A little off-topic but I have been thinking about how writing changes in the age of AI.
I wonder if similar to how many engineers now write 90% or maybe 99% of code with AI and primarily weigh in on the plan/design, will writers too primarily weigh in only on the content brief/plan and then the actual post is seldom read in full (once you have faith that the AI is not making up citations). Because the audience of most writing is now bots prose and style are no longer relevant.
> In cartography, a trap street is a fictitious entry in the form of a misrepresented street on a map, often outside the area the map nominally covers, for the purpose of "trapping" plagiarists of the map who, if caught, would be unable to explain the inclusion of the "trap street" on their map as innocent.
This is fascinating . I wonder what the mechanism was that punished someone who copied the map. Copyright laws? This appears to be an old strategy so im not sure thats the case.
Other than some sort of social punishment I don’t see the cost for the copier. For the original author there is a cost of making your map less accurate on purpose.
They're used by map makers to prevent other map makers from freeriding on the original work. They're less worried about personal copies and much more interested in deterring commercial reuse.
That doesn't answer my questions although I now realize I made a bad assumption. This is a modern technique. I misread the bit about Greece on the wikipedia article and somehow got it in my head that it was more ancient.
So I think to answer my question more directly, yes, it's just copyright laws.
My point, before understanding that, was that from the map consumer's perspective it doesnt make a difference. And from the original creator's perspective it has a little accuracy cost. So without an additional mechanism I didn't see the disincentive for copiers. So I wondered if it was some ancient form of copyright law or just strong sense of morals or what.
Deliberate traps are a modern 20th-century practice. In old times, each mapmaker made their own innocent mistakes, and those mistakes got copied into other mapmakers' work. In other words, historians can plot "family trees" of maps, and figure out who copied from whom, based on which errors appear on which maps and on what date. Apparently California drawn as an island lasted about a century because engravers copied each other.
There were a few factors discouraging mapmakers from blatantly copying each other:
Rulers granted printing monopolies from the late 1400s. A mapmaker like Ortelius would print "cum privilegio" (lol) on a map. If you copied it, your expensive copper plates could be seized, along with a hefty fine.
Spain and Portugal each had a master chart which were state secrets because they held navigation knowledge worth a fortune. Portugal handed out severe punishments for leaking charts. (Leaks still happened: the Cantino planisphere was smuggled from Lisbon to Italy in 1502.)
Booksellers' guilds kept registers of who owned which work, and enforced it.
And lastly, copying meant hand-engraving a new copper plate, which took months of work and real skill. The practical barriers alone got rid of most of the copiers, but it didn't stop the big houses.
(I used Claude to research this fascinating rabbit hole, so I'm basically paraphrasing here. But this comment is my own original writing, so treat it as you like.)
I'm reminded of a section of Cory Doctorow's latest non-fiction, The Reverse Centaur's Guide To Life After AI (it is of course an excellent read), where he discusses how the semantic web and having an internet set-up for agents and AI to use won't work:
>It also hasn't happened because there is a reward for cheating. Scammers will label their phone numbers as the customer service number for your cable operator or bank. Counterfeiters will label their batteries or phone cases as official accessories. It's hard enough to get people to correctly label their stuff—but fact-checking billions of labels created by millions of people, none of whom you know personally or whose identities you can verify? That's insurmountable.
There's more he says—the preceding paragraph is below—but that line "there is a reward for cheating" is something I keep thinking about. It's hard not to see in so many of the discussions of LLMs.
>For one thing, there are irreconcilable differences in the ways in which things should be described. A site run by transphobes isn't going to label every story about a trans woman as "a story about a woman," irrespective of what the standard says. Microsoft is never going to label Excel as equivalent to Apple's Numbers, Google Sheets, and LibreOffice's Calc. Hermès is never going to list a $40,000 handbag as being made of the same material and having the same capacity as a $30 leather handbag of the same size and shape.
Of course. But when you see text in the descriptor of a satchel on a satchel seller's site, you contextualize it as marketing. If you saw the same text in a textbook on the history, design, and manufacturing of satchels, you would contextualize it very differently.
When an LLM is the lens through which you view that text, you often don't have the context of where it came from, and the LLM is often really bad at filtering trustworthiness based on context when retrieving information to answer a query with.
I wouldn't go to a random site with a sketchy URL to get the customer service number for my xyz. I'd go to their official domain. Thus successfully navigating the treacherous waters scammers attempt to muddy.
Yea if you ever want to test how gullible even latest SOTA models are ask it for financial advice on a specific IPO and watch it vaccume up sell side nonsense and spew it out back to you as absolute fact.
How do you get the poison out of the water in the well? I understand the way this is supposed to work on the web, there are support channels to deal with this sort of thing. But if the deceit is baked into the model, what is your recourse?
Same as always: Develop a healthy sense of skepticism, cultivate a stable and diverse network of sources whose credibility you understand, don’t trust what you read, and certainly don’t trust synthesis unless you know how it’s made?
Of course, and I'm really not trying to move the goal posts here, but I agree that that's what (most) humans are going to do. But we're rapidly moving in a direction of agent-to-agent flow and "they" seem to lack the degree of discernment the average human does. They certainly don't have the same incentive structure we do to detect fraud. They happily regurgitate and build off of lies because, to them, the lies are not lies, and they can't be. They're just a pile of geometrically close labels to the model.
So “caveat emptor”? I get that’s always part of reality but we shouldn’t exactly steer into that skid IMO. It just shifts the burden to the rest of us and does nothing to address the root cause
It is, and always has been, the only possibility. It's up to every individual to check for themselves what is true and what is not, and to decide who to trust for things that they can't check.
The real lesson is this: trust in the source is the most important thing for 99.99% of what you read. You will sometimes read about facts that you can actually verify, but those are mostly useless in the grand scheme - except as a sanity check on the source. For everything else, you have to ultimately trust the source.
Now sure, sometimes you can get lucky and detect certain internal inconsistencies that prove that something can't be true. And to some extent, reading the same information from multiple sources you don't normally trust too much can be a slight signal that it might be true. But these are generally in the minority.
IMO, it's similar to what's happening with code. People have been shipping sloppy code and fake news forever. It's just that the cost of doing these things has fallen off a cliff.
Sure but the agents obfuscate the signals you might use to judge the authority/trustworthiness of search results. They get disinformation results, wrap it into a reasonably sounding report, present it to you as trustworthy. There are so many ways for bad actors to game the system
> Isn't all of this true for a human-used internet as well?
Yes, but if you read this "semantic" info directly from a site, you can judge the site and its potential biases, and decide for yourself "this sells Louis Vuitton, of course they will praise their handbags". Or to pick a more extreme example that nobody would ignore, if you somehow land on an extremist website spewing racist crap, your alarms will surely trigger by anything they say.
But through an LLM this gets whitewashed and presented as authoritative and "neutral", when it isn't. You might know not to trust the LLM either (if you're paying attention; we all have our unconscious biases) but I bet you most people will be caught off guard.
The primary purpose of Google is to stop that. You have 100% trust in Google results because of the effort they have put into cleaning the results you see.
Billions have been spent on it.
Try searching Bing sometime. First result for Firefox is a malware clone site (you’ll have to scroll past the malware ad to see it).
it seems like end stage of LLMs is an MCP economy in which Google and Apple will have a massive advantage in verified locations, businesses, identities and representation of services accurately.
The top 10 results were getting pretty bad even pre-LLM. I remember tons of poorly written articles near the top, just gaming the system with SEO. Everyone was paying some guy on Fiverr $5 an article to churn out hundreds of articles with little regard for accuracy.
1. This is a 2022 era picture that LLMs just suck up what's on the Internet and regurgitate it. It hasn't been true for a long time.
2. There's almost no one who has a stronger incentive to distinguish LLM generated text from non-generated LLM text. Training classifiers is pretty manageable.
The other day I asked Gemini to evaluate if a Reddit post was true. It told me it was definitely true because it had been verified by legitimate news agencies that had reported on the incident. These "legitimate" news agencies were an AI aggregator that just had a direct summary of the post with a disclaimer that it wasn't independently verified and a yahoo news "article" that was just the same AI summary. The AI summary included hallucinations that weren't even part of the original reddit post (like giving the OP a gender when no genders were mentioned).
>1. This is a 2022 era picture that LLMs just suck up what's on the Internet and regurgitate it. It hasn't been true for a long time.
When I ask ChatGPT certain questions, it very clearly shows me the sources it's searching through in the form of favicons, and they're always the favicons of sites that are in the first 5-10 results for a Google search query that would fit what I've asked the LLM. It will then cite those websites as it's source, with a direct link to the page that also shows up in an equivalent Google search. It does that daily.
Why do you believe that? The favicons seem like they’re generated by the UI, not the LLM.
On the other hand, I recently asked ChatGPT for some information about a group of people, and the headshots it put with each summary were very clearly not the people it was describing. So hallucinations do still occur even on the latest models, it just seems unlikely that the favicons would be LLM-generated.
>1. This is a 2022 era picture that LLMs just suck up what's on the Internet and regurgitate it. It hasn't been true for a long time
if anything it’s more true now, AI chatbots rarely use raw LLM output, instead they have CoT and use search tool calls. and the speed at which webpages are crawled is in the matter of hours. I posted something here on hn, then searched for it on google 1 hr later and its AI answered with my comment
there’s several companies right now making a killing of organic placement on chatbots similar to the SEO rush of early google
> 2) There's almost no one who has a stronger incentive to distinguish LLM generated text from non-generated LLM text. Training classifiers is pretty manageable
i think it’s pretty naive to think that if LLM companies would develop LLM detection they would use it to skip training on that content.
instead, they use it to train their output to be less detectable.
LLM detection is not solvable because it is adversarial, an arms race
I'm not sure you get my point.
Just ask ChatGPT, "What are the top 10 xxx," and see which sources it cites.
Oftentimes, it's complete garbage with LLM tells.
> 2. There's almost no one who has a stronger incentive to distinguish LLM generated text from non-generated LLM text.
There's a tension there: they want you to use AI for everything, and if they get what they want then new information will be embedded in LLM text.
> Training classifiers is pretty manageable.
Hopefully such things can be opened up, so the slop can be iterated on until it passes. It doesn't need to be perfect, just enough so more garbage gets through.
> There's a tension there: they want you to use AI for everything, and if they get what they want then new information will be embedded in LLM text.
In the long term, this is probably a real concern that they have to tackle. They'll have to move away from just classifying things as LLM generated or not, to attempting to judge the underlying quality, and that's not an easy problem.
> "Citogenesis" redirects here; not to be confused with cytogenesis.
I had to read that three times. I thought it was a self-referential joke at first. Turns out the name was a joke from an xkcd. Can't argue with that source in terms of authority.
Pre Google internet problem was not about the quality but about spam in headers and keywords, which were used by the pre-google search engines.
Google came and worked wonderfully for a year or two, until people learned to game page rank algorithm. Soon first google pages were just SEO spam and now half of the google is just spam, big corp websites, etc.
All of that precious stuff from small hobby websites about cool things to blogs that used to be wonderful read like https://www.joelonsoftware.com.
AI were not hurt that much by spam, I guess there were at least some curation done, the question is if this curation will work and for how long. It seems we are throwing the towel here as in case of search.
This problem is so bad that you can create a website just to review your own website so when LLMs want to pull out reviews for the website the fake reviews count
How can this be fixed? Nothing prevents you from publishing straight BS, and LLMs will use any site that doesn't have a garbage reputation as a valid third-party source.
Of course, LLMs already use Reddit, Facebook and co. as a source, so it doesn't really matter...
> How can this be fixed? Nothing prevents you from publishing straight BS, and LLMs will use any site that doesn't have a garbage reputation as a valid third-party source.
Personally, I hope it can't, or the fix is onerous and kneecaps a lot of the utility of AIs.
I wonder if we can even have some adversarial competition, where competing businesses try to get AIs to ingest bad data to pollute their competitors' decision-making.
For starters: lets get some fake math papers out there. Given proof-checking systems, maybe they won't totally stop OpenAI, but hopefully the bad math could at least drive up the cost and slow AI down.
I don't care where the bad data comes from. It's actually kinda good that some people will have a financial incentive to push it out and keep pushing.
> Bad for humans in general, good for hucksters and con artists. Net negative for humanity.
It's a sad state of affairs, but it's what has to be done. If we've got to burn the web down to save humanity from obsolescence, I'm for it.
The "net negative for humanity" was the attempt to build machines to replace human intellect. I'm not going to let any distaste for marketers or hucksters or con artists distract from that worse thing.
Ah, yes, arrogance of humans is unbounded. That you, humble reader are happier to swim in a sea of misinformation on the premise that you are smarter and faster at disambiguating bullshit from reality.
Furthermore, you are content to pretend that the rest of humanity is capable of doing this on a regular basis. And not just the top 10% of those who can read, interpret and weigh biases, but a solid portion of it.
It is a completely reasonable interpretation if you are an accelerationist living in the elite classes. In fact, perhaps, they are even counting on a majority of people thinking this is a fine environment.
I don't have the answers, but a lot of this is just a continuation of a very bad trend.
> Ah, yes, arrogance of humans is unbounded. That you, humble reader are happier to swim in a sea of misinformation on the premise that you are smarter and faster at disambiguating bullshit from reality.
I think you misunderstand me: I want people to shit in the sea until it stinks so bad that everyone gets out of the water.
The problem with this analogy is that we already literally shit in the actual sea so much people get sick and die sometimes and yet people are still getting in the sea.
Ok but I don't have a lot of faith that it will ever be turned back without violence because there are vested interests in keeping everyone swimming in it.
> Ok but I don't have a lot of faith that it will ever be turned back without violence because there are vested interests in keeping everyone swimming in it.
I'm not sure if we're talking about the same thing.
I think an acceptable good-but-not-perfect outcome it one where the "sea" (AI) stinks so bad that enough people get out that there's a viable life outside of it. The thing is people, individually and in aggregate, are lazy and competitive, so self-discipline isn't sufficient to sustain that life. There need to be problems that push people in that direction.
The web is foundational to AI, so that's a place where it's vulnerable.
Just to be clear, you're proposing that we poison the internet with misinformation - which is just as misleading to humans using traditional information retrieval tools as to AIs - until it's completely unusable, and this will be good because it's bad for AI, and everyone who is misled in the meantime is acceptable collateral damage?
Here I'm quoting from the website:
> It should go without saying that you should never publish fake content. While my experiment relied on tens of thousands of unverified stats, I ran it in order to share the results with the public, and the website, while still live, now carries a disclaimer.
"now carries a disclaimer" sure suggests it didn't, so "you should never publish fake content" really doesn't seem like a standard the author actually held themself to.
I'm very well aware of that. Unfortunately, this technology was invented, and the logic of our competitive society leaves little chance to contain it except scorched earth tactics.
Dude. The internet has been poisoned for a very long time. Remember Viagra? It used to be easy to call out the BS. But if you are outsourcing your thinking to a LLM, it’s much harder.
I've been thinking for awhile it is very strange that claude/ openai etc google "What was the inflation rate last quater" instead of just having a tool to query BLS datasets and that sort of thing.
So hopefully most of these statistics move towards first party sources.
I don't think it is up to us at this point. LLMs are now the lens ("filter for what people see"). I hope, they will fix this. I think that they will still rely on reasoning and better grounding, which should prevent straight lies. But I think two bigger problems here: 1. training data might have the BS which would steer reasoning; 2. grounding might still be limited by small number of sources.
It cannot be fixed. The most hilarious example of it is YouTube. They have a video summarisation feature, which automatically creates video summaries that then get fed into the search engine. The problem with those is that they are created using models that are good few months behind, and let's say someone published a video on Nikon ZR and the model used to create summaries does not know about Nikon ZR and inserts references to Nikon Z8 or Nikon Zf into the summaries. This decreases accuracy of YouTube search and negatively affect discoverability. It's the AI version of shitting into the well you drink from. Bon appetit!
People stop trusting what they read on the internet.
All alternates are just attempting to improve the baseline trustworthiness of random information from the internet (and/or LLM that's baby-birding it back to you). Any increase in how much people blindly trust random stuff from the internet only serves to increase the blast radius when it is wrong.
The fix that seems obvious to me is to only let the LLM see truthful (or at least non-malicious) data. The model trainers carefully curate the training data, and you don't give the LLM access to full-web search. Instead, you hook them up to an 'official' source like an encyclopedia, documentation for the software in question, etc.
It's a ton of effort and expense, but probably feasible from a technical point of view. So instead, the model providers will probably do something silly like wire up rules like "NYT=Reliable;my.blog.forbes.com=Unreliable" and they'll say the issue is solved.
Defer to authority and trust your government to rank what is truth and what is not, so your LLM always gives you the right answer. Nothing can go wrong here ever.
> Its made-up numbers have also ended up in an academic paper and in a bank's investment research.
It's an excellent way of showing how unreliable the information you find online is (and it will only get worse in the future), but willfully creating misinformation and made up shit and offering it as fact feels hostile to humanity.
Surely nobody got hurt as a consequence of those "facts", but bad decisions with real world impact could be made regardless. It feels like the behavior should be shunned in a general case (the same way I'd shun those numerous spam sites that pollute search results and very much would prefer they not be allowed to exist), even if in this case the intent was good.
Problem is, you can't really do anything about it, other than figuring out a way to take down hostile sites and keep them from popping up, so it's even worse than fraud or even just bad research in academia.
Part of the problem is also then the indiscriminate hoovering of any and all data by LLMs. If they kept their own trusted data sources instead of stealing from anyone and everything, then poisoned data wouldn't be an issue. The data poisoners are at fault, but so are the LLMs trusting anything and the companies thievery of human labor. Once the LLMs bring up this data, it's now on Anthropic and OpenAI for editorializing and saying "this is true".
Except for the fine print. LLMs are to be used for entertainment purposes. lol
> If they kept their own trusted data sources instead of stealing from anyone and everything, then poisoned data wouldn't be an issue.
If they didn't, they'd be unable to compete with their competition and also we'd get worse models that can't reason about many domains or do that much due to limited training data/generalization. I don't think we can put the cat back in the bag or mandate only non-problematic training codebases without shooting ourselves in the foot.
Now, if those AI orgs were mandated to properly compensate everyone whose data they've mined, to track which neurons get invoked and to pay accordingly based on what was used to train them... ehh, that's about as unrealistic as stopping. Kind of a bad situation all around, unless you abandon the idea of any sort of copyright.
Sucks to be them but they deserve this for what they did to train in the first place. Nothing good enough to outweigh the bad they will produce will come from LLMs.
I wonder if Googles products will eventually be the least vulnerable to this after spending so many years optimizing how they filter quality content from bad content.
The page demonstrates that Google specifically delisted them.
That said, I expect that OpenAI will eventually have its own search infrastructure that rivals Google's, and the overall system will get smarter. Whatever Google did to recognize this site as trash, OpenAI will do as well.
Reminds me of the people whining about Wikipedia. AI can know or care if the data is fake, it just needs to be programmed to cross check or rate the source. You can even tell it to do that before accepting the data.
These are not parallel situations. Wikipedia is far more accurate and consistent, and a lot of the whiners whine because their personal politics make it hard to square the information on the page.
Often real people don't either, plenty of fraud or selective reporting out there... but with AI we can also launder it so that the people who do care about fake data don't know any better.
You can prompt it to fact check. Getting tired of these slippery slope arguments that never pan out in the hopeless way people think (printing press/cotton gin/steam engine/computers/calculators/etc. will take all the jobs!!)... but I don't think it will ever stop, it seems inherent to human behavior or something.
"History shows that when a slope appears, regulation steps in, technology evolves to solve the problem, or the culture shifts to reinterpret the tool. In almost every case, the feared bottom of the slope was never reached because humans constantly built ramps or bridges along the way."
The fact that AI can be duped more easily than a human while sounding more authoritative and being entrusted with more and more critical tasks is really not something you should feel compelled to defend or relativize. It's a problem and the industry doesn't seem to have a good fix.
It can certainly make judgements on the data and how it was obtained. You just need to specify the kind of data you will accept (or this can be hard coded)
I thought everybody knew this was a problem. Scrapers vacuum up data but don't have any way tell what's true or false.
It's interesting to have a specific case where obviously false data is being spread, but there's nearly an infinite number of more subtle variations around.
falsifying statistics alone without the added stealing on top, was 3 years of labor camp in places. that was after they course corrected and stopped giving capital punishment for it.
good thing nobody falsified statistic just for the sake of it -- so being a sabotager and a foreign spy was always part of it too.
I wonder if similar to how many engineers now write 90% or maybe 99% of code with AI and primarily weigh in on the plan/design, will writers too primarily weigh in only on the content brief/plan and then the actual post is seldom read in full (once you have faith that the AI is not making up citations). Because the audience of most writing is now bots prose and style are no longer relevant.
https://en.wikipedia.org/wiki/Trap_street
Other than some sort of social punishment I don’t see the cost for the copier. For the original author there is a cost of making your map less accurate on purpose.
So I think to answer my question more directly, yes, it's just copyright laws.
My point, before understanding that, was that from the map consumer's perspective it doesnt make a difference. And from the original creator's perspective it has a little accuracy cost. So without an additional mechanism I didn't see the disincentive for copiers. So I wondered if it was some ancient form of copyright law or just strong sense of morals or what.
There were a few factors discouraging mapmakers from blatantly copying each other:
Rulers granted printing monopolies from the late 1400s. A mapmaker like Ortelius would print "cum privilegio" (lol) on a map. If you copied it, your expensive copper plates could be seized, along with a hefty fine.
Spain and Portugal each had a master chart which were state secrets because they held navigation knowledge worth a fortune. Portugal handed out severe punishments for leaking charts. (Leaks still happened: the Cantino planisphere was smuggled from Lisbon to Italy in 1502.)
Booksellers' guilds kept registers of who owned which work, and enforced it.
And lastly, copying meant hand-engraving a new copper plate, which took months of work and real skill. The practical barriers alone got rid of most of the copiers, but it didn't stop the big houses.
(I used Claude to research this fascinating rabbit hole, so I'm basically paraphrasing here. But this comment is my own original writing, so treat it as you like.)
>It also hasn't happened because there is a reward for cheating. Scammers will label their phone numbers as the customer service number for your cable operator or bank. Counterfeiters will label their batteries or phone cases as official accessories. It's hard enough to get people to correctly label their stuff—but fact-checking billions of labels created by millions of people, none of whom you know personally or whose identities you can verify? That's insurmountable.
There's more he says—the preceding paragraph is below—but that line "there is a reward for cheating" is something I keep thinking about. It's hard not to see in so many of the discussions of LLMs.
>For one thing, there are irreconcilable differences in the ways in which things should be described. A site run by transphobes isn't going to label every story about a trans woman as "a story about a woman," irrespective of what the standard says. Microsoft is never going to label Excel as equivalent to Apple's Numbers, Google Sheets, and LibreOffice's Calc. Hermès is never going to list a $40,000 handbag as being made of the same material and having the same capacity as a $30 leather handbag of the same size and shape.
When an LLM is the lens through which you view that text, you often don't have the context of where it came from, and the LLM is often really bad at filtering trustworthiness based on context when retrieving information to answer a query with.
I wouldn't go to a random site with a sketchy URL to get the customer service number for my xyz. I'd go to their official domain. Thus successfully navigating the treacherous waters scammers attempt to muddy.
I don't think the average human has a particularly great sense of discernment either
The real lesson is this: trust in the source is the most important thing for 99.99% of what you read. You will sometimes read about facts that you can actually verify, but those are mostly useless in the grand scheme - except as a sanity check on the source. For everything else, you have to ultimately trust the source.
Now sure, sometimes you can get lucky and detect certain internal inconsistencies that prove that something can't be true. And to some extent, reading the same information from multiple sources you don't normally trust too much can be a slight signal that it might be true. But these are generally in the minority.
Yes, but if you read this "semantic" info directly from a site, you can judge the site and its potential biases, and decide for yourself "this sells Louis Vuitton, of course they will praise their handbags". Or to pick a more extreme example that nobody would ignore, if you somehow land on an extremist website spewing racist crap, your alarms will surely trigger by anything they say.
But through an LLM this gets whitewashed and presented as authoritative and "neutral", when it isn't. You might know not to trust the LLM either (if you're paying attention; we all have our unconscious biases) but I bet you most people will be caught off guard.
Billions have been spent on it.
Try searching Bing sometime. First result for Firefox is a malware clone site (you’ll have to scroll past the malware ad to see it).
(I am not including AI results in this).
They now train on their own content.
Eating your own d̶o̶g̶ f̶o̶o̶d̶ poo.
Now, the model slips it in without any verification and casually builds on top of it.
1. This is a 2022 era picture that LLMs just suck up what's on the Internet and regurgitate it. It hasn't been true for a long time.
2. There's almost no one who has a stronger incentive to distinguish LLM generated text from non-generated LLM text. Training classifiers is pretty manageable.
The other day for me Google search AI cited triggerbites which is a vibe data website.
The LLM unless explicitly prompted cannot distinguish the validity of a data source.
When I ask ChatGPT certain questions, it very clearly shows me the sources it's searching through in the form of favicons, and they're always the favicons of sites that are in the first 5-10 results for a Google search query that would fit what I've asked the LLM. It will then cite those websites as it's source, with a direct link to the page that also shows up in an equivalent Google search. It does that daily.
On the other hand, I recently asked ChatGPT for some information about a group of people, and the headshots it put with each summary were very clearly not the people it was describing. So hallucinations do still occur even on the latest models, it just seems unlikely that the favicons would be LLM-generated.
if anything it’s more true now, AI chatbots rarely use raw LLM output, instead they have CoT and use search tool calls. and the speed at which webpages are crawled is in the matter of hours. I posted something here on hn, then searched for it on google 1 hr later and its AI answered with my comment
https://share.google/aimode/PkQUVfA68AlHGb7Yr
there’s several companies right now making a killing of organic placement on chatbots similar to the SEO rush of early google
> 2) There's almost no one who has a stronger incentive to distinguish LLM generated text from non-generated LLM text. Training classifiers is pretty manageable
i think it’s pretty naive to think that if LLM companies would develop LLM detection they would use it to skip training on that content.
instead, they use it to train their output to be less detectable.
LLM detection is not solvable because it is adversarial, an arms race
> They now train on their own content.
There's a tension there: they want you to use AI for everything, and if they get what they want then new information will be embedded in LLM text.
> Training classifiers is pretty manageable.
Hopefully such things can be opened up, so the slop can be iterated on until it passes. It doesn't need to be perfect, just enough so more garbage gets through.
In the long term, this is probably a real concern that they have to tackle. They'll have to move away from just classifying things as LLM generated or not, to attempting to judge the underlying quality, and that's not an easy problem.
> "Citogenesis" redirects here; not to be confused with cytogenesis.
I had to read that three times. I thought it was a self-referential joke at first. Turns out the name was a joke from an xkcd. Can't argue with that source in terms of authority.
Is this information on this website true/high quality?
We need a google for LLMs!
Oh, but LLMs don't consume ads.
We need ads for LLMs!
Help.
Google came and worked wonderfully for a year or two, until people learned to game page rank algorithm. Soon first google pages were just SEO spam and now half of the google is just spam, big corp websites, etc.
All of that precious stuff from small hobby websites about cool things to blogs that used to be wonderful read like https://www.joelonsoftware.com.
AI were not hurt that much by spam, I guess there were at least some curation done, the question is if this curation will work and for how long. It seems we are throwing the towel here as in case of search.
It’s hard to find and convict people who post actual illegal content online, much less fake reviews
How can this be fixed? Nothing prevents you from publishing straight BS, and LLMs will use any site that doesn't have a garbage reputation as a valid third-party source.
Of course, LLMs already use Reddit, Facebook and co. as a source, so it doesn't really matter...
As a human, this is fantastic.
> How can this be fixed? Nothing prevents you from publishing straight BS, and LLMs will use any site that doesn't have a garbage reputation as a valid third-party source.
Personally, I hope it can't, or the fix is onerous and kneecaps a lot of the utility of AIs.
I wonder if we can even have some adversarial competition, where competing businesses try to get AIs to ingest bad data to pollute their competitors' decision-making.
For starters: lets get some fake math papers out there. Given proof-checking systems, maybe they won't totally stop OpenAI, but hopefully the bad math could at least drive up the cost and slow AI down.
s/human/unscrupulous marketer/
Bad for humans in general, good for hucksters and con artists. Net negative for humanity.
> s/human/marketer/
I don't care where the bad data comes from. It's actually kinda good that some people will have a financial incentive to push it out and keep pushing.
> Bad for humans in general, good for hucksters and con artists. Net negative for humanity.
It's a sad state of affairs, but it's what has to be done. If we've got to burn the web down to save humanity from obsolescence, I'm for it.
The "net negative for humanity" was the attempt to build machines to replace human intellect. I'm not going to let any distaste for marketers or hucksters or con artists distract from that worse thing.
Why just web? Go straight for the powerplants.
Furthermore, you are content to pretend that the rest of humanity is capable of doing this on a regular basis. And not just the top 10% of those who can read, interpret and weigh biases, but a solid portion of it.
It is a completely reasonable interpretation if you are an accelerationist living in the elite classes. In fact, perhaps, they are even counting on a majority of people thinking this is a fine environment.
I don't have the answers, but a lot of this is just a continuation of a very bad trend.
I think you misunderstand me: I want people to shit in the sea until it stinks so bad that everyone gets out of the water.
I'm not sure if we're talking about the same thing.
I think an acceptable good-but-not-perfect outcome it one where the "sea" (AI) stinks so bad that enough people get out that there's a viable life outside of it. The thing is people, individually and in aggregate, are lazy and competitive, so self-discipline isn't sufficient to sustain that life. There need to be problems that push people in that direction.
The web is foundational to AI, so that's a place where it's vulnerable.
Here I'm quoting from the website:
> It should go without saying that you should never publish fake content. While my experiment relied on tens of thousands of unverified stats, I ran it in order to share the results with the public, and the website, while still live, now carries a disclaimer.
"now carries a disclaimer" sure suggests it didn't, so "you should never publish fake content" really doesn't seem like a standard the author actually held themself to.
Regardless of how the data is sourced, this is bad. It's poisoning the internet.
Besides, such issues come from a lack of due diligence, which have only been exacerbated by AI.
I'm very well aware of that. Unfortunately, this technology was invented, and the logic of our competitive society leaves little chance to contain it except scorched earth tactics.
So hopefully most of these statistics move towards first party sources.
People stop trusting what they read on the internet.
All alternates are just attempting to improve the baseline trustworthiness of random information from the internet (and/or LLM that's baby-birding it back to you). Any increase in how much people blindly trust random stuff from the internet only serves to increase the blast radius when it is wrong.
It's a ton of effort and expense, but probably feasible from a technical point of view. So instead, the model providers will probably do something silly like wire up rules like "NYT=Reliable;my.blog.forbes.com=Unreliable" and they'll say the issue is solved.
Defer to authority and trust your government to rank what is truth and what is not, so your LLM always gives you the right answer. Nothing can go wrong here ever.
It's an excellent way of showing how unreliable the information you find online is (and it will only get worse in the future), but willfully creating misinformation and made up shit and offering it as fact feels hostile to humanity.
Surely nobody got hurt as a consequence of those "facts", but bad decisions with real world impact could be made regardless. It feels like the behavior should be shunned in a general case (the same way I'd shun those numerous spam sites that pollute search results and very much would prefer they not be allowed to exist), even if in this case the intent was good.
Problem is, you can't really do anything about it, other than figuring out a way to take down hostile sites and keep them from popping up, so it's even worse than fraud or even just bad research in academia.
Except for the fine print. LLMs are to be used for entertainment purposes. lol
If they didn't, they'd be unable to compete with their competition and also we'd get worse models that can't reason about many domains or do that much due to limited training data/generalization. I don't think we can put the cat back in the bag or mandate only non-problematic training codebases without shooting ourselves in the foot.
Now, if those AI orgs were mandated to properly compensate everyone whose data they've mined, to track which neurons get invoked and to pay accordingly based on what was used to train them... ehh, that's about as unrealistic as stopping. Kind of a bad situation all around, unless you abandon the idea of any sort of copyright.
That said, I expect that OpenAI will eventually have its own search infrastructure that rivals Google's, and the overall system will get smarter. Whatever Google did to recognize this site as trash, OpenAI will do as well.
"History shows that when a slope appears, regulation steps in, technology evolves to solve the problem, or the culture shifts to reinterpret the tool. In almost every case, the feared bottom of the slope was never reached because humans constantly built ramps or bridges along the way."
It's interesting to have a specific case where obviously false data is being spread, but there's nearly an infinite number of more subtle variations around.
if you just lie and an llm or a newspaper or a kid believe you, at that point it isn’t a criticism of their naivete, you are just lying
similarly hacking through systems or picking locks isn’t a showcase of weakness if systems, it’s just being a hacker
good thing nobody falsified statistic just for the sake of it -- so being a sabotager and a foreign spy was always part of it too.