[HN Gopher] OpenAI researcher announced GPT-5 math breakthrough ...
___________________________________________________________________
OpenAI researcher announced GPT-5 math breakthrough that never
happened
Author : Topfi
Score : 350 points
Date : 2025-10-19 11:30 UTC (11 hours ago)
(HTM) web link (the-decoder.com)
(TXT) w3m dump (the-decoder.com)
| amelius wrote:
| > Summary (from the article)
|
| * OpenAI researchers claimed or suggested that GPT-5 had solved
| unsolved math problems, but in reality, the model only found
| known results that were unfamiliar to the operator of
| erdosproblems.com.
|
| * Mathematician Thomas Bloom and Deepmind CEO Demis Hassabis
| criticized the announcement as misleading, leading the
| researchers to retract or amend their original claims.
|
| * According to mathematician Terence Tao, AI models like GPT-5
| are currently most helpful for speeding up basic research tasks
| such as literature review, rather than independently solving
| complex mathematical problems.
| HarHarVeryFunny wrote:
| > GPT-5 had only surfaced existing research that Bloom had
| missed
|
| So GPT-5 didn't derive anything itself - it was just an
| effective search engine for prior research, which is useful,
| but not any sort of breakthough whatsoever.
| jgalt212 wrote:
| After the circular financing schemes involving hundreds of
| billions of dollars were uncovered, nothing I read about the AI
| business and its artificial hype machine surprises me anymore.
| bbor wrote:
| This is just tit-for-tat clickbait. The researcher's wording was
| a bit unclear for sure, but far from incorrect.
| resoluteteeth wrote:
| I disagree. There is no way to interpret "GPT-5 just found
| solutions to 10 (!) previously unsolved Erdos problems" as
| saying something other than GPT-5 having solved them.
|
| If it just found existing solutions then they obviously weren't
| "previously unsolved" so the tweet is wrong.
|
| He clearly misunderstood the situation and jumped to the
| conclusion that GPT-5 had actually solved the problems because
| that's what he wanted to believe.
|
| That said, the misunderstanding is understandable because the
| tweet he was responding to said they had been listed as "open",
| but solving unsolved erdos problems by itself would be such a
| big deal that he probably should have double checked it.
| andrewstuart wrote:
| Humans hallucinating about AI.
| MattGaiser wrote:
| Humans "hallucinate" in the AI way constantly, which is why I
| don't see them as a barrier to LLMs replacing humans in many
| contexts. It really isn't unusual for a human to make stuff up
| or be unaware of stuff.
| pas wrote:
| it's the same thing with self-driving, if you can make it
| safer than a good human driver it's enough. but the bar is
| pretty low with driving (as evidenced by the hundreds of
| thousands of collisions and deaths and permanent disabilities
| each year). and rather high in scientific publishing.
| zeknife wrote:
| A human being informed of a mistake will usually be able to
| resolve it and learn something in the process, whereas an LLM
| is more likely to spiral into nonsense
| alimw wrote:
| You must know better humans than I do.
| MattGaiser wrote:
| You must know people without egos. Humans are better at
| correcting their mistakes, but far worse at admitting them.
|
| But yes, as an edge case handler humans still have an edge.
| topaz0 wrote:
| LLMs by contrast love to admit their mistakes and self-
| flagellate, and then go on to _not_ correct them. Seems
| like a worse tradeoff.
| thaumasiotes wrote:
| It's true that the big public-facing chatbots love to
| admit to mistakes.
|
| It's not obvious to me that they're better at admitting
| their mistakes. Part of being good at admitting mistakes
| is recognizing when you haven't made one. That humans
| tend to lean too far in that direction shouldn't suggest
| that the right amount of that behavior is... less than
| zero.
| skeeter2020 wrote:
| Not when your goal is to create ASI: Artificial Sycophant
| Intelligence
| tonyhart7 wrote:
| and this is why LLM is getting cooked
|
| they feed an internet data into that shit, they basically
| "told" LLM to behave because surprise surprise, human
| sometimes can be more nasty
| zeroonetwothree wrote:
| Humans are a bit better at knowing which things are important
| and doing more research. Also better at being honest when
| directly pressed. And infinitely better at learning from
| errors.
|
| (Yes, not everyone, but we do have some mechanisms to judge
| or encourage)
| lapcat wrote:
| > Humans "hallucinate" in the AI way constantly
|
| This claim is ambiguous. The use of the word "Humans" here
| obscures rather than clarifies the issue. Individual humans
| typically do not "hallucinate" constantly, especially not on
| the job. Any individual human who is as bad at their job as
| an LLM should indeed be replaced, by a more competent
| individual human, not by an equally incompetent LLM. This was
| true long before LLMs were invented.
|
| In the movie "Bill and Ted's Excellent Adventure," the
| titular characters attempt to write a history report by
| asking questions of random strangers in a convenience store
| parking lot. This of course is ridiculous and more a
| reflection of the extreme laziness of Bill and Ted than
| anything else. Today, the lazy Bill and Ted would ask ChatGPT
| instead. It's equally ridiculous to defend the wild
| inaccuracy and hallucinations of LLMs by comparing them to
| average humans. It's not the job of humans to answer random
| questions on any subject.
|
| Human subject matter experts are not perfect, but they're
| much better than average and don't hallucinate on their
| subjects. They also have accountability and paper trails, can
| be individually discounted for gross misconduct, unlike LLMs.
| skeeter2020 wrote:
| Do you think the OpenAI human, when informed of their
| "oopsie" replied "You're right, there is existing evidence
| that this problem has already been solved. Blah Blah Blah ...
| and that's why our new model has made a huge breakthrough
| against previously unsolved math problems!"
| topaz0 wrote:
| > Humans "hallucinate" in the AI way constantly
|
| This is more and more clearly false. Humans get things wrong
| certainly, but the manner in which they get things wrong is
| just not comparable to how the LLMs get things wrong, beyond
| the most superficial comparison.
| random9749832 wrote:
| Best case: Hallucination
|
| Worst case (more probable): Lying
| MPSimmons wrote:
| Hanlon's Razor
| forgetfulness wrote:
| Lying is a stupid way of selling something and making money
| reaperducer wrote:
| _Lying is a stupid way of selling something and making
| money_
|
| Works for Elon.
| random9749832 wrote:
| They are expanding into the adult market because they are
| running out of ideas. I think common sense is enough to
| decide what is what here.
| JKCalhoun wrote:
| "OpenAI Researcher Hallucinates GPT-5 Math Breakthrough" could
| be a headline from The Onion.
| antegamisou wrote:
| Seriously those headlines are getting DailyMail
| sensationalism levels of ridiculous.
| nicce wrote:
| I the old world we would just use the word bullshit.
| reaperducer wrote:
| _" OpenAI Researcher Hallucinates GPT-5 Math Breakthrough"
| could be a headline from The Onion._
|
| Off topic, but I saw The Onion on sale in the magazine rack
| of Barnes and Noble last month.
|
| For those who miss when it was a free rag in sidewalk
| newsstands, and don't want to pony up for a full
| subscription, this is an option.
| pera wrote:
| Heh stockholders are not hallucinating: They know very well
| what they are doing.
| skeeter2020 wrote:
| retail investors? no way. The fever-dream may continue for a
| while but eventually it will end. Meanwhile we don't even
| know our full exposure to AI. It's going to be ugly and
| beyond burying gold in my backyard I can't even figure out
| how to hedge against this monster.
| rixed wrote:
| These days AI just obsequiously praise whatever stupid ideas
| the human throw at them, which encourage humans into
| hallucinating breakthroughs.
|
| But it's only a matter of time before AI gets better at prompt
| engineering.
|
| /s?
| Palmik wrote:
| More like humans hallucinating about humans hallucinating about
| AI, see here: https://news.ycombinator.com/item?id=45634120
| alkyon wrote:
| They started believing the very lies they invented.
| moffkalast wrote:
| "The truth is usually just an excuse for a lack of
| imagination."
| Kiboneu wrote:
| No no, openai is actually secretly run by AI.
| mentalgear wrote:
| Another instance of openAI manipulating results to prolong their
| unsustainable circular hype bubble.
|
| The inevitable collapse could be even more devastating than the
| 2008 financial crisis.
|
| All while so vast resources are being wasted on non-verifiable
| gen AI slob, while real approaches (neuro-symbolic like
| DeepMind's AlphaFold) are mostly ignored financially because they
| don't generate the quick stock market increases that hype does.
| the_duke wrote:
| People keep spouting this, but I don't see how the AI bubble
| bursting would be all that devastating.
|
| 2008 was a systemic breakdown rippling through the foundations
| of the financial system.
|
| It would lead to a market crash (80% of gains this year were
| big tech/AI) and likely a full recession in the US, but nothing
| nearly as dramatic as a global systemic crisis.
|
| In contrast to the dot com bubble, the huge AI spending is also
| concentrated on relatively few companies, many with deep
| pockets from other revenue sources (Google, Meta, Microsoft,
| Oracle), and the others are mostly private companies that won't
| have massive impact on the stock market.
|
| A sudden stop in AI craze would be hard for hardware companies
| and a few big AI only startups , but the financial fallout
| would be much more contained than either dot com or 2008.
| Topfi wrote:
| Isn't the dot com bubble a far better proxy? Notably, todays
| spending is both higher and more concentrated in a few
| companies that a large part of the population has exposure to
| (most dot com companies weren't publicly traded and far
| smaller vs MSFT, Alphabet, Meta, Oracle, NVDA making up most
| investment today) by way of pension funds, ETFs, etc.?
| the_duke wrote:
| Sure, but all of the above have solid businesses that rake
| in lots of money, revenue based on AI is a small percentage
| for them.
|
| An AI bust would take the stock price down a good deal, but
| the stock gains have been relatively moderate. Year on
| year: Microsoft +14%, Meta +24%, Google +40, Oracle +60%,
| ... And a notable chunk of those gains have indirectly come
| from the dollar devaluing.
|
| Nvidia would be hit much harder of course.
|
| There is a good amount of smaller AI startups, but a lot of
| the AI development is concentrated on the big dogs, it's
| not nearly as systemic as in dot com, where a lot of
| businesses went under completely.
|
| And even with an AI freeze, there is plenty of value and
| usage there already that will not go away, but will keep
| expanding (AI chat, AI coding, etc) which will mitigate
| things.
| rhetocj23 wrote:
| The problem is do you know much 14% etc is? We are
| talking about valuations in the trillions my friend!
| MattGaiser wrote:
| > People keep spouting this, but I don't see how the AI
| bubble bursting would be all that devastating.
|
| Well, an enormous amount of debt is being raised and issued
| for AI and US economic growth is nearly entirely AI. Crypto
| bros showed the other day that they were leveraged to the
| hilt on coins and it wouldn't surprise me if people are the
| same way on AI. It is pretty heavily tied to the financial
| system at this point.
| techblueberry wrote:
| I think my theory into contagion would be that There's been
| lots of talk about these companies starting to rack up debt,
| and I think AI is so tied into the US GDP that things like -
|
| If the stock market crashes, there's lots of talk about how
| wealth and debt are interlinked. Could the crash be general
| enough to start calls on debt backed by stocks.
|
| My recollection in 2008 was that we didn't learn about how
| bad it was until after. The tech companies have been so
| desperate for a win, I wonder if some of them are over their
| skis in some way, and if there are banks that are risking it
| all on AI. (We know for some tech bros think the bet on AI is
| a longtermist like bet; closer to religion than reason and
| that it's worth risking everything because the payback could
| be in the hundreds of trillions)
|
| Combine this with the fact that AI is like what - 30% of the
| US economy? Magnificent 7 are 60%?
|
| What happens if sustainable PE ratios in tech collapse. Does
| it take out Tesla?
|
| Maybe the contagion is just the impact on the US economy
| which, classically anyways has been intermingled with
| everything.
|
| I would bet almost everything that there is some lie at the
| center of this thing that we aren't really aware of yet.
| the_duke wrote:
| It may well be that an AI bubble burst is the tipping
| point, but I think that tipping point was coming either
| way.
|
| The US admin has been (almost desperately) trying to prop
| up markets and an already struggling economy. If it wasn't
| AI, it could have been another industry.
|
| I think AI is more of a sideshow in this context. The
| bigger story is the dollar losing its dominant position ,
| money draining out into Gold/Silver/other stock markets,
| India buying oil from Russia in Yen, a global economy that
| has for years been propped up by government spending
| (US/China/Europe/...), large and lasting geopolitical power
| balance shifts, ...
|
| These things don't happen overnight, and in fact over many
| years for USD holdings, but the effects will materialize.
|
| Some of the above (dollar devaluation) is actually what the
| current admin wanted, which I would see as an admission of
| global shifts. We might see much larger changes to the
| whole financial system in the coming decades, which will
| have a lot of effects.
| SJC_Hacker wrote:
| > Combine this with the fact that AI is like what - 30% of
| the US economy? Magnificent 7 are 60%?
|
| Nowhere close. US GDP is like $30 trillion. Open AI revenue
| is ~$4 billion. All the other AI companies revenue might
| amount to $10 billion at most, and that is being generous.
| $10 billion/ $30 trillaion is not even 1%.
|
| You are forgetting all those "boring" sectors that form the
| basis of economies like agriculture and energy. They have
| always been bigger than the tech sector at any point, but
| they aren't "sexy" because there isn't the potential
| "exponential growth" that tech companies
| ben_w wrote:
| Small quibble, doesn't challenge your overall point, but
| as I understand it their revenue is somewhat higher than
| you say.
|
| The Open AI revenue was ~$4 billion for the first half of
| the year; Anthropic recently reported a rate (which isn't
| total revenue, I know) equivalent to about $10
| billion/year; NVIDIA's sales are supposed to be up 78%
| this quarter due to AI sales, reaching $39.33 billion, so
| plausibly ($39.33/1.78)*0.78 ~= $17 billion from AI in
| that quarter (rate, again yes I know, of $68
| billion/year). So I can believe AI is order-of $100
| billion/year economically... to US businesses with
| customers almost everywhere important except possibly
| China.
|
| But just to re-iterate, this doesn't change your point.
| Even 100 B / 30 T is only one third of a percent.
| Theodores wrote:
| When America sneezes, the rest of the world catches a cold.
| This was said after the OG 1929 crash and I can remember it
| said in the 80s. Nobody says it anymore.
|
| Due to exorbitant privilege, with the dollar as the only
| currency that matters, every country that trades with America
| is swapping goods and services for 'bits of green paper'.
| Unless buying oil from Russia, these bits of green paper are
| needed to buy oil. National currencies and the Euro might as
| well be casino chips, mere proxies for dollars.
|
| Just last week the IMF issued a warning regarding AI stocks
| and the risk they pose to the global economy if promises are
| not delivered.
|
| With every hiccup, whether that be the dot com boom, 2008 or
| the pandemic, the way out is to print more money, with this
| money going in at the top, for the banks, not the masses.
| This amounts to devaluation.
|
| When the Ukraine crisis started, the Russian President
| stopped politely going along with Western capitalism and
| called the West out for printing too much money during the
| pandemic. Cut off from SWIFT and with many sanctions, Russia
| started trading in other currencies with BRICS partners. We
| are now at a stage of the game where the BRICS countries, of
| which there are many, already have a backup plan for when the
| next US financial catastrophe happens. They just won't use
| the dollar anymore. Note that currently, China doesn't want
| any dollars making it back to its own economy, since that
| would cause inflation. So they invest their dollars in Belt
| and Road initiatives, keeping those green bits of paper
| safely away from China. They don't even need exports to the
| USA or Europe since they have a vast home market to develop.
|
| Note that Russia's reserve of dollars and euros was
| confiscated. They have nothing to lose so they aren't going
| to come back into the Western financial system.
|
| Hence, you are right. A market crash won't be a global
| systematic crisis, it just means that Shanghai becomes the
| financial capital of the world, with no money printing unless
| it is backed up by mineral, energy or other resources that
| have tangible value. This won't be great for the collective
| West, but pretty good for the rest of the world.
| the_duke wrote:
| I have similar views on many points, see my response to a
| sibling comment.
|
| I just think that effects of the AI bubble bursting would
| be at most a symptom or trigger of much larger geopolitical
| and financial shifts that would happen anyway.
| jcranmer wrote:
| There's a few variables which can make it much worse.
|
| The first is how much of the capital expenditures are being
| fueled by debt that won't be repaid, and how much that unpaid
| debt harms lending institutions. This is fundamentally how a
| few bad debts in 2008 broke the entire financial system: bad
| loans felled Lehman Brothers, which caused _one_ money market
| fund to break the buck, which spurred a massive exodus from
| the money markets rather literally overnight.
|
| The second issue is the psychological impact of 40% of market
| value just evaporating. A lot of people have indirect
| exposure to the stock market and these stocks in particular
| (via 401(k)s or pensions), and seeing that much of their
| wealth evaporate will definitely have some repercussions on
| consumer confidence.
| kif wrote:
| This honestly doesn't surprise me. We have reached a point where
| it's becoming clearer and clearer that AGI is nowhere to be seen,
| whereas advances in LLM ability to 'reason' have slowed down to
| (almost?) a halt.
| dawnerd wrote:
| But if you ask an AI hype person they'll say we're almost there
| we just need a bit more gigawatts of compute!
| rhetocj23 wrote:
| I hate to say this but I think the LLM story is going to go
| the same way as Teslas stock - everyone knows its completely
| detached from fundamentals and driven by momentum and hype
| but nobody wants to do the right thing.
| vbezhenar wrote:
| In my book, chat-based AGI has been reached years ago, when I
| couldn't reliably distinguish computer from human.
|
| Solving problems that humanity couldn't solve is super-AGI or
| something like that. It's not there indeed.
| 3836293648 wrote:
| Beating the Turing Test is not AGI, but it _is_ beating the
| Turing Test and that was impressive enough when it happened
| jdiff wrote:
| We're not even solving problems that humanity can solve.
| There's been several times where I've posed to models a
| geometry problem that was novel but possible for me to solve
| on my own, but LLMs have fallen flat on executing them every
| time. I'm no mathematician, these are not complex problems,
| but they're well beyond any AI, even when guided. Instead,
| they're left to me, my trusty whiteboard, and a non-
| negligible amount of manual brute force shuffling of terms
| until it comes out right.
|
| They're good at the Turing test. But that only marks them as
| indistinguishable from humans in casual conversation. They
| are fantastic at that. And a few other things, to be clear.
| Quick comprehension of an entire codebase for fast queries is
| horribly useful. But they are a long way from human-level
| general intelligence.
| vbezhenar wrote:
| I'm pretty sure there are billions of people on the Earth
| unable to solve your geometry problem. That doesn't make
| them less human. It's not a benchmark. You should think
| about something almost any human can do, not selected few.
| That's the bar. Casual conversation is one of the examples
| that almost any human can do.
| steveBK123 wrote:
| Hence the pivot into ads, shop-in-chat and umm.. adult content.
| Analemma_ wrote:
| "AGI achieved internally"
|
| Another case of culture flowing from the top I guess.
| strangescript wrote:
| This entire thing has been pretty disingenuous on both sides of
| the fence. All the anti-AI (or anti OpenAI) people are doing
| victory laps, but what GPT-5 Pro did is still very valuable.
|
| 1) What good is your open problem set if really its a trivial
| "google search" away from being solved. Why are they not catching
| any blame here?
|
| 2) These answers still weren't perfectly laid out for the most
| part. GPT-5 was still doing some cognitive lifting to piece it
| together.
|
| If a human would have done this by hand it would have made news
| and instead the narrative would have been inverted to ask serious
| questions about the validity of some these style problem sets
| and/or ask the question how many other solutions are out there
| that just need pieced together from pre-existing research.
|
| But, you know, AI Bad.
| nurettin wrote:
| AI great, but AI not creative, yet.
| puttycat wrote:
| This is a strawman argument. No anti-AI sentiment was involved
| here. Simply the fact that finding and matching text on the
| Internet is several orders of magnitude easier than finding
| novel solutions to hard math problems.
| strangescript wrote:
| You didn't read the X replies if you believe that
| matsemann wrote:
| You're moving the goal post.
| Topfi wrote:
| > What good is your open problem set if really its a trivial
| "google search" away from being solved. Why are they not
| catching any blame here?
|
| They are a community run database, not the sole arbiter and
| source of this information. We learned the most basic research
| back in highschool, I'd hope researchers from top institutions
| now working for one of the biggest frontier labs can do the
| same prior to making a claim, but microblogging has and
| continues to be a blight on any accurate information so nothing
| new there.
|
| > GPT-5 was still doing some cognitive lifting to piece it
| together.
|
| Cognitive lifting? It's a model, not a person, but besides that
| fact, this was already published literature. Handy that a LLM
| can be a slightly better search, but calling claims of "solving
| maths problems" out as irresponsible and inaccurate is the only
| right choice in this case.
|
| > If a human would have done this by hand it would have made
| news [...]
|
| "Researcher does basic literature review" isn't news in this or
| any other scenario. If we did a press release every journal
| club, there wouldn't be enough time to print a single page
| advert.
|
| > [...] how many other solutions are out there that just need
| pieced together from pre-existing research [...]
|
| I am not certain you actually looked into the model output or
| why this was such an embarrassment.
|
| > But, you know, AI Bad.
|
| AI hype very bad. AI anthropomorphism even worse.
| andrepd wrote:
| > 1) What good is your open problem set if really its a trivial
| "google search" away from being solved. Why are they not
| catching any blame here?
|
| Please explain how this is in any way related to the matter at
| hand. What is the relation between the incompleteness of an
| math problem database, and AI hypesters lying about the
| capabilities of GPT5? I fail to see the relevance.
|
| > If a human would have done this by hand it would have made
| news
|
| If someone updated information on an obscure math problem
| aggregator database this would be news?? Again, I fail to see
| your point here.
| lukev wrote:
| Framing this question as "AI good" OR "AI bad" is culture-war
| thinking.
|
| The real problem here is that there's clearly a strong
| incentive for the big labs to deceive the public (and/or
| themselves) about the actual scientific and technical
| capabilities of LLMs. As Karpathy pointed out on the recent
| Dwarkesh podcast, LLMs are _quite terrible_ at novel problems,
| but this has become sort of an "Emperor's new clothes"
| situation where nobody with a financial stake will actually
| _admit_ that, even though it 's common knowledge if you
| actually work with these things.
|
| And this directly leads to the misallocation of _billions_ of
| dollars and potentially trillions in economic damage as
| companies align their 5-year strategies towards capabilities
| that are (right now) still science fiction.
|
| The truth is at stake.
| strangescript wrote:
| Except they weren't intentionally trying to deceive anyone.
| They made the faulty assumption that these problems were non-
| trivial to solve and didn't think it was simply GPT-5
| aggregating solutions in the wild.
| lukev wrote:
| Knowing what I know about LLMs, from their internal
| architecture and from extensive experience working with
| them daily, I would find this kind of result _highly
| surprising_ and in a clear violation of my mental model of
| how these things work. And I 'm very far from an expert.
|
| If a purported _expert in the field_ can is willing to
| credulously publish this kind of result, it 's not
| unreasonable to assume that either they're acting in bad
| faith, or (at best) are high on their own supply regarding
| what these things can actually do.
| random9749832 wrote:
| You are telling me a language model trained on Reddit can't solve
| novel problems? Shocking.
|
| Edit: we are in peak damage control phase of the hype cycle.
| creativeCak3 wrote:
| Can't wait for the Buble to burst so we can get back to solving
| real problems (like the fact that we have very little
| competition in the CPU market right now, AMD is getting way too
| comfortable...). I do think though that when this bubble bursts
| it will hurt the machine learning field (which does have people
| doing practically useful stuff like protein folding) and
| investors might pull all funding because this generative
| nonsense(which has no real use beyond generating porn) will
| taint the entire field, even the parts of it that are actually
| useful.
| phplovesong wrote:
| How fing obvious was it that AI slop did not do anything other
| than scarpe some websites.
| Jweb_Guru wrote:
| I felt like I was going crazy when people uncritically accepted
| the original claim from OpenAI. Have people actually _used_
| these models?
| amirhirsch wrote:
| The sad truth about this incident is that it reveals that OpenAI
| does not have a serious effort to actually work on unsolved math
| problems.
| rowanG077 wrote:
| How so? I wouldn't put much stock into a roque employee
| announcing something wrong.
| mrbungie wrote:
| That's not any employee, its their VP of Science.
| amirhirsch wrote:
| The people involved are very smart and must know that AI
| doing novel math is a canary for AGI. A serious effort
| around solving open problems would not fuck up this kind of
| announcement.
| coldtea wrote:
| "rogue employee"
| jebarker wrote:
| That's a non sequitur. They're a fairly large organization, I'd
| be amazed if they don't have multiple research sub-teams
| pursuing all sorts of different directions.
| grafmax wrote:
| I realized they jumped the shark when they announced the pivots
| to ads and porn. Markets haven't caught on yet.
| zeroonetwothree wrote:
| They know where the money is.
| grafmax wrote:
| It's standard practice for VC companies to enshittify after
| building a moat, relying on user lock-in. What's remarkable
| is how quickly they've had to shift gears. And with this
| rapid pivot it's questionable how large that moat really
| is.
| raincole wrote:
| I think people hugely overestimate how profitable porn (at
| least "actual" porn) is. Aylo (the owner of Pornhub) makes
| peanuts compared to Youtube or Disney.
| HarHarVeryFunny wrote:
| The porn / sex-chat one is really disappointing. It seems
| they've given up even pretending that they are trying to do
| something beneficial for society. This is just a pure
| society-be-damned money grab.
| disgruntledphd2 wrote:
| They've raised far too much money for those kinda ethics,
| unfortunately.
| bradly wrote:
| My hunch is that they don't have a way to stop anything, so
| they are creating verticals to at least contain porn,
| medical, higher-ed users.
| HarHarVeryFunny wrote:
| I'm pretty sure that if they didn't deliberately chose to
| train on sex chat/stories, etc, then the LLM wouldn't be
| any good at it. The model isn't getting this capability
| by training on WikiPedia or Reddit.
|
| So, it's not a matter of them not being able to do a good
| job of preventing the model from doing it, therefore
| giving up and instead encouraging it to do it (which
| anyways makes no sense), but rather them having chosen to
| train the model to do this. OpenAI is targetting porn as
| one of their profit centers.
| derektank wrote:
| >The model isn't getting this capability by training on
| WikiPedia or Reddit
|
| I don't know about the former, but the latter absolutely
| has sexually explicit material that could make the model
| more likely to generate erotic stories, flirty chats,
| etc.
| HarHarVeryFunny wrote:
| OK, maybe bad example, but it would be easy to create a
| classifier to identify stuff like that and omit it from
| the training data if they wanted to, and now that they
| are going to be selling this I'd assume they are
| explicitly seeking out and/or paying for creation of
| training material of this type.
| j_maffe wrote:
| Ah... The classic "If we don't do it, someone else will"
|
| Tell that to the thousands of 18 year olds who'll be
| captured by this predatory service and get AI psychosis
| goalieca wrote:
| The porn pivot makes perfect sense. Porn is already quite
| fake and unconvincing and none of that matters.
| throwacct wrote:
| Unfortunately, the porn pivot might be their path to
| "profitability".
| goalieca wrote:
| Global porn industry revenue is 100B. They won't take 10%
| of that. Real humans are already selling themselves
| pretty cheap or free en masse.
| mrbombastic wrote:
| It might not matter as far as profitability is concerned,
| ethically the second order effects will be very
| problematic. I am no puritan but the widespread
| availability of porn has already affected peoples sexual
| expectations greatly. AI generated porn is going to remove
| even more guardrails for behavior previously considered
| deviant, people will view and bring those expectations back
| to real life.
| glenstein wrote:
| To perhaps make the same point as you in a different way,
| I have no issue with "deviancy" but I think it can
| accelerate the cycle of chasing a sugar high.
| swat535 wrote:
| This is the same argument that people used for video
| games, "rock music" and violent movies.
|
| I would argue that AI generated porn might be more
| ethical than traditional porn because the risk of the
| models being abused or trafficked is virtually zero.
| malfist wrote:
| > because the risk of the models being abused or
| trafficked is virtually zero.
|
| That's not really true. Look at one if the more common
| uses for AI porn: taking a photo of someone and making
| them nude.
|
| Deepfake porn exists and it does harm
| derektank wrote:
| The harms associated with someone creating a deep fake of
| you are real but they're pretty insignificant compared to
| the harms associated with being sex trafficked or being
| exposed to an STI or being unable to find traditional
| employment after working in the industry.
| ummonk wrote:
| Would you support installing public spy cams in
| everyone's bedrooms so as to end the demand for human
| trafficking in porn?
| derektank wrote:
| No? And I didn't suggest deepfakes should be legal.
|
| I was just pointing out that when you're talking about
| the scale of harm caused by the existing sex industry
| compared to the scale of harm caused by AI generated
| pornographic imagery, one far outweighs the other.
| throwaway-0001 wrote:
| You couldn't just photoshop that before ai came out?
|
| What if you get a model that is 99% similar to your
| "target" - what we do with that?
| malfist wrote:
| Sure, someone skilled could spend an hour or so
| photoshoping someone nude. But any teenager can do that
| to a classmate in 30 seconds with ai
| chanux wrote:
| And there's no escape. The Internet was built for gambling
| and this.
| grafmax wrote:
| People spin up ablated models for pennies. You don't need
| advanced reasoning for this crap. OpenAI has 8 billion plus
| in burn. I guess it's all effectively paying for brand
| awareness?
| JKCalhoun wrote:
| I try not to lose sight of the first time that I heard (some
| years back) that people were using this new LLM thing for DM'ing
| ("dungeon mastering", leading) a game of _Dungeons and Dragons_.
| I thought, this must be bullshit or some kind of witchcraft.
|
| Definitely not anti-AI here. I think I have been disappointed
| though, since then, to slowly learn that they're (still) little
| beyond that.
|
| Still amazing though. And better than a Google search (IMHO).
| llm_nerd wrote:
| Yann LeCun's "Hoisted by their own GPTards" is fantastic.
| frays wrote:
| I might be missing context here, but I'm surprised to see Yann
| using language that plays on 'retard.'
|
| That seems out of character for him - more like something I'd
| expect from Elon Musk. What's the context I'm missing?
| nova22033 wrote:
| It's a play on the word petard
| microtonal wrote:
| I found this background useful as a non-native speaker:
| https://en.wikipedia.org/wiki/Hoist_with_his_own_petard
| grey-area wrote:
| Hoist (thrown in the air) by your own petard (bomb) is a
| common phrase.
| znkr wrote:
| I don't think it's a wordplay with the r-word, but rather a
| reference to the famous Shakespeare quote: "Hoist with his
| own petard". It's become an English proverb. (A petard is a
| smallish bomb)
| card_zero wrote:
| From _peter,_ to fart.
|
| Possibly entered the language as a saying due to
| Shakespeare being scurrilous.
| NitpickLawyer wrote:
| While Yann is clearly brilliant, and has a deeper understanding
| of the roots of the filed than many of us mortals, I think he's
| been on a debbie downer trend lately, and more importantly,
| some of his public stances have been proven wrong in mere
| months / years after he made them.
|
| I remember a public talk, where he was on the stage with some
| young researcher from MS. (I think it was one of the authors of
| the "sparks of brilliance in gpt4" paper, but not sure).
|
| Anyway, throughout that talk he kept talking above the guy, and
| didn't seem to listen, even though he obviously didn't try the
| "raw", "unaligned" model that the folks at MS were talking
| about.
|
| And he made 2 big claims:
|
| 1) LLMs can't do math. He went on to "argue" that LLMs trick
| you with poetry that sounds good, but is highly subjective, and
| when tested on hard verifiable problems like math, they fail.
|
| 2) LLMs can't plan.
|
| Well, merely one year later, here we are. AIME is saturated
| (with tool use), gold at IMO, and current agentic uses clearly
| can plan (and follow up with the plan, re-write parts, finish
| tasks, etc etc).
|
| So, yeah, I'd take everything any one singular person says with
| a huge grain of salt. No matter how brilliant said individual
| is.
|
| Edit: oh, and I forgot another important argument that Yann
| made at that time:
|
| 3) because of the nature of LLMs, errors compound. So the
| longer you go in a session, the more errors accumulate so they
| devolve in nonsense.
|
| Again, mere months later the o series of models came out, and
| basically proved this point moot. Turns out RL + long context
| mitigate this fairly well. And a year later, we have all SotA
| models being able to "solve" problems 100k+ tokens deep.
| mrbungie wrote:
| Pretty sure you can fill a room with serious researchers that
| at the very least will doubt about 2) being solved with LLMs,
| especially when talking about formal planning with pure LLMs
| and without a planning framwork.
|
| PS: So just we're clear: formal planning in AI </> making a
| coding plan in Cursor.
| NitpickLawyer wrote:
| > with pure LLMs and without a planning framwork.
|
| Sure, but isn't that moving the goalposts? Why shouldn't we
| use LLMs + tools if it works? If anything it shows that the
| early detractors weren't even considering this could work.
| Yann in particular was skeptical that long-context things
| can happen in LLMs at all. We now have "agents" that can
| work a problem for hours, with self context trimming,
| planning to md files, editing those plans and so on. All of
| this just works, today. We used to dream about it a year
| ago.
| mrbungie wrote:
| > Sure, but isn't that moving the goalposts?
|
| It can be considered as that, sure, but anytime I see
| Lecun talking about this, he does recognize that you can
| patch your way around LLMs, the point is that you are
| going to hit limits eventually anyways. Specific planning
| benchmarks like Blockworld and the like show that LLMs
| (with frameworks) hit limits when they're exposed to out-
| of-distribution problems, and that's a BIG problem.
|
| > We now have "agents" that can work a problem for hours,
| with self context trimming, planning to md files, editing
| those plans and so on. All of this just works, today. We
| used to dream about it a year ago.
|
| I use them everyday but I still woulnd't really let them
| work for hours in greenfield projects. And we're seeing
| big vibe coders like Karpathy say the same.
| badsectoracula wrote:
| > Sure, but isn't that moving the goalposts? Why
| shouldn't we use LLMs + tools if it works?
|
| Personally i do not see it like that at all as one is
| referring to LLMs specifically while the other is
| referring to LLMs plus a bunch of other stuff around
| them.
|
| It is like person A claiming that GIF files can be used
| to play Doom deathmatches, person B responding that, no,
| a GIF file cannot start a Doom deathmatch, it is
| fundamentally impossible to do so and person A retorting
| that since the GIF format has a provision for advancing a
| frame on user input, a GIF viewer can interpret that
| input as the user wanting to launch Doom in deathmatch
| mode - ergo, GIF files can be used to play Doom
| deathmatches.
| NitpickLawyer wrote:
| At the end of the day LLM + tools is asking the LLM to
| create a story with very specific points where "tool
| calls" are parts of the story, and "tool results" are
| like characters that provide context. The fact that they
| _can_ output stories like that, with enough accuracy to
| make it worthwhile is, IMO, proof that they can "do"
| whatever we say they can do. They can "do" math by
| creating a story where a character takes NL and invokes a
| calculator, and another character provides the actual
| computation. Cool. It's still the LLM driving the
| interaction. It's still the LLM creating the story.
| badsectoracula wrote:
| I think you have that last part backwards, it is not the
| LLM driving the interaction, it is the program that uses
| the LLM to generate the instructions that does the actual
| driving - that is the bit that makes the LLM start doing
| things. Though that is just splitting hairs.
|
| The original point was about the capabilities _LLMs
| themselves_ since the context was about the technology
| itself, not what you can do by making them part of a
| larger system that combines LLMs (perhaps more than one)
| with other tools.
|
| Depending on the use case and context this distinction
| may or may not matter, e.g. if you are trying to sell the
| entire system, it probably is not any more important how
| the individual parts of the system work than what
| libraries you used to make the software.
|
| However it can be important in other contexts, like
| evaluating the abilities of LLMs themselves.
|
| For example i have written a script on my PC that my
| window manager calls to grab whatever text i have
| selected on whatever application i'm running and passes
| it to a program i've written in llama.cpp to load Mistral
| Small with a prompt that makes it check for spelling and
| grammar mistakes which in turn produces some script-
| readable input that another script displays in a window.
|
| This, in a way, is an entire system. This system helps me
| find grammar and spelling mistakes in the text i have
| selected when i'm writing documents where i care about
| finding such mistakes. _However_ it is not Mistral Small
| that has the functionality of finding grammar and
| spelling mistakes in my selected text, it only provides
| the part that does the text checking, the rest is done by
| other external non-LLM pieces. An LLM cannot intercept
| keystrokes in my computer, it cannot grab my selected
| text nor can create a window on my desktop, it doesn 't
| even understand these concepts. In a way this can be
| thought as a limitation from the perspective of the end
| result i want, but i work around it with the other
| software i have attached to it.
| pessimizer wrote:
| > Why shouldn't we use
|
| So weird that you immediately move the goalposts after
| accusing somebody of moving the goalposts. Nobody on the
| planet told you not to use "LLMs + tools if they work."
| You've moved onto an entirely different discussion with a
| made-up person.
|
| > All of this just works, today.
|
| Also, it definitely doesn't "just work." It slops around,
| screws up, reinserts bugs, randomly removes features,
| ignores instructions, lies, and sometimes you get a lucky
| result or something close enough that you can fix up.
| Nothing that should be in production.
|
| Not that they're not very cool and very helpful in a lot
| of ways. But I've found them more helpful in showing me
| how they would do something, and getting me so angry that
| they nerd-snipe me into doing it correctly. I have to
| admit, 1) however, that sometimes I'm not sure that I'd
| have gotten there if I hadn't seen it _not_ getting
| there, and 2) sometimes "doing it correctly" involves
| dumping the context and telling it almost _exactly_ how I
| want something implemented.
| Topfi wrote:
| > AIME is saturated (with tool use) [...]
|
| But isn't tool use kinda the crux here?
|
| Correct me if I'm mistaken, but wasn't the argument back then
| on whether LLMs could solve maths problems without e.g.
| writing python to solve? Cause when "Sparks of AGI" came out
| in March, prompting gpt-3.5-turbo to code solutions to assist
| solving maths problems over just solving them directly was
| already established and seemed like the path forward. Heck,
| it is still the way to go, despite major advancements.
|
| Given that, was he truly mistaken on his assertions regarding
| LLMs solving maths? Same for "planning".
| NitpickLawyer wrote:
| AIME was saturated with tool use (i.e. 99%) for SotA
| models, but pure NL, no tool still perform "unreasonably
| well" on the task. Not 100% but still within 90%. And with
| lots of compute it can reach 99% as well, apparently [1]
| (@512 rollouts, but still)
|
| [1] - https://arxiv.org/pdf/2508.15260
| goalieca wrote:
| > LLMs can't do math. He went on to "argue" that LLMs trick
| you with poetry that sounds good, but is highly subjective,
| and when tested on hard verifiable problems like math, they
| fail.
|
| They really can't. Token prediction based on context does not
| reason. You can scramble to submit PRs to ChatGPT to keep up
| with the "how many Rs in blueberry" kind of problems but it's
| clear they can't even keep up with shitposters on reddit.
|
| And your 2nd and third point about planning and compounding
| errors remain challenges.. probably unsolvable with LLM
| approaches.
| NitpickLawyer wrote:
| > They really can't. Token prediction based on context does
| not reason.
|
| Debating about "reasoning" or not is not fruitful, IMO.
| It's an endless debate that can go anywhere and nowhere in
| particular. I try to look at results:
|
| https://arxiv.org/pdf/2508.15260
|
| Abstract:
|
| > Large Language Models (LLMs) have shown great potential
| in reasoning tasks through test-time scaling methods like
| self-consistency with majority voting. However, this
| approach often leads to diminishing returns in accuracy and
| high computational overhead. To address these challenges,
| we introduce Deep Think with Confidence (DeepConf), a
| simple yet powerful method that enhances both reasoning
| efficiency and performance at test time. DeepConf leverages
| modelinternal confidence signals to dynamically filter out
| low-quality reasoning traces during or after generation. It
| requires no additional model training or hyperparameter
| tuning and can be seamlessly integrated into existing
| serving frameworks. We evaluate DeepConf across a variety
| of reasoning tasks and the latest open-source models,
| including Qwen 3 and GPT-OSS series. Notably, on
| challenging benchmarks such as AIME 2025, DeepConf@512
| achieves up to 99.9% accuracy and reduces generated tokens
| by up to 84.7% compared to full parallel thinking.
| goalieca wrote:
| > Debating about "reasoning" or not is not fruitful, IMO.
|
| Thats kind of the whole need isn't it? Humans can
| automate simple tasks very effectively and cheaply
| already. If I ask my pro versions of LLM what the Unicode
| value of a seahorse is, and it shows a picture of a horse
| and gives me the Unicode value for a third completely
| related animal then it's pretty clear it can't reason
| itself out of a wet paper bag.
| NitpickLawyer wrote:
| Sorry perhaps I worded that poorly. I meant debating
| about if context stuffing is or isn't "reasoning". At the
| end of the day, whatever RL + long context does to LLMs
| seems to provide good results. Reasoning or not :)
| goalieca wrote:
| Well that's my point and what I think the engineers are
| screaming at the top of their lungs these days.. that
| it's net negative. It makes a really good demo but hasn't
| won anything except maybe translating and simple graphics
| generation.
| Palmik wrote:
| The original tweet was clearly misunderstood...
|
| https://x.com/SebastienBubeck/status/1977181716457701775:
|
| > gpt5-pro is superhuman at literature search:
|
| > it just solved Erdos Problem #339 (listed as open in the
| official database https://erdosproblems.com/forum/thread/339) by
| realizing that it had actually been solved 20 years ago
|
| https://x.com/MarkSellke/status/1979226538059931886:
|
| > Update: Mehtaab and I pushed further on this. Using thousands
| of GPT5 queries, we found solutions to 10 Erdos problems that
| were listed as open: 223, 339, 494, 515, 621, 822, 883 (part
| 2/2), 903, 1043, 1079.
|
| It's clearly talking about finding existing solutions to "open"
| problems.
|
| The main mistake is by Kevin Weil, OpenAI CTO, who misunderstood
| the tweet:
|
| https://x.com/kevinweil/status/1979270343941591525:
|
| > you are totally right--I actually misunderstood @MarkSellke's
| original post, embarrassingly enough. Still very cool, but not
| the right words. Will delete this since I can't edit it any
| longer I think.
|
| Obviously embarassing, but completely overblown reaction. Just
| another way for people to dunk on OpenAI :)
| Topfi wrote:
| If holding the CTO of OpenAI accountable for his wildly
| inaccurate statement constitutes "dunking on OpenAI", then I'd
| say dunk away.
|
| He, more than anyone else, should be able to for one parse the
| original statements correctly and for another maybe realize
| that if one of their models had accomplished what he seemed to
| think GPT-5 had, that may require some more scrutiny and
| research before posting it. That would have, after all, been a
| clear and incredibly massive development for the space,
| something the CTO of OpenAI should recognize instantly.
|
| The amount of people that told me this is clear and
| indisputable proof that AGI/ASI/whatever is either around the
| corner or already here is far more than zero and arguing
| against their misunderstanding was made all the more
| challenging because "the CTO of OpenAI knows more than you" is
| quite a solid appeal to authority.
|
| I'd recommend maybe a waiting period of 48h before any
| authority in any field can send a tweet, that might resolve
| some of the inaccuracies and the incredibly annoying need to
| just jump on wild bandwagons...
| zozbot234 wrote:
| "you are totally right--I actually misunderstood" ...like,
| seriously? Did an AI come up with this retraction, or are
| humans actually talking like robots now?
| Topfi wrote:
| Guess even the CTO of OpenAI relies on Anthropic models in a
| pinch...
| 827a wrote:
| This happening the same week as DeepMind's seemingly legitimate
| AI-assisted cancer treatment breakthrough is a startlingly bad
| look for OpenAI.
|
| My boss always used to say "our only policy is, don't be the
| reason we need to create a new policy". I suspect OpenAI is going
| to have some new public communication policies going forward.
| gpjt wrote:
| To be fair to the OpenAI team, if read in context the situation
| is at worst ambiguous.
|
| The deleted tweet that the article is about said "GPT-5 just
| found solutions to 10 (!) previously unsolved Erdos problems, and
| made progress on 11 others. These have all been open for
| decades." If it had been posted stand-alone then I would
| certainly agree that it was misleading, but it was not.
|
| It was a quote-tweet of this:
| https://x.com/MarkSellke/status/1979226538059931886?t=OigN6t...,
| where the author is saying he's "pushing further on this".
|
| The "this" in question is what this second tweet is in turn
| quote-tweeting:
| https://x.com/SebastienBubeck/status/1977181716457701775?t=T...
| -- where the author says "gpt5-pro is superhuman at literature
| search: [...] it just solved Erdos Problem #339 (listed as open
| in the official database erdosproblems.com/forum/thread/3...) by
| realizing that it had actually been solved 20 years ago"
|
| So, reading the thread in order, you get *
| SebastienBubeck: "GPT-5 is really good at literature search, it
| 'solved' an apparently-open problem by finding an existing
| solution" * MarkSellke: "Now it's done ten more" *
| kevinweil: "Look at this cool stuff we've done!"
|
| I think the problem here is the way quote-tweets work -- you only
| see the quoted post and not anything that it in turn is quoting.
| Kevin Weil had the two previous quotes in his context when he did
| his post and didn't consider the fact that readers would only see
| the first level, so wouldn't have Sebastien Bubek's post in mind
| when they read his.
|
| That seems like an easy mistake to entirely honestly make, and I
| think the pile-on is a little unfair.
| card_zero wrote:
| So the first guy said "solved [...] by realizing that it had
| actually been solved 20 years ago", and the second guy said
| "found solutions to 10 (!) previously unsolved Erdos problems".
|
| _Previously unsolved._ The context doesn 't make that true,
| does it?
| glenstein wrote:
| Right, and I would even go a step further and say the context
| from SebastienBubeck is stretching "solved" past its breaking
| point by equating literature research with self-bootsrapped
| problem solving. When it's later characterized as "previously
| unsolved" it's doubling down on the same equivocation.
|
| Don't get me wrong, effectively surfacing unappreciated
| research is great and extremely valuable. So there's a real
| thing here but with the wrong headline attached to it.
| moefh wrote:
| > Kevin Weil had the two previous quotes in his context when he
| did his post and didn't consider the fact that readers would
| only see the first level, so wouldn't have Sebastien Bubek's
| post in mind when they read his.
|
| No, Weil said he himself misunderstood Sellke's post[1].
|
| Note Weil's wording (10 _previously unsolved_ Erdos problems)
| vs. Sellke 's wording (10 Erdos problems that _were listed as
| open_ ).
|
| [1] https://x.com/kevinweil/status/1979270343941591525
| GodelNumbering wrote:
| Also, previous comment omitted the part that now-deleted
| tweet from Bubeck begins with "Science revolution via AI has
| officially begun...".
| Frieren wrote:
| > "GPT-5 is really good at literature search, it 'solved' an
| apparently-open problem by finding an existing solution"
|
| Survivor bias.
|
| I can assure you that GPT-5 fucks up even relatively easy
| searches. I need to have a very good idea how the results looks
| like and the ability to test it to be able to use any result
| from GPT-5.
|
| If I throw the dice 1000 times and post about it each time that
| I got a double six. Am I the best dice thrower that there is?
| zacmps wrote:
| For literature search that might be ok. It doesn't need to
| replace any other tools, and if 1/10 it surfaces something
| you wouldn't have found otherwise it could be worth the time
| on the dud attempts.
| OtherShrezzing wrote:
| Am I correct in thinking this is the 2nd such fumble by a major
| lab? DeepMind released their "matrix multiplication better than
| SOTA" paper a few months back, which suggested Gemini had
| uncovered a new way to optimally multiply two matrices in fewer
| steps than previously known. Then immediately after their
| announcement, mathematicians pointed out that their newly
| discovered SOTA had been in the literature for 30-40 years, and
| was almost certainly in Gemini's training set.
| glenstein wrote:
| It's an interesting type of fumble too, because it's easy to
| (mistakenly!) read it as "LLM tries and fails to solve
| problem but thinks it solved it" when really it's being
| credited with originality for discovering or reiterating
| solutions already out there in the literature.
|
| It sounds like the content of the solutions themselves are
| perfectly fine, so it's unfortunate that the headline will
| leave the impression that these are just more hallucinations.
| They're not hallucinations, they're not wrong, they're just
| wrongly assigned credit for existing work. Which, you know,
| where have we heard that one before? It's like the stylistic
| "borrowing" from artists, but in research form.
| card_zero wrote:
| Well, it _is_ important that we have some technology to
| prevent us from going round in circles by reinventing things,
| such as search.
| ummonk wrote:
| We also had the GPT-5 presentation which featured both
| incorrect bar charts (likely AI generated) and an incorrect
| explanation of lift.
| ogogmad wrote:
| No, your claim about matrix multiplication is false. Google's
| new algorithm can be applied recursively to 4x4 block
| matrices (over the field of complex numbers). This results in
| an asymptotically faster algorithm for nxn matrix
| multiplication than Strassen's. Earlier results on 4x4
| matrices by Winograd and others did not extend to block
| matrices..
|
| Google's result has more recently been generalised:
| https://arxiv.org/abs/2506.13242
| jsnell wrote:
| That doesn't match my recollection of the AlphaEvolve
| release.
|
| Some people just read the "48 multiplications for a 4x4
| matrix multiplications" part, and thought they found prior
| art at that performance or better. But they missed that the
| supposed prior art had tighter requirements on the _contents_
| of the matrix, which meant those algorithms were not usable
| for implementing a recursive divide and conquer algorithm for
| much larger matrix multiplications.
|
| Here is a HN poster claiming to be one of the authors
| rebutting the claim of prior art:
| https://news.ycombinator.com/item?id=43997136
| camillomiller wrote:
| I have some more mirrors for you to try and climb, if you need
| them.
| jibal wrote:
| That's being disingenuous, not fair.
| ripped_britches wrote:
| I make mistakes all the time. This seems like a genuine mistake,
| not malice.
|
| Imagine if you were talking about your own work online, you make
| an honest mistake, then the whole industry roasts you for it.
|
| I'm so tired of hearing everyone take stabs at people at OpenAI
| just because they don't personally like sama or something.
| strongbond wrote:
| Maybe Open AI shouldn't be so stabable?
| Timsky wrote:
| > GPT-5 is proving useful as a literature review assistant
|
| No, it does not. It only produces a highly convincing
| counterfeit. I am honestly happy for people who are satisfied
| with its output: life is way easier for them than for me.
| Obviously, the machine discriminates me personally. When I spend
| hours in the library looking for some engineering-related math
| made in the 70s-80s, as a last resort measure, I can try to play
| this gambling with chat, hoping for any tiny clue to answer my
| question. And then for the following hours, I am trying to
| understand what is wrong with the chat output. Most often, I
| experience the "it simply can't be" feeling, and I know I am not
| the only one having it.
| glenstein wrote:
| Struggling to understand this one. Is it that (1) it's lopsided
| toward reference materials found on the modern internet and not
| as useful for reviewing literature from the Before Times or (2)
| it's offering specific solutions but you're skeptical of them?
| crazygringo wrote:
| In my experience doing literature super-deep-dives, it
| hallucinates sources about 50% of the time. (For higher-level
| literature surveys, it's maybe 5%.)
|
| Of the other 50% that are real, it's often ~evenly split into
| sources I'm familiar with and sources I'm not.
|
| So it's _hugely_ useful in surfacing papers that I may very
| well never have found otherwise using e.g. Google Scholar. It
| 's particularly useful in finding relevant work in parallel
| subfields -- e.g. if you work in physics but it turns out their
| are math results, or you work in political science and it turns
| out there are relevant findings from anthropology. And also
| just obscure stuff -- a random thesis that never got published
| or cited but the PDF is online and turns out to be relevant.
|
| It doesn't matter if 75% of the results are not useful to me or
| hallucinated. Those only waste me minutes. The other 25% _more_
| than make up for it -- they 're things I simply might never
| find otherwise.
| macrolime wrote:
| What is "it". Gpt-5 auto? Gpt-5 pro? Deep research? These
| have wildly different hallucination rates.
| bathtub365 wrote:
| If these rates are known it would be great for OpenAI to be
| open about them so customers can make an informed decision
| malfist wrote:
| "Known" implies that these rates are consistent and
| measurable. It seems to me, that this is highly unlikely
| to be the case
| clbrmbr wrote:
| OpenAI goes into great detail on hallucination rates of
| GPT5 models versus o3 in the GPT5 System Card [1],
| section 3.7.
|
| [1] https://cdn.openai.com/gpt-5-system-card.pdf#page12
| Maxatar wrote:
| OpenAI has published a great deal of information about
| hallucination rates, as have the other major LLM
| providers.
|
| You can't just give one single global hallucination rate
| since the rates depend on the different use cases and
| despite the abundant amount of information available to
| people on how to pick the appropriate tool for a given
| task, it seems very few people care to take the time to
| actually first recognize that these LLMs are tools, and
| that you do need to learn how to use these tools in order
| to be productive with them.
| crazygringo wrote:
| I use all of the current versions of ChatGPT, Gemini, and
| Claude.
|
| The hallucination rates are about the same as far as I can
| tell. It depends mostly on how niche the area is, not which
| model. They do seem to train on somewhat different sets of
| academic sources, so it's good to use them all.
|
| I'm not talking about deep research or advanced thinking
| modes -- those are great for some tasks but don't really
| add anything when you're just looking for all the sources
| on a subject, as opposed to a research report.
| andrewflnr wrote:
| So, the exact stuff Google used to be good at.
| georgemcbay wrote:
| Pretty much, though Google got bad at these things well
| before LLMs really came on to the scene, and we can all
| debate which project manager was responsible and the month
| and year things took a downward turn, but the IMO obvious
| catalyst was that "Barely Good Enough" search creates more
| ad impressions, especially when virtually all of the bad
| results you are serving are links to sites that also serve
| Google managed ads.
| andrewflnr wrote:
| Oh, sure, Google was starting to take a dive almost a
| decade before LLMs came on the scene.
| xiphias2 wrote:
| It was a very clear point: when Amit Singhal was kicked
| out for sexual harassment in the me too era. He was the
| heart of search quality but he went too far when he was
| drinking.
| rhetocj23 wrote:
| Apple is the only firm that seems to do a good job in
| preventing itself from falling prey to what leads to the
| demise of every corp in history.
| ramenbytes wrote:
| The exact stuff I now use Kagi for. Finding obscure
| relevant PDFs that Google didn't is literally one of the
| things that made me switch.
| andrepd wrote:
| Another win for big tech: Google has been enshittified to
| such a point that you can now spin up a machine that
| consumes 1000x the power to give you a result that has a
| coin toss odds of being totally made up.
| bbarnett wrote:
| That's nothing! Next gen will use the entire power output
| of a small nation for a week, to tell you a nice cake
| recipe.
| jsnell wrote:
| A search query probably uses about 10x _more_ electricity
| than a matching LLM query. There 's enough wiggle-room
| depending on the assumptions that they might be about
| even. There is no way search uses 1/1000th of an LLM.
| JamesBarney wrote:
| What questions are you asking LLMs where they're wrong
| 50% of the time?
| crazygringo wrote:
| Nope. I'm talking about the stuff keywords are no good at,
| and which Google Scholar doesn't tend to surface because
| it's just not cited much or it's from a different niche.
|
| The fact that LLM's understand your question semantically,
| not just with keyword matching, is _huge_.
| scosman wrote:
| Saying it isn't useful is a bit of an overstatement. It can
| search, churn through 500k words in a few minutes, and come
| back with summaries, answers, and sources for each point.
|
| Should you blindly trust the summary? No. Should you verify key
| claims by clicking through to the source? Yes. Is it still
| incredibly useful as a search tool and productivity booster?
| Absolutely.
| scruple wrote:
| I gave it a PDF recently and asked it to help me generate
| some tables based on the information there in. I thought I'd
| be saving myself time. I spent easily twice as long as I
| would have if it I had done it myself. It kept making trivial
| mistakes, misunderstanding what was in the PDF,
| hallucinating, etc.
| ben_w wrote:
| Last summer I used one of the models to help translate a
| few German wikipedia pages to English, hoping it would make
| things easier by keeping all the formatting etc. that I'd
| lose if I copy-pasted mere content via Google Translate.
|
| I did check the translations were correct as part of this
| -- while my German isn't great, it was sufficient for this
| -- and it was fine up until reaching a long table about the
| timeline of events relevant to the subject, at which point
| it couldn't help but make stuff up.
|
| Still useful, but when you find the limits of their
| competence, there's no point attempting to cajole them to
| go further. They'll save you whatever % of the task in
| effort, now you have to do all the rest; it's a waste of
| effort to think either carrot or stick will get them to
| succeed if they can't do it in the first few tries.
| Timsky wrote:
| It is excellent when just finding something is enough. Most
| often in my practice, I am dealing with questions that have
| no written-down answers, meaning the probability of finding a
| book/article that provides one is negligible. Instead, I am
| looking for indirect answers or proofs before I make a final
| engineering decision. Yet another problem is that the
| language itself changes over time. For instance, at the
| beginning of the 20th century, the integers were called
| integral numbers. IMHO, LLMs poorly handle such cases when
| considered as a substitute for search engines. For full-text
| vector search, I am using https://www.recoll.org/ a real time
| saver for me, especially for desktop search.
| scosman wrote:
| > GPT-5 is proving useful as a literature review assistant
|
| > No, it does not.
|
| > It is excellent when just finding something is enough.
| Timsky wrote:
| I meant that it obviously fits your needs but not mine
| signatoremo wrote:
| > No, it does not. It only produces a highly convincing
| counterfeit.
|
| How could you say that with high confidence when you
| admitted it might be useful for others?
| Timsky wrote:
| Because this is precisely what the word counterfeit
| means, an imitation that deceives you. The functionality
| of counterfeit can be from 0% to 100%, depending on your
| luck. If you accidentally bought a fake iPhone, it can
| still make calls. Chat output is something that looks
| like a literature review, some collection of summaries of
| relevant papers. But the problem is that a review is not
| just text compression. It is also about rejecting low-
| quality research, considering the historical context,
| identifying contradictions, etc. No machine can do that
| analysis for you, yet. I have quite a confidence in that.
| bootsmann wrote:
| If I knew what's in the paper I don't need the summary but if
| I don't know what's in the paper I cannot possibly judge the
| accuracy of its summary.
| kianN wrote:
| If you're interested in a literature review tool, I built a
| public one for some friends in grad school that uses
| hierarchical mixture models to organize bulk searches and
| citation networks.
|
| Example:
| https://platform.sturdystatistics.com/deepdive?search_type=e...
| Timsky wrote:
| Thank you for sharing! I like your dendrogram-like circular
| graphs! They are way more intuitive. That could be a nice
| companion for a bibliometrix/biblioshiny library for
| bibliometric analysis https://www.bibliometrix.org/. I tried
| "Deep Dive" with my own request, and ... it unfortunately
| stops at the end of "Organizing results". Maybe I should try
| again later.
| kianN wrote:
| Haha that's embarrassing! The progress bars are an
| estimate. If a paper has a lot of citations, it may take a
| bit longer than the duration of the bars but it will
| hopefully finish relatively soon!
|
| Edit: Got home and checked the error logs. There was a very
| long search query with no results. Bug on my end to not
| return an error in that case.
|
| If you were hoping to use the citation network, it needs
| the url as input rather than the title.
| andai wrote:
| There's this principle, I forget the name, but how everyone
| when reading the newspaper, when they read on a subject they're
| familiar with, will instantly spot all the holes, all the
| errors. And they will ask themselves, how was this even
| published in the first place?
|
| But then they flip to the next page and they read a story on a
| subject they're not an expert on and they just accept all of it
| without question.
|
| I think people might have a similar relationship with ChatGPT.
| btrettel wrote:
| https://en.wikipedia.org/wiki/Gell-Mann_amnesia_effect
| lukev wrote:
| The Gell-Mann Amnesia effect. And you're absolutely right,
| it's extremely pronounced in LLM users.
| aleph_minus_one wrote:
| > The Gell-Mann Amnesia effect. And you're absolutely
| right, it's extremely pronounced in LLM users.
|
| And I guess a lot of LLM-hype critics have the trait to be
| much less capable of "being able to flip to the next page
| and read a story on a subject they're not an expert on and
| they just accept all of it without question".
|
| Because this is an unusual personality trait, these LLM-
| hype critics get reprimanded all the time by the "mob" that
| they don't see the great opportunities that LLMs could
| bring, even though the LLMs may not be perfect.
| ben_w wrote:
| I've thought of the same analogy, but you know, I've never
| actually seen someone go "It's wrong about the stuff I
| understand, but I'll trust it anyway on everything I know
| nothing about". It's either:
|
| (1) people getting caught using it to do their own jobs for
| them (i.e. they don't even realise it's wrong about the
| stuff they do understand);
|
| (2) people who see the problems and therefore don't trust
| them _anywhere at all_ (i.e. no amnesia, quite sensible
| reaction);
|
| (3) people who see the problems and therefore limit their
| use to domains where the answers can be verified (I do
| this).
|
| --
|
| As an aside, I'm a little worried that I keep spotting
| turns of phrase that I associate with LLMs, for example
| where you write "you're absolutely right": I have no idea
| if that's all just us monkeys copying what we see around us
| (something we absolutely do), or if you're using that
| phrase deliberately because of the associations.
|
| The only thing I'm confident of is that you're _not_ doing
| is karma-farming with an LLM, but that 's based on your
| other comments not sounding at all like LLMs so why would
| you (oh how surprising it was when I was first accused of
| being an LLM), but eh, dead internet theory feels more and
| more real...
| happy_dog1 wrote:
| I wonder whether for a lot of the search & literature review-
| type use-cases where people are trying to use GPT-5 and similar
| we'd honestly be much better off with a really powerful
| semantic search engine? Any time you ask a chatbot to summarize
| the literature for you or answer your question, there's a risk
| it will hallucinate and give you an unreliable answer. Using
| LLM-generated embeddings for documents to retrieve the nearest
| match, by contrast, doesn't run any risk of hallucination and
| might be a powerful way to retrieve things that Google / Bing
| etc. wouldn't be able to find using their current algorithms.
|
| I don't know if something like this already exists and I'm just
| not aware of it to be fair.
| Timsky wrote:
| I think you have a very good point here: a semantic search
| would be the best option for such a search. The items would
| have unique identifiers so the language variations can be
| avoided. But unfortunately, I am not aware of any of these
| kinds of publicly available projects, except DBpedia and some
| biology-oriented ontologies that would massively analyze
| scientific reports.
|
| Currently, I am applying RDF/OWL to describe some factual
| information and contradictions in the scientific literature.
| On an amateur level. Thus I do it mostly manually. The GPT-
| discourse somehow brings up not only the human-related
| perception problems, such as cognitive biases, but also truly
| philosophical questions of epistemology that should be
| resolved beforehand. LLM developers cannot solve this because
| it is not under their control. They can only choose what to
| learn from. For instance, when we consider a scientific text,
| it is not an absolute truth but rather a carefully verified
| and reviewed opinion that is based on the previous authorized
| opinions and subject to change in the future. So the same
| author may have various opinions over time. More recent
| opinions are not necessarily more "truthful" ones. Now
| imagine a corresponding RDF triple (subject-predicate-object
| tuple) that describes that. Pretty heavy thing, and no NLTK
| can decide for us what the truth is and what is not.
| opdahl wrote:
| Since you specifically were wondering if something like this
| exist, I feel okay with mentioning my own tool
| https://keenious.com since I think it might fit your needs.
|
| Basically we are trying to combine the benefits of chat with
| normal academic search results using semantic search and
| keyword search. That way you get the benefit of LLMs but
| you're actually engaging with sources like a normal search.
|
| Hope it was what you were looking for!
| malshe wrote:
| I think its scope is narrower than a lit review assistant. I
| use it mainly for finding papers that I or my RAs might have
| missed in our lit review.
|
| I have a recent example where it helped me locate a highly
| relevant paper for my research. It was from an obscure journal
| and wouldn't show up in the first few pages of Google Scholar
| search. The paper was real and recently published.
|
| However, using LLMs for _doing_ lit review has been fraught
| with peril. LLMs often misinterpret the research findings or
| extrapolate them to make incorrect inferences.
| cj wrote:
| I was reminded how terribly ChatGPT hallucinates this morning
| when I used it to look up the 7 point measurement locations for
| skin fold calipers to estimate body fat.
|
| It correctly described the locations in text, then it offered
| to provide a diagram.
|
| I said "sure", and it generated an image saying the chest
| location is on the neck, and a bunch of other clearly incorrect
| locations for the other measurement sites.
|
| It's gotten better. But it's still bad.
| cedws wrote:
| Making such a claim should at the very least require proof that
| the information wasn't in the training data.
| d_tr wrote:
| Whatever happened to "don't get high on your own supply"?
| DonHopkins wrote:
| Man, that's soap, man!!!
|
| https://www.youtube.com/watch?v=RvGE-xhroy0
|
| [drinks pee twice]
| YesBox wrote:
| Wouldn't be surprised if OpenAI employees are being asked to
| phrase ( _market_ ) things this way. This is not the first time
| they claimed GPT-5 "solved" something [1]
|
| [1] https://x.com/SebastienBubeck/status/1970875019803910478
|
| edit: full text
|
| _It 's becoming increasingly clear that gpt5 can solve MINOR
| open math problems, those that would require a day/few days of a
| good PhD student. Ofc it's not a 100% guarantee, eg below gpt5
| solves 3/5 optimization conjectures. Imo full impact of this has
| yet to be internalized..._
| flkiwi wrote:
| Thanks for calling that out. You're right to be upset.
| sbaidon94 wrote:
| You would think Open AI employees have a pretty good grasp of
| their model capabilities, but even if you don't, you probably
| always want to be on the cautious side for every claim you see on
| the internet.
|
| This just seems to be the Open AI culture, which for better or
| worse has helped foster the AI hype environment we are currently
| in.
| bwfan123 wrote:
| "It is difficult to get a man to understand something, when his
| salary depends upon his not understanding it,"
| kenjackson wrote:
| "Mathematician Thomas Bloom, who runs erdosproblems.com, pushed
| back right away. He called the statements "a dramatic
| misinterpretation," clarifying that "open" on his site just means
| he personally doesn't know the solution - not that the problem is
| actually unsolved."
|
| What mathematician uses this as the definition for "open"? I
| don't go around saying that most problems in this textbook are
| open questions, just because I don't know how to do them.
___________________________________________________________________
(page generated 2025-10-19 23:01 UTC)