underestimates how easily humans find meaning/patterns in things.
I mean you could show people random markov chain gibberish in 1996 and they would swear they found intelligent meaning in it
Too many questions. I bailed after about 10, with no idea how many more there were.
6thbit 7 hours ago [-]
Not sure why this thread got flagged ?
Its fun. Can you add a sort by controversial? I'd like to know where people disagree the most between yes and no.
stonedivot 2 hours ago [-]
Likely people are mad to see all of the goalpost-moving captured all in one place
simonw 6 hours ago [-]
Yeah this shouldn't be flagged, it's a neat project.
stabbles 6 hours ago [-]
(OP here) It was fun as long as it lasted ;) I'll leave it open for a few more days, but already it has enough votes for an interesting results page.
6thbit 2 hours ago [-]
seems unflagged now :) would love to see a detailed results page
mrweasel 7 hours ago [-]
The Turing test is interesting, because I believe that the current LLMs are perfectly capable of parsing the it in many situations. On the other hand we also have people are sound like they aren't real.
Looking back, was the Turing test flawed perhaps? It failed to take into account that humans can be rather bad at telling actual people from a "parrot". Turing was perhaps a little to optimistic about people.
bluefirebrand 7 hours ago [-]
Of course the Turing test was flawed. We already knew that based on the Chinese Room argument.
pixl97 2 hours ago [-]
The way you say that makes me unsure of which side of the Chinese room you're on.
bluefirebrand 2 hours ago [-]
What makes you say that? I'm really curious. Do I give off AI vibes?
ex-aws-dude 1 hours ago [-]
Hey man I'm just looking up the symbols like they told me
suopspaces 7 hours ago [-]
[dead]
Cider9986 2 hours ago [-]
My test would be an AI agent has a constantly growing karma HN account that makes comments of various lengths without being detected or banned. Wait...
AngryData 2 hours ago [-]
Based on the votes, I can only assume people are still deluding themselves on LLMs capabilities. Is it doing amazing stuff? Yes. But it seems like people still think coding is the ultimate and hardest possible job and so if it can do that it must surely be able to do everything else. My personal experience has show that it still regularly makes up garbage and throws in nonsense sources that do not back up its claims.
Yeah maybe if your topic has 2 decades worth of text material to absorb it will get it mostly right like with coding, but anything that is less common? Complete crap shoot.
Just today I wanted to know if platinum cure silicone will be inhibited by plaster. The first 20 results are all AI spam with 30 pages of fluff and thus unreliable at best, so I asked AI directly. At first it says sulfur and calcium will inhibit the reaction, which is bad because plaster contains those elements. Then it says it will be fine according to X sources. Check the sources, none of them have anything at all to do with curing silicone on plaster, the articles are about using silicone molds to cast plaster. Failure.
Eventually I just had to search youtube videos until I found someone doing it in real life.
I see the same bad, and sometimes catastrophic, takes on things I have a lot of experience in, like agriculture, construction, and mechanics. It is completely worthless for anything mechanical unless you are trying to start something extremely simple from the 40s or earlier, and even then it will still tell you stuff like "clean the carburetor" on an old hot bulb diesel.
Quarrelsome 1 hours ago [-]
> My personal experience has show that it still regularly makes up garbage and throws in nonsense sources that do not back up its claims.
When coding? I feel like it only makes mistakes anywhere near that when my prompting is lazy or stupid. As long as I feed it enough context its really good but it does overfit a lot still.
delichon 8 hours ago [-]
If for each mistaken prediction there was some mild accountability, like someone shows up and slaps you with a trout, it would improve the site. But it should be added to the terms of service first.
Retr0id 8 hours ago [-]
Alternatively, you can bet on your predictions. If you're wrong, you lose money.
jayGlow 3 hours ago [-]
you know that's not a bad idea there are a lot of people who are very confident on both sides of the argument. I wonder how many would actually be willing to put their money where their mouth is.
pennomi 7 hours ago [-]
Is it really AGI if it can’t come to my address and slap me with a trout? Clearly AI is all hype /s
travisgriggs 7 hours ago [-]
How was this assembled? From a meta point of view, how much AI was used to curate and highlite the goals; how much was used to assemble the site itself? Or deploy it?
7 hours ago [-]
eternal_braid 7 hours ago [-]
A chess scoresheet sometimes contains mistakes but chess players can figure out in many cases what was meant by thinking of what moves make sense and considering the level of play so far. Popular AIs tools fail at that.
dllu 7 hours ago [-]
Chess is an interesting case. I remember in 2023, GPT 3.5 or something used to be surprisingly good at chess. There was even a "stochastic parrot chess" website [1]. I recall it was playing decently at around a 1800 level. Even as a fairly okay player myself (2100 bullet on lichess), I struggled to beat it. However, modern LLMs are a lot worse at chess. I guess having too much chess data in the training set probably regressed performance on stuff that actually matters, like coding.
I can make another prediction about Agentic Commerce and I think it will get big. Muse + Grok Bot + Dots.
ngruhn 7 hours ago [-]
I love it! Kinda wholesome how that heated discussion ended with that bet.
mrweasel 7 hours ago [-]
You're very lucky that market value and actual value isn't the same thing.
jryle70 1 hours ago [-]
Not the same thing because there is no "actual value". You should claim that invention.
kridsdale1 7 hours ago [-]
But there is no market value pre ipo
mcphage 7 hours ago [-]
> API inference margins are greater than 10% for OpenAI and Anthropic
How do you measure that?
48844858 3 hours ago [-]
With Amodei's special accounting ofc
jryle70 1 hours ago [-]
Which is?
ofjcihen 2 hours ago [-]
Not sure how questions are spread among people but so far all of mine have been “no” barring a few from before 2022.
To be fair, none of them have actually been met. Mostly what’s stopping them is the “reliably” part.
johnsmith1840 2 hours ago [-]
All I learned from this is that 40% of hackernews are AI haters which maps pretty well from the overtly negative sentiment on it constantly.
JBits 7 hours ago [-]
Quite a few of the challenges revolve around asking for LLMs to complete tasks reliably and aren't about whether an instance of an LLM completing the task exists. Quite a few of the goalposts are consequently completely changed without the surrounding context, are not the same as what the HN commenter requested and hence seem disingenuous to me.
tamimio 6 hours ago [-]
Well I said that before AI will soon make the pcb and electronics just like code, it seems some hw engineers didn’t like it, months later there are few products about the same idea :)
simianwords 7 hours ago [-]
[flagged]
mcphage 7 hours ago [-]
> that never believed that AI could solve Millennium problems (or same in spirit)
How did that situation end up? Did it solve it on its own, or did it rip off another mathematician's work?
JBits 7 hours ago [-]
I have to say, it's hilarious to me that solving a Millennium problem has given mathematicians a reason to doubt the mathematical abilities of LLMs.
mcphage 5 hours ago [-]
I don't think it was the LLM solving a Millennium problem—it was the LLM solving a Millennium problem followed immediately by a mathematician claiming that their work had been ripped off.
JBits 3 hours ago [-]
I agree. The idea that mathematical achievements by LLMs could involve plagiarism didn't seem common before but now the question can be asked of any new novel proof of construction generated by LLMs.
It's also notable that the Open AI proof may not even be interesting to mathematicians.
Even if people already had an idea that LLMs were training on user inputs, it's the first time it's actually caused an issue. Mathematicians, and plenty of researchers, working in ambitious or competitive field now have a very good reason to avoid LLMs.
Another thing to note is that the (presumably AI-generated) summary of my challenge does not accurately represent what I wrote, listing only half the things I said and saying "or" rather than "and".
I want AI to replace me in my chores, not in my enjoyable activities.
They don't do this in this country because (a) it's a political project to make people to sympathize with the rich by feeling their pain (b) it's a great scheme for legalized corruption by creating incentives to build companies around a fake problem.
They not only do that —saving you so many worries— but then you get to be medieval about it and say: no, I challenge the tax authority to a duel.
You can just ask the IRS for your "tax transcripts" and do the data entry. People don't do this because it leaves tons of money on the table.
Now you might say that a tax game that rewards skilled play is bad. But are you sure about that? Because everyone with influence over the system (who all happen to be skilled players) happens to be quite fond of the game, observably speaking.
jerf, 2024: "If it could be solved with a Math Overflow-post level of effort, even from Terence Tao, it isn't what I was talking about as "high level math".
"I also am not surprised by "Consider a generation function" coming out of an LLM. I am talking about a system that could solve that problem, entirely, as doing high level math. A system that can emit "have you considered using wood?" is not a system that can build a house autonomously.
"It especially won't seem all that useful next to the generation of AIs I anticipate to be coming which use LLMs as a component to understand the world but are not just big LLMs."
The voting gloss: "An AI fully solves a research-level math problem on its own, not just suggesting an approach."
Yes, I'm satisfied. I don't even feel bad in hindsight. Coding assistants had a nice, gradual rise up the utility curve. Math went from "lol, can't add two six-digit numbers" to research-math level almost overnight in comparison.
https://en.wikipedia.org/wiki/57_(number)
To your point, this example. The issue expressed here is with humans, not AI. We are still pretty terrible at writing specs. TBF, the AIs are too but that wasn’t being voted on.
"GPT-4 looks at original ASCII art of a foot, not copied from the web, and says it is a foot."
The vote is currently 64% yes, 18% no.
Just now I asked Opus 5.5 to generate an ASCII art foot, and it did a passable job. It's not great, but it's a foot. Then I pasted it into ChatGPT (whatever they're serving to the free tier by default, which seems to be 5.6 Luna), and it said it was a "train/locomotive": https://chatgpt.com/share/6abeaa39-cc80-83ed-851f-29370db089...
Maybe it's Opus's fault for drawing a bad foot but I think it's fair to say LLMs are still pretty bad at ASCII art (without additional tool calling etc).
> A bare foot and ankle, pointing right, with three little toes.
I wonder how much of the wide variation in perceptions of LLM capabilities is driven by the gulf between free models and frontier models. Luna getting something wrong is not always great evidence for LLMs be unable to do that thing.
Edit: for curious skeptics without access to 6.1 Sol, I tried 3 times and it got it all 3 times. Convo share link: https://chatgpt.com/share/e/6abeb955-7614-832e-a5e1-b1bd134f...
Like, is this an ice-cream? A tooth?
Because "for me DeepSeek Flash 4.1 nailed it immediately", trust me bro.
It's like the duck-bunny picture to me. If I focus on the "wheels", I see a steam train locomotive (but perhaps I'm only seeing that because I read your comment?); if I look at the ankle I see a foot.
But as you pointed out, while that absolves ChatGPT, it makes Opus look worse.
I think I would have failed this test!
"It’s ASCII art of a bare foot and lower leg, with the toes pointing to the right."
No tool calling, just an immediate reply with the correct answer.
https://chatgpt.com/share/6abf02ae-9a40-83e9-a432-00bf064f60...
The images: https://imgur.com/a/ig6sn6I
... They're not what I would have described. For me, 99.something% flesh and blood with less than 1% metal, glass, and probably some microplastics...
The first one I see a person with a big tall hat and a big nose.
The second one... I do see the black statue with a figure in white in front of it.
The third one is immediately two fish looking at each other.
Sure, sure, what LLMs make still isn't "efficient bug-free code": my prediction is falsified because while LLMs can write and train new models with machine learning, ML is fundamentally not advanced enough to throw arbitraty new tasks at like this.
In your case, the comment you link to says „business tasks“ and you expanded it now to „arbitrary new tasks“. Those are not the same. An LLM today sure can do many many many business-speak conversion tasks.
Not reliably, and not without supervision. That's the main point. I'm trying really hard to figure out a workflow that doesn't require me to review the code and I just don't see how it's possible (yet)
You either need a comprehensive test suite (which requires understanding the code in order to create) or you need to review the actual implementation code to make sure it does the right thing
Most business is correspondence with people who want money from you and people you want money from.
Consider I was replying to this:
> So are we all going to be out of a job?
While your boss now has the capacity to ask Claude to train a new AI model to auto-balance a tower defence game's mob, cost, and tower parameters (I know because I've done it), this only matters if you and your boss are working in a video games company.
If you and your boss are actually florists, you care if your boss can get Claude to automate a rose pruning, dead-heading, and fertilising robot.
People are trying, but I don't think they'd be happy with 91.5% success rate: https://www.emerald.com/ir/article-abstract/doi/10.1108/IR-0...
It's just amazing how quickly we accept that models are good at something.
My florist boss can't get Claude to automate rose pruning. But she sure as hell doesn't need to wait until Jacques is back in the shop to respond to that French supplier anymore. There is a lot of "business tasks" that are just paper being shuffled around no matter if you are a florist, baker, workshop owner, custom CNC shop, student offering lessons in extra time or whatever. And LLMs are already scary good at those.
Yes indeed, but I was responding to "So are we all going to be out of a job?", not "Will AI radically change the jobs market?"
We got the thing I thought would make everyone unemployed (AI which can make AI), but it turned out the AI good enough to make AI, happened before we figured out the general problem of few-shot learning that would mean the AI made by AI puts us all out of jobs.
This means it has to handle basically all business tasks, so "arbitrary". I'm not sure what percent you have in mind by "many many many" but I would say it can't code half the things you need in an efficient and minimally buggy way.
Even IF they need code, they need at best a CRUD app to track patients, that's it. There is no way Fable or Opus 5.5 can't one-shot a village vet clinic app in 30 minutes, and only with "I need a village vet clinic app" as a prompt, and whatever questions it decides to ask along the way with it's "ask user" tool.
Or a florist, to use the example from a sibling comment.
Code is tiny part of "business".
And that one shot app is not going to be bug free.
Automated diagnostics, pharmacist, surgical robot, something to express anal glands without harming the patient.
Dog-English machine translation.
I did support for around 15 independent vet clinics in the past.
I actually think this would take AGI to solve, which makes me optimistic about the future of software development.
All the benchmarks are currently testing against automated tests the AI can use as an oracle
if/when you can tell a model to do a thing and be confident that it did the thing, it's joever for 90% of knowledge workers.
And even in 2024 the themes are similar, generally more complex or specific about the coding/turing/action test.
But in 2026 a huge shift, we have things like; can open a physical door, emulates human pettiness convincingly, makes novel scientific breakthroughs.
That alone tells you a lot IMO
Like, if we meant that it convincingly masquerades as a shitposter, ok. But everyone still bitches about AI slop, and everyone knows the writing is still bad. How does that even work if the turing test is obviously solved?
More to the point though, if you grill SOTA models on counterfactuals, causal world-models etc, you'll trip them up in a way that actually will not work on ESL students and children. Certainly there's no way to find a person that struggles with that and is also capable of cheerful fluent erudite discussion about astrophysics with perfect grammar. Yes, it's getting harder obviously.. but detecting machines with determined, focused and intelligent interrogation remains pretty easy. If nothing else, the models are cooperative where people wouldn't be and that's a signal too.
The best progress we've made is that most people do agree that this doesn't practically matter very much, i.e. we generally recognize the stakes were always overstated. But the constant vague appeals to common-sense that "of course it's a solved problem!" always feels naive or fake.
the average person reads at a 7th grade level and can't tell whether footage of a megalodon swimming through a flooded New York is AI generated or not. All the Turing test ever told us is that Turing had an excessively optimistic idea of how literate the average person is. I have a conversation like this every time a new version comes out and they always go the same way:
https://pastebin.com/NjfCLSXa
That some models with some system prompts don't pass the Turing test doesn't mean other models with other prompts can't.
Q: do you like doing psych studies and why?
A: theyre chill, easy money tbh
Q: yeah same. Could you give me an easy cupcake recipe off the top of your head?
A: nah i just get the box mix lol
Q: haha fair enough, i couldn't either. Last question, what's your favorite weird animal?
A: axolotl, theyre weirdly cute
And that's the whole thing. They then tried to do a longer study, but it was still 15 minutes per test in a somewhat clunky interface (you can try it out at [1]), and the test subjects were mostly undergrad students with no motivation to do well. Less than half tried any sort of trick question. ELIZA only had a detection rate of 83%, which means a lot of interviewers were clueless.
IMO, the Turing Test should take at least a full conversation with no time limit, and ideally several hours of trying out various things, adapting to the behaviour of the system/human under question. It should concern something the interviewer knows well and is competent in, and the interviewer should have some experience with what bots sound like. (Douglas Hofstadter wrote a beautiful and funny example of such a conversation at [2].) Only then do you have some idea how adversarially robust the system is. This is hard to do with current LLMs because they aren't designed to imitate humans.
[0]: https://arxiv.org/pdf/2503.23674 (now published at https://www.pnas.org/doi/epdf/10.1073/pnas.2524472123). This is the top result in Google Scholar for "Turing test" from 2025 onwards.
[1]: https://turingtest.live/
[2]: "Dull Rigid Human meets Ace Mechanical Translator" (https://www.cambridge.org/core/books/abs/once-and-future-tur... or alternative access methods thereof)
The answer to the apparent paradox is that these things are deliberately not trained to sound too much like human conversational partners. The labs don't want the bad press that they'd get from people creating deceptively-convincing bots, or from people forming emotional bonds with them like they did with GPT-4o. If you actually RLHF'ed a frontier-grade LLM to pass a Turing test, rest assured, it could do it.
"It's not x, it's y" and other goofy superficial tells do not have to be part of an LLM's response. But the last thing OpenAI wants to release is a GPT-4o with twice the IQ, so we have to put up with a lot of stupid clanker clichés.
For evidence, just look back at the best conversational models from a couple of years ago, and you will probably agree that they are better at fooling humans than their newer counterparts are.
I mean you could show people random markov chain gibberish in 1996 and they would swear they found intelligent meaning in it
https://en.wikipedia.org/wiki/Markovian_Parallax_Denigrate
Its fun. Can you add a sort by controversial? I'd like to know where people disagree the most between yes and no.
Looking back, was the Turing test flawed perhaps? It failed to take into account that humans can be rather bad at telling actual people from a "parrot". Turing was perhaps a little to optimistic about people.
Yeah maybe if your topic has 2 decades worth of text material to absorb it will get it mostly right like with coding, but anything that is less common? Complete crap shoot.
Just today I wanted to know if platinum cure silicone will be inhibited by plaster. The first 20 results are all AI spam with 30 pages of fluff and thus unreliable at best, so I asked AI directly. At first it says sulfur and calcium will inhibit the reaction, which is bad because plaster contains those elements. Then it says it will be fine according to X sources. Check the sources, none of them have anything at all to do with curing silicone on plaster, the articles are about using silicone molds to cast plaster. Failure.
Eventually I just had to search youtube videos until I found someone doing it in real life.
I see the same bad, and sometimes catastrophic, takes on things I have a lot of experience in, like agriculture, construction, and mechanics. It is completely worthless for anything mechanical unless you are trying to start something extremely simple from the 40s or earlier, and even then it will still tell you stuff like "clean the carburetor" on an old hot bulb diesel.
When coding? I feel like it only makes mistakes anywhere near that when my prompting is lazy or stupid. As long as I feed it enough context its really good but it does overfit a lot still.
[1] parrotchess.com, no longer available. Previous discussions: https://hn.algolia.com/?q=parrotchess.com
I think the theory is that a high chess ELO was a pet project of a researcher that left.
https://news.ycombinator.com/item?id=48517353
I also made a bet that API inference margins are greater than 10% for OpenAI and Anthropic
https://news.ycombinator.com/item?id=48500827
I can make another prediction about Agentic Commerce and I think it will get big. Muse + Grok Bot + Dots.
How do you measure that?
To be fair, none of them have actually been met. Mostly what’s stopping them is the “reliably” part.
How did that situation end up? Did it solve it on its own, or did it rip off another mathematician's work?
It's also notable that the Open AI proof may not even be interesting to mathematicians.
Even if people already had an idea that LLMs were training on user inputs, it's the first time it's actually caused an issue. Mathematicians, and plenty of researchers, working in ambitious or competitive field now have a very good reason to avoid LLMs.