Why wordfreq will not be updated(github.com) |
Why wordfreq will not be updated(github.com) |
The web isn't dead, (Gen)AI, SEO, spam and pollution didn't kill anything.
The world is chaotic and net entropy (degree of disorder) of any isolated or closed system will always increase. Same goes for the web. We just have to embrace it and overcome the challenges that come with it.
1. Prove the human-ness of an author... 2. ...without grossly encroaching on their privacy. 3. Ensure that the author isn't passing off AI-generated material as their own.
We'll leave out the "don't let AI models train on my data" part for now.
Whatever solution we come up with, if any, will necessarily be mired in the politics of privacy, anonymity, and/or DRM. In any case, it's hard to conceive of a world where the human web returns as we once knew it.
If it was never really the case that you’d be better off for serious or improving reading having only the Web versus only access to a decent library, then we haven’t lost something so precious.
I mean, the most valuable site on the Web is probably a book & research paper piracy website. That’s its crowning achievement. Faster interlibrary loan, basically, but illegal.
2. I never said that things are the same as ever, quite the opposite actually. I am saying the world evolves constantly. It's naive to say company X/Y/Z killed something or made something unusable, when there is constant inevitable change. We should focus on how to move forward giving this constraint, and not dwell on times where the web was so much 'cleaner' and 'nicer', more manageable etc.
For some of us, it was 1994, the eternal September.
For some of us, it was when Aaron Swartz left us.
For some of us, it was when Google killed Google Reader (in hindsight, the turning point of Google becoming evil).
For some others, like the author of this post, it's when twitter and reddit closed their previously open APIs.
I think a decade or two ago, when most of the new tech being introduced (at least by our industry) started being unmistakably abusive and dehumanizing. When the recent past shows a strong trend, it's not unreasonable to expect the the near future will continue that trend. Particularly when it makes companies money.
That’s neither fair nor accurate. That slop is ultimately generated by the humans who run those models; they are attempting (perhaps poorly) to communicate something.
> two companies that I already despise
Life’s too short to go through it hating others.
> it's very likely because they are creating a plagiarism machine that will claim your words as its own
That begs the question. Plagiarism has a particular definition. It is not at all clear that a machine learning from text should be treated any differently from a human being learning from text: i.e., duplicating exact phrases or failing to credit ideas may in some circumstances be plagiarism, but no-one is required to append a statement crediting every text he has ever read to every document he ever writes.
Credits: every document I have ever read grin
This kind of AI slop is quite literally written by no one (an algorithm pushed it out), and it doesn't communicate anything since communication first requires some level of understanding of the source material - and LLM's are just predicting the likely next token without understanding. I would also extend this to AI slop written by someone with a limited domain understanding, they themselves have nothing new to offer, nor the expertise or experience to ensure the AI is producing valuable content.
I would go even further and say it's "read by no one" - people are sick and tired of reading the next AI slop article on google and add stuff like "reddit" to the end of their queries to limit the amount of garbage they get.
Sure there are people using LLMs to enhance their research, but a vast, vast majority are using it to create slop that hits a word limit.
Given that LLMs and human creativity work on fundamentally different principles, there is every reason to believe there is a difference.
The issue with generative 'AI' isn't that they generate text, it's that they can (and are) used to generate high-volume low-cost nonsense at a scale no human could ever achieve without them.
> Life’s too short to go through it hating others
Only when they don't deserve it. I have my doubts about Google, but I've no love for OpenAI.
> Plagiarism has a particular definition ... no-one is required to append a statement crediting every text he has ever read
Of course they aren't, because we rightly treat humans learning to communicate differently from training computer code to predict words in a sentence and pass it off as natural language with intent behind it. Musicians usually pay royalties to those whose songs they sample, but authors don't pay royalties to other authors whose work inspired them to construct their own stories maybe using similar concepts. There's a line there somewhere; falsely equating plagiarism and inspiration (or natural language learning in humans) misses the point.
And yet we're filled to the gills with Luddite sentiments and AI content fearmongering.
Imagine the hysteria and the skull-vibrating noise of the non-HN rabble when they come to understand where all of this is going. They're going to do their darndest to stop us from achieving post-economy.
Which isn’t AIs fault. I’m sure they can be great in cancer detection, unless they replace what we’re already doing because they are cheaper than doctors. In combination with an expert AI is great, but that’s not what’s happening is it?
Anyway in the long run AI will kill tons of jobs. Regardless of blog posts like that. The true key is governments assistance.
The dependency on closed data combined with the cost of compute to do anything interesting with LLMs has made individual contributions to NLP research extremely difficult if one is not associated with a very large tech company. It's super unfortunate, makes the subject area much less approachable, and makes the people doing research in the subject area much more homogeneous.
It's already happening. There is a growing number of groups that are forming their own "private internets" that is separated from the internet-at-large, precisely because the internet at large is becoming increasingly useless for a whole lot of valuable things.
>Reddit also stopped providing public data archives, and now they sell their archives at a price that only OpenAI will pay.
>And given what's happening to the field, I don't blame them."
What beautiful doublethink.
Given just how many AI bots scrape up everything they can, oftentimes ignoring robots.txt or any rate limits (there have been a few complaint threads on HN about that), I can hardly blame the operators of large online services just cutting off data feeds.
Twitter however didn't stop their data feeds due to AI or because they wanted money, they stopped providing them because its new owner does everything he can to hinder researchers specializing in propaganda campaigns or public scrutiny.
In other words, why is it wrong for X but okay for Reddit? If you ignore one individual’s politics, the two services did the same thing.
God I hate this dystopic timeline we live in.
And now a hopefully new comment: having a word frequency measure of the internet as we're going into AI being more used would be IMMENSELY useful specifically _because_ more of the internet is being AI generated! I could see such a dataset being immensely useful to researchers who are looking for the impacts of AI on language, and to test empirically a lot of claims the author has made in this very post! What a shame that they stopped measuring.
Also: as to the claims that AI will cause stagnation and a reduction of the variance of English vocabulary used, this is a trend in English that's been happening for over 100 years ( https://evoandproud.blogspot.com/2019/09/why-is-vocabulary-s... ). I believe the opposite will happen, AI will increase the average persons vocabulary, since chat AIs tends to be more professionally written than a lot of the internet. It's like being able to chat with someone that has an infinite vocabulary. It also makes it possible for people to read complicated documents well out of their domain, since they can ask not just for definitions but more in depth explanations of what words/sections mean.
Here's to a comment that will never be read because of all the noise in this thread :/
Sound familiar to anyone?
On the level of meta-discourse you seem to want to also speak to: Dang even when people have the Official Corporate Approved Perspective (in particular, the claim that it's "like being able to chat with someone that has an infinite vocabulary" is probably the silliest delusional AI hype I've heard all week) and the most upvotes in the thread they still think they're an embattled ideological minority. Starting to think that literally zero people in the modern world don't have or affect a victim complex of some kind
But speaking of healthy disagreement, as to "chatting with someone that has an infinite vocabulary", I'd love to hear any counterarguments you might have; or was calling it "silly and delusional" meant to be your argument? :P I think it's a pretty uncontroversial statement seeing as eg ChatGPT very likely knows every word in the English language.
I understand the position.
The only exception would be China, but the GFW is probably not helping.
LLV might lower the cost of that so much that it will become more interesting to do so.
Furthermore, while X had also only announced this in February, Reddit announced their API shutdown just 2 months later in April.
And, to further add to that, X was pretty upfront that they think they have access to a large and powerful dataset in X and didn't want to give it out for free. Reddit used very similar wording when announcing their changes.
And also agreed: many trumpet the merits of "unassisted" human output. However, they're suffering from ancestor veneration: human writing has always been a vast mine of worthless rock (slop) with a few gems of high-IQ analysis hidden here and there.
For instance, upon the invention of the printing press, it was immediately and predominantly used for promulgating religious tracts.
And even when you got to Newton, who created for us some valuable gems, much of his output was nevertheless deranged and worthless. [1]
It follows that, whether we're a human or an LLM, if we achieve factual grounding and the capacity to reason, we achieve it despite the bulk of the information we ingest. Filtering out sludge is part of the required skillset for intellectual growth, and LLM slop qualitatively changes nothing.
[1] https://www.newtonproject.ox.ac.uk/view/texts/diplomatic/THE...
About your last point sometimes I think that in the future there will be models specifically distilling the climax of selected thinkers, so that not only their production will be preserved but maybe something more that is only implicitly contained in their output.
Anecdotally, I met success in prompting GPT-3 to "mimic Stephen Pinker" when solving logical puzzles. Puzzles that it would initially fail, it would succeed attempting to mimic his language. GPT-3 seemed to have grokked the pattern of how Stephen Pinker thinks through problems, and it could leverage those patterns to improve its own reasoning. OpenAI o1 needs no such assistance, and I expect that o2 will fully supplant humans with its ability to reason.
It follows that all that we have to offer with our brightest minds will be exhausted, and we will be eclipsed in every conceivable way by our creation. It will mark the end of the Anthropocene; something that likely exceeds the headiest of Nick Bostom speculations will take its place.
It seems that this is coming in 2026 if not sooner, and Alignment is the only thing that ought occupy our minds: the question of whether we're creating something that will save us from ourselves, or whether all that we've built will culminate in something gross and final.
Looking around myself, however, I see impassioned "discourse" about immigration. The merits of DEI. Patriotism. Transgenderism. Religion. Copyright. Vast herds of dinosaurs preying upon one another, giving only idle attention to the glowing object in the sky. Is it an asteroid? Is it a UFO that is coming down to provide dinosaur healthcare? Nope, not even that level of thought is mustered. With 8 billion people on the planet, Utopia by Nick Bostrom hasn't even mustered 100 reviews on Amazon. On the advent of the defining moment of the universe itself, when virtually all that is imaginable is unlocked for us, our species' heads remains buried in the mud, gnawing at one another's filthy toes, and I'm alienated and disgusted.
The only glints of beauty I see in my fellow man are in those with minds which exceed a certain IQ threshold and cognitive flexibility, as well as in lesser minds which exhibit gentleness and humility. There is beauty there, and there is beauty in the staggering possibility of the universe itself. The rest is at best entomology, and I won't mourn its passing.
There are charts / graphs in the link, both since 2021, and since earlier.
The final graph suggests the phenomenon started earlier, possibly correlated in some way to Malaysian / Indian usages of English.
It does seem OpenAI's family of GPTs as implemented in ChatGPT unspool concepts in a blend of India-based-consultancy English with American freshmen essay structure, frosted with superficially approachable or upbeat blogger prose ingratiatingly selling you something.
Anthropic has clearly made efforts to steer this differently, Mistral and Meta as well but to a lesser degree.
I've wondered if this reflects training material (the SEO is ruining the Internet theory), or is more simply explained by selection of pools of Hs hired for RLHF.
> As one example, Philip Shapira reports that ChatGPT (OpenAI's popular brand of generative language model circa 2024) is obsessed with the word "delve" in a way that people never have been, and caused its overall frequency to increase by an order of magnitude.
This will surely affect how we speak. It's possible that human language evolution could come to a halt, stuck in time as AI datasets stop being updated.
In the worst case, we will see a global "model collapse" with human languages devolving along with AI's, if future AIs are trained on their own outputs...
https://news.ycombinator.com/item?id=34966335
We will all get used to it.
It seems however it started increasing most in usage just these last few months, maybe people are talking more about "delve" specifically because of the increase in usage? A usage recursion of some sorts.
But also, words and phrases do become popular among humans, right? It would be a shame if AI caused the language to get more stagnant, as keeping up with which phrases are popular get you labeled as an AI.
And language in general stagnates and shrinks in vocabulary over time ( https://evoandproud.blogspot.com/2019/09/why-is-vocabulary-s... ). (Link that ChatGPT helped me find :P) I think AI will increase the average persons vocabulary, since it appears to in general be better/more professionally written than a lot of what the average person is exposed to online.
The funny fact: It doesn't result in the increase for search results for "delve".
Someone should start scanning all those microfiche archives in local libraries and sell the data.
[1] Or maybe Sheckley or Lem, now that I think about it.
...and then I realized you wrote 3 books a day. What the hell.
This is a self-inflicted problem, IMO.
Do you just have shitty friends that share all that crap? Or are you following shitty pages?
I use Facebook a decent amount, and I don't suffer from what you're complaining about. Your feed is made of what you make it. Unfollow the pages that make that crap. If you have friends that share it, consider unfriending or at the very least, unfollowing. Or just block the specific pages they're sharing posts from.
'OpenAI and Google can collect their own damn data. I hope they have to pay a very high price for it, and I hope they're constantly cursing the mess that they made themselves.'
really does betray some real naivete. OpenAI and Google could literally burn $10million dollars per day (okay, maybe not OpenAI - but Google surely could) and reasonably fail to notice. Whatever costs those companies have to pay to collect training data will be well worth it to them. Any messes made in the course of obtaining that data will be dealt with by an army of employees either manually cleaning up the data, or by algorithms Google has its own LLM write for itself.
I do find the general sense of impending dystopian inhumanity arising out of the explosion of LLMs to be super fascinating (and completely understandable).
Maybe this is because I’m European, but what is partisan about calling X invariably worthless drivel? Seems a lot like facts to me considering what has been going on with the platform moderation since Elon Musk bought it. It’s so bad that the EU consider it a platform for misinformation these days.
Can you define "misinformation"? Is it just things the government disagrees with?
I then changed course. Why? I had read increasing reports of human e-book pirates (copying your book's content then repackaging it for sale under a diff title, byline, cover, and possibly at a much lower or even much higher price.)
And then the rise of LLMs and their ravenous training ingest bots -- plagiarism at scale and potentially even easier to disguise.
"Not gonna happen." - Bush Sr., via Dana Carvey
Now I keep the bulk of my book material non-public during dev. I'm sure I'll share a chapter candidate or so at some point before final release, for feedback and publicity. But the bulk will debut all together at once, and only once polished and behind a paywall
Two of the languages we support, Serbian and Chinese, are written in multiple scripts. To avoid spurious differences in word frequencies, we automatically transliterate the characters in these languages when looking up their words.
Serbian text written in Cyrillic letters is automatically converted to Latin letters, using standard Serbian transliteration, when the requested language is sr or sh."
I'd support keeping both scripts (српска ћирилица and latin script) , similarly to hiragana (ひらがな) and katakana (カタカナ) in Japanese.
> The field I know as "natural language processing" is hard to find these days. It's all being devoured by generative AI. Other techniques still exist but generative AI sucks up all the air in the room and gets all the money.
Traditional NLP has been surpassed by transformers, making this project obsolete. The rest of the post reads like rationalization and sour grapes.
> I don't think anyone has reliable information about post-2021 language usage by humans.
It's information about language usage by humans. We know the rate at which generated text has increased after 2021. How do we filter this to only have data from humans?
The bottom is just lamenting what's happening in the field (which is pretty much what everyone that's been doing anything with NLP research is also complaining about behind closed doors).
How sure can we be about that?
The conclusion I arrived at was that making my own crawler actually is feasible (and given my goals, necessary!) because I'm only interested in a very, very small fraction of what's out there.
The case for ChatGPT doing significant coinage that sticks isn't particularly strong either, partially from theory and partially because I'd think I'd've heard a lot of complaints about it by now, and the ones on hackernews would be repetitive to the point of seeming unavoidable (we agree on that for sure)
Anyway, re: the silliest hype I've heard all week, I'm mostly just trying to find humor in what has been a pretty bad hype wave for someone who's pathologically bad at sounding like the kind of nontechnical hype guys that pervade any tech hype wave but is nonetheless mostly seeking out jobs in this field because it's what my expertise is in. Incredibly awful job market for a lot of people I realize, but it feels like a special hell I get for getting into ML research before it was (quite so) cool. I'm trying to fight the negativity but I've gotten screwed over a lot lately, but I don't have anything against you personally for being silly on hn
Ah mate sorry to hear that, the market is tough right now. I will say objectively I believe there's very little in my comment that's hype-y. I think using AI while reading documents out of your comfort zone, and asking it questions can expand your vocabulary. I've personally tried it, it's helped me read papers not in my field, it's helped me find papers for better research. I can understand how someone can disagree with that, but calling it hype sounds to me more like a response to an invisible enemy/to "all the ones who hyped before" than to an actual concrete response to this specific case. And I think that mentality could put you in a potential catch-22 mental loop that will leave you constantly dissatisfied with anything AI or ML, by constantly seeing this invisible enemy where it might not be present. Anyways, stay positive and best of luck with the job hunt!
Edit: and it looks like my comment has now fallen deep into the depths of the comment thread, never to be heard from again! See, I told you I was an embattled ideological minority ;)
As far as my job hunt goes, I'm not finding myself rejecting positions out of disappointment, but noticing that I'm often rejected by C-suite people after being technically vetted, often after having what I believe was a pleasant, positive, and often even generative conversation about what the company's plans with AI are and how I might help accomplish them. As I said before, I really do try to stay positive, and I think when putting my best foot forward, like in a job interview, I tend to succeed at that, but my experience leads me to be more negative about this moment in the industry when I am more candid, such as in this forum. I think if you're going out for work in whatever the tech bubble du jour is, the people you're talking to have really different biases and expectations from those hiring for more "boring" development jobs, in a way that makes me want to just go out for more general roles and lie low for a while, except that since I've been working on primarily ML-related projects for most of the last decade, it's also hard to convince people not in that area that I have adequate relevant experience. In this context and with bills to pay, it's hard to stay optimistic
It also made the web a less than ideal source for training. And yet LLMs were still fed articles written for Googlebot, not humans. ML/LLM is the second iteration of writing pollution. The first was humans writing for corporate bots, not other humans.
Blog spam was generally written by humans. While it sucked for other reasons, it seemed fine for measuring basic word frequencies in human-written text. The frequencies are probably biased in some ways, but this is true for most text. A textbook on carburetor maintenance is going to have the word "carburetor" at way above the baseline. As long as you have a healthy mix of varied books, news articles, and blogs, you're fine.
In contrast, LLM content is just a serpent eating its own tail - you're trying to build a statistical model of word distribution off the output of a (more sophisticated) model of word distribution.
SEO text carefully tuned to tf-idf metrics and keyword stuffed to them empirically determined threshold Google just allows should have unnatural word frequencies.
LLM content should just enhance and cement the status quo word frequencies.
Outliers like the word "delve" could just be sentinels, carefully placed like trap streets on a map.
GOGI.
For a while I thought email as a medium was doomed, but spammers mostly lost that arms race. One interesting difference is that with spam, the large tech companies were basically all fighting against it. But here, many of the large tech companies are either providing tools to spammers (LLMs) or actively encouraging spammy behaviors (by integrating LLMs in ways that encourage people to send out text that they didn't write).
At least in Googles case, they're having so much difficulty keeping AI slop out of their search results that I don't have much faith in their ability to give it an appropriately low training weight. They're not even filtering the comically low-hanging fruit like those YouTube channels which post a new "product review" every 10 minutes, with an AI generated thumbnail and AI voice reading an AI script that was never graced by human eyes before being shat out onto the internet, and is of course always a glowing recommendation since the point is to get the viewer to click an affiliate link.
Google has been playing the SEO cat and mouse game forever, so can startups with a fraction of the experience be expected to do any better at filtering the noise out of fresh web scrapes?
The top of search results is consistently crowded by pages that obviously game ranking metrics instead of offering any value to humans.
I don't see how Google's SEO rules being written or unwritten has any bearing. Spammers will always find a way.
Based on the process above, naturally, the third iteration then is LLMs writing for corporate bots, neither for humans nor for other LLMs.
How do we know what content LLMs were fed? Isn't that a highly guarded secret?
Won't the quality of the content be paramount to the quality of the generated output or does it not work that way?
The complaint about pollution of the Web with artificial content is timely, and it's not even the first time due to spam farms intended to game PageRank, among other nonsense. This may just mean there is new value in hand-curated lists of high-quality Web sites (some people use the term "small Web").
Each generation of the Web needs techniques to overcome its particular generation of adversarial mechanisms, and the current Web stage is no exception.
When Eric Arthur Blair wrote 1984 (under his pen name "George Orwell"), he anticipated people consuming auto-generated content to keep the masses from away from critical thinking. This is now happening (he even anticipated auto-generated porn in the novel), but the technologies criticized can also be used for good, and that is what I try to do in my NLP research team. Good will prevail in the end.
As a random example: just trying to find a particular popular set of wireless earbuds takes me at least 10 minutes, when I already know the company, the company's website, other vendors that sell the company's goods, etc. It's just buried under tons of dreck. And my laptop is "old" (an 8-core i7 processor with 16GB of RAM) so it struggles to push through graphics-intense "modern" websites like the vendor's. Their old website was plain and worked great, letting me quickly search through their products and quickly purchase them. Last night I literally struggled to add things to cart and check out; it was actually harrowing.
Fuck the web, fuck web browsers, web design, SEO, searching, advertising, and all the schlock that comes with it. I'm done. If I can in any way purchase something without the web, I'mma do that. I don't hate technology (entirely...) but the web is just a rotten egg now.
We've been past the tipping point when it comes to text for some time, but for video I feel we are living through the watershed moment right now.
Especially smaller children don't have a good intuition on what is real and what is not. When I get asked if the person in a video is real, I still feel pretty confident to answer but I get less and less confident every day.
The technology is certainly there, but the majority of video content is still not affected by it. I expect this to change very soon.
Fair and accurate. In the best cases the person running the model didn't write this stuff and word salad doesn't communicate whatever they meant to say. In many cases though, content is simply pumped out for SEO with no intention of being valuable to anyone.
On the one hand, I completely agree with Robyn Speer. The open web is dead, and the web is in a really sad state. The other day I decided to publish my personal blog on gopher. Just cause, there's a lot less crap on gopher (and no, gopher is not the answer).
But...
A couple of weeks ago, I had to send a video file to my wife's grandfather, who is 97, lives in another country, and doesn't use computers or mobile phones. Eventually we determined that he has a DVD player, so I turned to x264 to convert this modern 4K HDR video into a form that can be played by any ancient DVD player, while preserving as much visual fidelity as possible.
The thing about x264 is, it doesn't have any docs. Unlike x265 which had a corporate sponsor who could spend money on writing proper docs, x264 was basically developed through trial and error by members of the doom9 forum. There are hundreds of obscure flags, some of which now operate differently to what they did 20 years ago. I could spend hours going through dozens of 20 year old threads on doom9 to figure out what each flag did, or I could do what I did and ask a LLM (in this case Claude).
Claude wasn't perfect. It mixed up a few ffmpeg flags with x264 ones (easy mistake), but combined with some old fashioned searching and some trial and error, I could get the job done in about half an hour. I was quite happy with the quality of the end product, and the video did play on that very old DVD player.
Back in pre-LLM days, it's not like I would have hired a x264 expert to do this job for me. I would have either had to spend hours more on this task, or more likely, this 97 year old man would never have seen his great granddaughter's dance, which apparently brought a massive smile to his face.
Like everything before them, LLMs are just tools. Neither inherently good nor bad. It's what we do with them and how we use them that matters.
Didn't most DVD burning software include video transcoding as a standard feature? Back in the day, you'd have used Nero Burning ROM, or Handbrake - granted, the quality may not have been optimized to your standards, but the result would have been a watchable video (especially to 97 year-old eyes)
In my opinion the internet can be considered as the equivalent of a natural environment like the earth. it's a space where people share, meet, talk, etc.
I find it astonishing that after polluting our natural environment we know polluted the internet.
(I initially wanted to say 'paid for by the government' but that'd be socialising losses and we've had quite enough of that in the past.)
>> Generative AI has polluted the data
Just like low-background steel marks the break in history from before and after the nuclear age, these types of data mark the distinction from before and after AI.
Future models will begin to continue to amplify certain statistical properties from their training, that amplified data will continue to pollute the public space from which future training data is drawn. Meanwhile certain low-frequency data will be selected by these models less and less and will become suppressed and possibly eliminated. We know from classic NLP techniques that low frequency words are often among the highest in information content and descriptive power.
Bitrot will continue to act as the agent of Entropy further reducing pre-AI datasets.
These feedback loops will persist, language will be ground down, neologisms will be prevented and...society, no longer with the mental tools to describe changing circumstances; new thoughts unable to be realized, will cease to advance and then regress.
Soon there will be no new low frequency ideas being removed from the data, only old low frequency ideas. Language's descriptive power is further eliminated and only the AIs seem able to produce anything that might represent the shadow of novelty. But it ends when the machines can only produce unintelligible pages of particles and articles, language is lost, civilization is lost when we no longer know what to call its downfall.
The glimmer of hope is that humanity figured out how to rise from the dreamstate of the world of animals once. Future humans will be able to climb from the ashes again. There used to be a word, the name of a bird, that encoded this ability to die and return again, but that name is already lost to the machines that will take our tongues.
But we do know that now it's a lot more, with a big LOT.
Many of my searches nowadays include suffixes like "site:reddit.com" (or similar havens of, hopefully, still mostly human-generated content) to produce reasonably useful results. There's so much spam pollution by sites like Medium.com that it's disheartening. It feels as if the Internet humanity is already on the retreat into their last comely homes, which are more closed than open to the outside.
On the positive side:
1. Self-managed blogs (like: not on Substack or Medium) by individuals have become a strong indicator for interesting content. If the blog runs on Hugo, Zola, Astro, you-name-it, there's hope.
2. As a result of (1), I have started to use an RSS reader again. Who would have thought!
I am still torn about what to make of Discord. On the one hand, the closed-by-design nature of the thousands of Discord servers, where content is locked in forever without a chance of being indexed by a search engine, has many downsides in my opinion. On the other hand, the servers I do frequent are populated by humans, not content-generating bots camouflaged as users.
Maybe we actually need to preserve all the old movies / documentaries / books in all languages and mark them as pre-LLM / non-LLM.
But I hazard a guess this wont happen, as its a common good that could only be funded by left-leaning taxation policies - no one can make money doing this, unlike burning carbon chains to power LLMs.
As mentioned, we have heuristics like frequency of the word "delve", and simple techniques such as measuring perplexity. I'd like to see a GAN style approach to this problem. It could potentially help improve the "humanness" of AI-generated content.
It's actually not. It's rather difficult for humans as well. We can see verbose text that is confused and call it AI, but it could just be a human aswell.
To borrow an older model training method, "Generative adversarial network". If we can distinguish AI from humans... We can use it to improve AI and close the gap.
So, it becomes an arms race that constantly evolves.
If we add linguistics to NLP I can see an argument but if we define NLP as the research of enabling a computer process language then it seems to me that LLM’s/ Generative AI is the only research that an NLP practitioner should focus on and everything else is moot. Is there any other paradigm that we think can enable a computer understand language other than training a large deep learning model on a lot of data?
compare the frequency of words to those used in human natural writings and you spot the computer from the human.
Brains aren't nearly as good at slightly adjusting the statistical properties of a text corpus as computers are.
Hmm I don’t disagree but I think it will be valuable skill going forward to write text that doesn’t read like it was written by an LLM
This is an arms race that I’m not sure we can win though. It’s almost like a GAN.
But that's a losing endeavor: if you can do that, you can immediately ask your LLM to fix its output so that it passes that test (and many others). It can introduce typos, make small errors on purpose, and anything you can think of to make it look human.
I'm sure this has occurred to them already. Apart from the near-impossibility of continuing the task in the same way they've always done it, it seems like the other reason they're not updating wordfreq is to stick a thumb in the eye of OpenAI and Google. While I appreciate the sentiment, I recognize that those corporations' eyes will never be sufficiently thumbed to satisfy anybody, so I would not let that anger change the course of my life's work, personally.
I like this.
Maybe even take it a step further - have a badge on the source that is both human and machine visible to indicate that the content is not AI generated.
Or potentially even more dystopian would be that AI slop would be dictating/driving human communication going forward.
Might even change the tool name.
I guess the same way the scientists had to account for the bomb pulse in order to provide accurate carbon-14 dating, wordfreq would need a magic way to account for non human content.
Saying magic, because unfortunately it was much easier to detect nuclear testing in the atmosphere than to it will be to detect AI-generated content.
In case that doesn't get my comment completely buried, I will go ahead and say honestly that even though "AI slop" and paywalled content is a problem, I don't think that generative AI in itself is a negative at all. And I also think that part of this person's reaction is that LLMs have made previous NLP techniques, such a those based on simple usage counts etc., largely irrelevant.
What was/is wordfreq used for, and can those tasks not actually be done more effectively with a cutting edge language model of some sort these days? Maybe even a really small one for some things.
I certainly have not encountered enough straight drivel where I would think it would have a significant effect on overall word statistics.
I suspect there may be some over-identification of AI content happening, a sort of Baader–Meinhof effect cognitive bias. People have their eye out for it and suddenly everything that reads a little weird logically "must be AI generated" and isn't just a bad human writer.
Maybe I am biased, about a decade ago I worked for an SEO company with a team of copywriters who pumped out mountains the most inane keyword packed text designed for literally no one but Google to read. It would rot your brain if you tried, and it was written by hand by a team of humans beings. This existed WELL before generative AI.
How confident are you in this assessment?
> straight drivel
We're past the point where what AI generates is "straight drivel"; every minute, it's harder to distinguish AI output from actual output unless you're approaching expertise in the subject being written about.
> a team of copywriters who pumped out mountains the most inane keyword packed text designed for literally no one but Google to read.
And now a machine can generate the same amount of output in 30 seconds. Scale matters.
So, then what really is the problem with just including LLM-generated text in wordfreq?
If quirky word distributions will remain a "problem", then I'd bet that human distributions for those words will follow shortly after (people are very quick to change their speech based on their environment, it's why language can change so quickly).
Why not just own the fact that LLMs are going to be affecting our speech?
2017: Invention of transformer architecture
June 2018: GPT-1
February 2019: GPT-2
June 2020: GPT-3
March 2022: GPT-3.5
November 2022: ChatGPT
The new stuff generated does (and this is honestly already captured).
This author doesn't generate content. They analyze data from humans. That "from humans" is the part that can't be discerned enough and thus the project can't continue.
Their research and projects are great.
Making resources like wordfreq more visible won't exacerbate any of these concerns.
See a lot of people upset about AI still using AI image generation because it's not in their field so they feel less strongly about it and can't create art themselves anyway, hypocritical either use it or don't but don't fuss over it then use it for something thats convenient for you.
Another example is how data on humans after 2020 or so can't be separated by sex because gender activists fought to stop recording sex in statistics on crime, medicine, etc.
Edit: just the first one
Every content system seems to get polluted by noise once it hits mainstream usage: IRC, Usenet, reddit, Facebook, geocities, Yahoo, webrings, etc. Once-small curated selections eventually grow big enough to become victims of their own successes and taken over by spam.
It's always an arms race of quality vs quantity, and eventually the curators can't keep up with the sheer volume anymore.
You ask on HN, one of the highest quality sites I've ever visited in any age of the Internet.
IRC is still alive and well among pretty much the same audience as always. I'm not sure it's fair to compare that with the others.
1. build a userbase, free product
2. once userbase get big enough, any new account requires a monthly fee, maybe $1
3. keep raising the fee higher and higher, until you get to the point that the userbase is manageable.
no ads, simple.
The people who stay away from critical thinking were doing that already and will continue to do so, 'AI' content or not.
Even if, this is a dangerous thought that discourages decisive action that is likely to be necessary for this to happen.
Sci-fi author:
I created the Torment Nexus to serve as a cautionary tale...
Tech Company:
Alas, we have created the Torment Nexus from the classic Sci-fi novel "Don't Create the Torment Nexus"
1. https://www.marxists.org/archive/marx/works/1894-c3/ch25.htm
This is going to be the thing that makes me quit Amazon. If I'm missing something and there's still a way to to a direct search, please tell me.
Product page (copy the identifier at the end): https://www.amazon.com/Long-Thanks-Hitchhikers-Guide-Galaxy-...
Review page (paste the identifier at the end): https://www.amazon.com/product-reviews/B001OF5F1E/
This seems to bypass all of the LLM stuff for now.
I used to be able to say search for Trek bike derailleur hanger and the first result would be what I wanted. Now I have to scroll past 5 ads to buy a new bike, one that's a broken link to a third party, and if I'm really lucky, at the bottom of page 1 will be the link to that part's page.
The shitification of the web is real.
(The Agner Fog of cycling?)
LEEZWOO 15.6" Laptop - 16GB RAM 512GB SSD PC Laptop, Quad-Core N95 Processor Up to 3.1GHz, Laptop Computers with Touch ID, WiFi, BT4.2, for Students/Business
Name rolls off the tongue doesn’t it
On the other hand, what you call "The Web" seems to be just what you can get at through search engines. There's still the old web, the thing that's mediated by relationships and reputation rather than aggregation services with billions of users. Like the link I shared above. Or this heroically moderated site we're using right now.
To get to the milk you'll have to walk by 3 rows of chips and soda.
Hey, who cares about making services that work when we can give people a cool chatbot assistant and a 1800 number with no real-person alternative to the decision tree
https://www.nytimes.com/interactive/2024/09/09/technology/ai...
https://www.nytimes.com/interactive/2024/01/19/technology/ar...
These are a little bit unfair, in that we're comparing handpicked examples, but I don't think many experts will pass a test like this. Technology only moves forward (and seemingly, at an accelerating pace).
What's a little shocking to me is the speed of progress. Humanity is almost 3 million years old. Homosapiens are around 300,000 years old. Cities, agriculture, and civilization is around 10,000. Metal is around 4000. Industrial revolution is 500. Democracy? 200. Computation? 50-100.
The revolutions shorten in time, seemingly exponentially.
Comparing the world of today to that of my childhood....
One revolution I'm still coming to grips with is automated manufacturing. Going on aliexpress, so much stuff is basically free. I bought a 5-port 120W (total) charger for less than 2 minutes of my time. It literally took less time to find it than to earn the money to buy it.
I'm not quite sure where this is all headed.
It really isn't. Have a look at daily median income statistics for the rest of the planet:
https://ourworldindata.org/grapher/daily-median-income?tab=t...
$2.48 Eastern and Southern Africa (PIP)
$2.78 Sub-Saharan Africa (PIP)
$3.22 Western and Central Africa (PIP)
$3.72 India (rural)
$4.22 South Asia (PIP)
$4.60 India (urban)
$5.40 Indonesia (rural)
$6.54 Indonesia (urban)
$7.50 Middle East and North Africa (PIP)
$8.05 China (rural)
$10.00 East Asia and Pacific (PIP)
$11.60 Latin America and the Caribbean (PIP)
$12.52 China (urban)
$7.75 World
Flashlights? Sure, bring on aliexpress. USB cables with pop-off magnetically attached heads, no problem. But power supplies? Welp, to each their own!
Is there a big recent qualitative change here? Or is this a continuation of manufacturing trends (also shocking, not trying to minimize it all, just curious if there’s some new manufacturing tech I wasn’t aware of).
For some reason, your comment got me thinking of a fully automated system, like: you go to a website, pick and choose charger capabilities (ports, does it have a battery, that sort of stuff). Then an automated factor makes you a bespoke device (software picks an appropriate shell, regulators, etc). I bet we’ll see it in our lifetimes at least.
There is the very real possibility that everything just stalls and plateau where we are at. You know, like our population growth, it should have gone exponentially but it did not. Actually, quite the reverse.
Progress isn't inevitable. It's possible for knowledge to be lost and for civilization to regress.
The Technological Singularity - https://en.wikipedia.org/wiki/Technological_singularity
I don't share your confidence in identifying real people anymore.
I often flag as "false-ish" a lot of things from genuinely real people, but who have adopted the behaviors of the TikTok/Insta/YouTube creator. Hell, my beard is grey and even I poked fun at "YouTube Thumbnail Face" back in 2020 in a video talk I gave. AI twigs into these "semi-human" behavioral patterns super fast and super hard.
There is a video floating around with pairs of young ladies with "This is real"/"This is not real" on signs. They could be completely lying about both, and I really can't tell the difference. All of them have behavioral patterns that seems a little "off" but are consistent with the small number of "influencer" videos I have exposure to.
I don't. I mean, I can identify the bad ones, sure, but how do I know I'm not getting fooled by the good ones?
I see a lot of outrage around fake posts already. People want to believe bad things from the other tribes.
And we are going to feed them with it, endlessly.
Even what's free & open source in the special effects community is astonishing lately.
And it already happened, and no one pushed back while it was happening.
It's by Language Jones, a YouTube linguist. Title: "The AI Apocalypse is Here"
If we haven't already, we will be very soon. I'm sure there are people working on this problem, but I think we're starting to hit a very imminent feedback loop moment. Most of human's recorded information is digitized and most of that is generating non-human content at an incredible pace. We've injected a whole lot of noise into our usable data.
I don't know if the answer is more human content (I'm doing my part!) or novel generative content but this interim period is going to cause some medium-term challenges.
I like to think the LLM more-tokens-equals-better era is fading and we're getting into better use of existing data, but there's a very real inflection point we're facing.
Corporations did that, not humans.
"few people recognize that we already share our world with artificial creatures that participate as intelligent agents in our society: corporations" - https://arxiv.org/abs/1204.4116
Nice try
If it’s not clear, I’m joking.
Next token-seeking is a solved problem. Novel thinking can be solved by humans and possibly by AI soon, but adding more garbage to the data won't improve things.
That's why on FB I mark my own writing as AI generated, and the AI generated slop as genuine. Because what is disguised as "transparency disclaimer" is just flagging content of what's a potential dataset to train from and what isn't.
Apropos of nothing in particular, see LinkedIn now admitting [1] it is training its AI models on "all users by default"
What would it take for Open AI overlords to inject words they want to force into usage in their models and will new words into use? Few have had the power to do such things. Open AI through its popular GPT platform now has the potential of dictating the evolution of human language.
This is novel and scary.
Or we'll be fine, because inbreeding isn't actually sustainable either economically nor technologically, and to most of the world the Silicon Valley "AI" crowd is more an obnoxious gang of socially stunted and predatory weirdos than some unstoppable omnipotent force.
So I would still state noun-phrase frequency in LLM output would tend to reflect noun-phrase frequency in training data in a similar context (disregarding enforced bias induced through RLHF and other tuning at the moment)
I'm sure there will be cross-fertilization from LLM to Human and back, but I'm not seeing the data yet that the influence on word-frequency is that outspoken.
The author seems to have some other objections to the rise of LLM's, which I fully understand.
There is the case of what is "truth". As soon as you start to ensure some quality of truth to what is generated, that is political.
As soon as generative AI has the capability to take someone's job, that is political.
The instant AI can make someone money, it is political.
When AI is trained on something that someone has created, and now they can generate something similar, it is political.
What is political is people enshrining themselves in chokepoints and demanding a toll for passing through or getting anything done. That is what you do when you make a certain job politically 'untakable'.
People who espouse that the 'personal is political' risk making the definition of politics so broad that it is useless.
It could also be useful for guessing whether someone might have been trying to do some kind of steganographic or additional encoding in their work, by telling you how abnormal compared to how many people write it is that someone happened to choose a very unusual construction in their work, or whether it's unlikely that two people chose the same unusual construction by coincidence or plagiarism.
You might also find statistical models interesting for things like noticing patterns in people for whom English or others are not their first language, and when they choose different constructions more often than speakers for whom it was their first language.
I'm not saying you can't use an LLM to do some or all of these, but they also have something of a scalar attached to them of how unusual the conclusion is - e.g. "I have never seen this construction of words in 50 million lines of text" versus "Yes, that's natural.", which can be useful for trying to inform how close to the noise floor the answer is, even ignoring the prospect of hallucinations.
I already find myself mentally filtering out audible releases after a certain date unless they're from an author I recognize.
> Why not just own the fact that LLMs are going to be affecting our speech?
The problem is that we cannot tell what's a result of LLMs affecting our speech, and what's just the output of LLMs.
If LLMs result in a 10% increase of the word "gimple" online, which then results in a 1% increase of humans using the word "gimple" online, how do we measure that? Simply continuing to use the web to update wordfreq would show a 10% increase, which is incorrect.
I can’t find it, but there’s someone selling sock puppet posts on HN even.
There's probably an analogy to be made about the open decentralised internet in the age of AI here, if it gets to the point that search engines have to assume all sites are spam by default until proven otherwise, much like how an email server is assumed guilty until proven innocent.
However, the frontline of the email war has shifted lately. Now the most important part of the war is being fought over emails that look just like ham, but aren't. Business frauds where someone convinces you that they are the CEO or CFO or some VP and they need you to urgently buy this or that for them right now no time to talk is big business right now, and before you get too high-and-mighty about how immune you are to that, they are now extremely good at looking official. This war has not been won yet, and to a large degree, isn't something you necessarily win by AI either.
I think there's an analogy here to the war on content slop. Since what the content slop wants is just for you to see it so they can serve you ads, it doesn't need anything else that our algorithms could trip on, like links to malware or calls to action to be defrauded, or anything else. It looks just like the real stuff, and telling that it isn't could require a human rather vast amounts of input just to be mostly sure. Except we don't have the ability to authenticate where it came from. (There is no content authentication solution that will work at scale. No matter how you try to get humans to "sign their work" people will always work out how to automate it and then it's done.) So the one good and solid signal that helps in email is gone for general web content.
I don't judge this as a winning scenario for the defenders here. It's not a total victory for the attackers either, but I'd hesitate to even call an advantage for one side or the other. Fighting AI slop is not going to be easy.
I'm not saying this is impossible but that's going to be an uphill sell for me as a concept. According to some quick stats I checked I'm getting roughly 600 emails per day, about 550 of which go directly to spam filtering, and of the remaining 50, I'd say about 6 are actually emails I want to be receiving. That's an impressive amount overall for whoever built this particular filter, but it's also still a ton of chaff to sort wheat from and as a result I don't use email much for anything apart from when I have to.
Like, I guess that's technically usable, I'm much happier filtering 44 emails than 594 emails? But that's like saying I solved the problem of a flat tire by installing a wooden cart wheel.
It's also worth noting there that if I do have an email thats flagged as spam that shouldn't be, I then have to wade through a much deeper pond of shit to go find it as well. So again, better, but IMO not even remotely solved.
Looking at the last days’ spam¹ I have three 419-style scams (widows wanting to give away their dead husbands’ grand piano or multi-million euro estate) and three phishing attempts. There are duplicate messages in each category.
About fifteen years ago, I did a purge of mailing list subscriptions and there’s very little that comes in that I don’t want, most notably a writer who’s a nice guy, but who interpreted my question about a comment he made on a podcast as an invitation to be added to his manually managed email list and given that it’s only four or five messages a year, I guess I can live with that.
⸻
1. I cleaned out spam yesterday while checking for a confirmation message from a purchase.
Advertising in your mails isn't Google's.
Athens had a democracy over 2500 years ago. A few Native American tribes had long-lasting democracies. Ukrainian cities were democratically self-governing 500 years ago, and Poland had elected kings.
Those were isolated examples. This was not a revolution. We also haven't regressed; isolated examples continued throughout history. If you point to a year, you can probably find some democracy somewhere. The only major regression I know in history was around 1000BC. Regressions are rare.
What changed was a revolution. From just before 1800 to just a little after 1900, virtually every country had a revolution which led to either being some form of democracy, or pretending to be one. Democracy was no longer isolated. We had the creation of a free world covering much of the world's population, and the creation of what was pretending to be a democracy (today, even the Democratic People's Republic of Korea pretends to be a democracy).
The number of countries which claim to not be a democracy, you can count on your fingers. Iran. Vatican City. Saudi Arabia. UAE. Oman. Eswanti. Did I miss any?
https://en.wikipedia.org/wiki/List_of_countries_by_system_of...
Everything is “seamless” nowadays. Like I am seamlessly commenting here.
Arguably, the meaning of these words evolve due to misuse too.
I wish there were a way to check.
This is incorrect. Furthermore, what the LLM says is also determined by what its user wants it to say, and how frequently the user wants the LLM to post on the internet. This will have a large effect on the internet’s word frequency distribution.
They're the ones who post racist diatribes under green alt account and anti-vaxx conspiracy theories and complain about wokeness and talk out of their ass about subjects they have no actual expertise in. The ones who think flagging is just a super downvote.
I mean, the premise that simply having a minimalist layout would filter for a better class of people has always been a bit silly. Eternal September is an inevitable result of having new people join a community. Every new person, every new comment, increases entropy. It doesn't matter how technically proficient they are, they will change the culture simply by virtue of their existence and participation at scale.
But seriously, it's harder to accidentally make a USB cable that fries your equipment. The more common failure mode is it fails to work, or wears out too fast. Chargers on the other hand, handle a lot of voltage, generate a lot of heat, and output to sensitive equipment. More room to mess up, and more room for mistakes to cause damage.
It's relatively trivial to photoshop misinformation in a really powerful and undetectable way- but I don't see (legitimate) instances of groundbreaking news over a fake photo of the president or a CEO etc doing something nefarious. Why is AI different just because it's audio/video?
And it's not the grounbreaking the problem, it's the little constant lies.
Last week a photoshopped Musk tweet was going around, people getting all up in arms against it despite the fact it was very easy to spot as a fabricated one.
People didn't care, they hate the guy, they just wanted to fuel their hate more.
The whole planet run on fake content, magazin covers, food packaging, instagram pics of places that never looks that way...
And now, with AI, you can automate it and scale it up.
People are not ready. And in fact, they don't want to be.
Is it of your own coinage? When the AI sifts through the digital wreckage of the brief human empire, they may give you the credit.
Increasingly I find that for in-depth explanations or tutorials Youtube is the only place to go, but even there the search results can lead to loads of videos which just seem… off. But at least those are still made by humans.
It seems to me that the fact it's so cheap and relatively easy for people with dreams of becoming wealthy influencers to put stuff out there has more to do with the flood of often mediocre content than AI does.
Of course the vast majority don't have much real success and get on with life and the crank turns and a new generation perpetuates the cycle.
LLMs etc. may make things marginally easier but there's no shortage of twenty somethings with lots of time imagining riches while making pennies.
Google has been _monetizing_ the SEO game forever. They chose not to act against many notorious actors because the metric they optimize for is ad revenue and and those sites were loaded with ads. As long as advertisers didn’t stop buying, they didn’t feel much pressure to make big changes.
A smaller company without that inherent conflict of interest in its business model can do better because they work on a fundamentally different problem.
The problem is that, of the signals you mention,
• the highly-informative ones (posting a new review every 10 minutes, having affiliate links in the description) are contextual — i.e. they're heuristics that only work on a site-specific basis. If the point is to create a training pipeline that consumes "every video on the Internet" while automatically rejecting the videos that are botspam, then contextual heuristics of this sort won't scale. (And Google "doesn't do things that don't scale.")
• and, conversely, the context-free signals you mention (thumbnail looks AI-generated, voice is synthesized) aren't actually highly correlated with the script being LLM-barf rather than something a human wrote.
Why? One of the primary causes is TikTok (because TikTok content gets cross-posted to YouTube a lot.) TikTok has a built-in voiceover tool; and many people don't like their voice, or don't have a good microphone, or can't speak fluent/unaccented English, or whatever else — so they choose to sit there typing out a script on their phone, and then have the AI read the script, rather than reading the script themselves.
And then, when these videos get cross-posted, usually they're being cross-posted in some kind of compilation, through some tool that picks an AI-generated thumbnail for the compilation.
Yet, all the content in these is real stuff that humans wrote, and so not something Google would want to throw away! (And in fact, such content is frequently a uniquely-good example of the "gen-alpha vernacular writing style", which otherwise doesn't often appear in the corpus due to people of that age not doing much writing in public-web-scrapeable places. So Google really wants to sample it.)
I’m guessing that the association between “pagers” and “Hezbollah” ended up creating the latter two tabs, but who knows. Maybe some AI video out there did a product review of Hezbollah.
I've noticed that lately. It used to be the top google result was almost always what you needed. Now at the top is an AI summary that is pretty consistently wrong, often in ways that aren't immediately obvious if you aren't familiar with the topic.
A useful Search would ideally send a user to the site with the most signal and the fewest noise. Meanwhile, ads are inherently noise; they're extra pieces of information inserted into a webpage that at best tangentially correlate to the subject of a page.
Up until ~5 years ago, Google was able to strike a balance on keeping these two stable; you'd get results with some Ads but the signal generally outweighed the noise. Unfortunately from what I can tell from anecdotes and courtroom documents, the Ad team at Google has essentially hijacked every other aspect of the company by threatening that yearly bonuses won't be given out if they don't kowtow to the Ad teams wishes to optimize ad revenue somewhere in 2018-2019 and has no sign of stopping since there's no effective competition to Google. (There's like, Bing and Kagi? Nobody uses Bing though and Kagi is only used by tech enthusiasts. The problem with Google is that to copy it, you need a ton of computing resources upfront and are going up against a company with infinitely more money and ability to ensure users don't leave their ecosystem; go ahead and abandon Search, but good luck convincing others to give up say, their Gmail account, which keeps them locked to Google and Search will be there, enticing the average user.)
Google has absolutely zero incentive to filter out generative AI junk from their search results outside the amount of it that's damaging their PR since most of the SEO spam is also running Google Ads (since unless you're hosting adult content, Google's ad network is practically the only option). Their solution therefore isn't to remove the AI junk, but to instead reduce it enough to the degree where a user will not get the same type of AI junk twice.
A search engine isn't a two-sided market in itself but the ad network that supports it is. A better search engine is a technological problem, but a decently paying ad network is a technological problem and a hard marketing problem.
(If children are never taught to think critically, then...)
I'm certain you'd be shocked to see the amount of CG that's in some of your favorite movies made in the last ~10-20 years that you didn't notice because it's undetectable
But, yeah, I do think it is some kind of bias. Maybe not survivorship, though… maybe it is a generalized sort of Malmquist bias? Like the measurement is not skewed by the tendency of movies with good CGI to go away. It is skewed by the fact that bad CGI sticks out.
It sucks, because sharing recipes seemed like one of those things the internet could be really good at.
I read Spanish and Italian fluently and stumble my way through Japanese (with translation). It's easier to find a good recipe in these languages, provided you can find the ingredients or substitutes.
The analogy is that data is now contaminated with AI like steel is now contaminated with nuclear fallout.
https://en.wikipedia.org/wiki/Low-background_steel
>Low-background steel, also known as pre-war steel[1] and pre-atomic steel,[2] is any steel produced prior to the detonation of the first nuclear bombs in the 1940s and 1950s. Typically sourced from ships (either as part of regular scrapping or shipwrecks) and other steel artifacts of this era, it is often used for modern particle detectors because more modern steel is contaminated with traces of nuclear fallout.[3][4]
From the same wiki you linked:
"Since the end of atmospheric nuclear testing, background radiation has decreased to very near natural levels, making special low-background steel no longer necessary for most radiation-sensitive uses, as brand-new steel now has a low enough radioactive signature"
and
"For the most demanding items even low-background steel can be too radioactive and other materials like high-purity copper may be used"
For applications that need to avoid the background radiation (like physics research), pre atomic age steel is extracted, like from old shipwrecks.
> Low Background Steel (and lead) is a type of metal uncontaminated by radioactive isotopes from nuclear testing. That steel and lead is usually recovered from ships that sunk before the Trinity Test in 1945.
This is a desired quality, increasingly less present in IT work environments. People afraid of being shamed for stating knowledge gaps are not the folks you want to work with.
LLMs pollute the internet like atomic bombs polluted the environment.
When I saw them, they blew my mind. Short to store and easy to understand.
But if they ever include other topics, they risk becoming more mainstream and noisy. Even within adjacent fields (like the various Stacks) it gets pretty bad.
Maybe the trick is to stay within a single small sphere then and not become a general purpose discussion site? And to have a low enough volume of submissions where good moderation is still possible? (Thank you dang and HN staff)
I wonder if it is more to do with the community itself. HN users tend to have very intelligent discussions on pretty much anything, and discourages shitty, unnuanced, one-line takes. This, coupled with a healthy moderation system, makes it hard for the lower quality discussion to break in and override the good stuff.
A good example of the generalization problem you discuss is reddit.
You have to unsubscribe from all the defaults and find the small, niche, communities about specific topics. If not, it's the same stuff, reposted, over and over, across different subs and/or social sites.
My experience here is that it's pretty good for things outside of tech (at least better than the average internet) but definitely not great.
A vaping discussion brought up glycerin used was safe and the same thing used in smoke machines and someone else brought up a study showing that smoke machines are an occasional safety issue. Nowhere near every discussion goes that well but stick around and you’ll see in-depth discussion.
Go to a public health website by comparison and you’ll see warnings without context and a possibility positive spin compared to smoking. https://www.cdc.gov/tobacco/e-cigarettes/index.html I suspect most people get basically nothing from looking at it.
As someone with domain expertise here, I wholeheartedly disagree. HN is very bad at percolating accurate information about topics outside its wheelhouse, like clinical medicine, public health, or the natural sciences. It is also, simultaneously, extremely prone to overestimating its own collective competency at understanding technical knowledge outside its domain. In tandem, those two make for a rather dangerous combination.
Anytime I see a post about a topic within my area of specialty, I know to expect articulate, lengthy, and completely misguided or inaccurate comments dominating the discussion. It's enough of a problem that trying to wade in and correct them is a losing battle; I rarely even bother these days.
It's kind of funny that XKCD #793[0] is written about physicists, because the effect is way worse with software engineers.
Sometimes I try and engage, but honestly, mostly I think it's not worth it. Otherwise you end up doing this with your life: https://xkcd.com/386/
And to hold up discussions about MS as an example of 'extremely' low quality discussion is, ah, interesting. Do you have any recent examples of such discussions?
relative to what? reddit?
also there's a trade off between entropy and "quality". too much "quality" and everyone gets bored and goes somewhere more entertaining
That is, when baking, you can usually (again, exceptions for creaming the sugar in butter, etc.) take all of your dry ingredients and mix/sift them together, and then you pour your wet ingredients in a well you’ve made in the dry ingredients (these can also usually be mixed together).
But for shortbread or fork biscuits those three could indeed all go in the bowl in one go (but that one admittedly doesn't really need a bracket because the recipe is "put in bowl, mix with hands, bake").
I’ve worked on the government side of this stuff and find it disheartening.
So it's classic positive feedback. LLM uses delve more, delve appears in training data more, LLM uses delve more...
Who knows what other semantic quirks are being amplified like this. It could be something much more subtle, like cadence or sentence structure. I already notice that GPT has a "tone" and Claude has a "tone" and they're all sort of "GPT-like." I've read comments online that stop and make me question whether they're coming from a bot, just because their word choice and structure echoes GPT. It will sink into human writing too, since everyone is learning in high school and college that the way you write is by asking GPT for a first draft and then tweaking it (or not).
Unfortunately, I think human and machine generated text are entirely miscible. There is no "baseline" outside the machines, other than from pre-2022 text. Like pre-atomic steel.
Some day we may view this as the beginnings of machine culture.
Have you ever seen someone use their smartphone? They're not "here," they are "there." Forming themselves in cyberspace -- or being formed, by the machine.
2. Given how LLMs work, a prompt is a bias — they're one-and-the-same. You can't ask an LLM to write you a mystery novel without it somewhat adopting the writing quirks common to the particular mystery novels it has "read." Even the writing style you use in your prompt influences this bias. (It's common advice among "AI character" chatbot authors, to write the "character card" describing a character, in the style that you want the character speaking in, for exactly this reason.) Whatever prompt the developer uses, is going to bias the bot away from the statistical norm, toward the writing-style elements that exist within whatever hypersphere of association-space contains plausible completions of the prompt.
3. Bot authors do SEO too! They take the tf-idf metrics and keyword stuffing, and turn it into training data to fine-tune models, in effect creating "automated SEO experts" that write in the SEO-compatible style by default. (And in so doing, they introduce unintentional further bias, given that the SEO-optimized training dataset likely is not an otherwise-perfect representative sampling of writing style for the target language.)
But, insofar as your use-case is against the TOUs of the big proprietary inference platforms, this second cost quickly swamps the first cost. They keep banning you, and you keep having to buy new dark-web credentials to come back.
Given this, it’s a lot cheaper and more reliable — you might summarize these as “more predictable costs” — to design a system around a substrate whose “immune system” won’t constantly be trying to kill the system. Which means either your own hardware, or a “being your own model” inference platform like RunPod/Vast/etc.
(Now consider that there are a bunch of fly-by-night BYO-model hosted inference platforms, that are charging unsustainable flat-rate subscription prices for use of their hardware. Why do these exist? Should be obvious now, given the facts already laid out: these are people doing TOU-violating things who decided to build their own cluster for doing them… and then realized that they had spare capacity on that cluster that they could sell.)
TFA mentions this hasn't been the case.
Too deep we delved, and awoke the ancient delves.https://news.ycombinator.com/item?id=41499957
https://news.ycombinator.com/item?id=41408124
https://news.ycombinator.com/item?id=41335757
https://news.ycombinator.com/item?id=41327379
None of which fit your description of “neckbeardy tropes about their products being garbage spyware, switch to Linux, they're stealing your data, the OS is trash”.
And it isn’t just me, because if you look at those comments, I was talking to other people who weren’t invoking those “neckbeardy tropes” either
The only remaining reliable source - now that many newspapers are axing the remaining staff in favour of LLMs - is pre-2020 print cookbooks. Anything online or printed later must be assumed to be tainted, full of untested sewage and potentially dangerous suggestions.
I joke with folks that my assumption with "one clove of garlic" is that they really mean "one head of garlic" if you want any flavour. (And if the recipe title has "garlic" in it and you are using one clove, you’re lying.)
I also don't care if people leave because HN isn't 'entertaining' enough. I don't come here for that, and I don't expect the community members that make this place what it is to either.
It’s the same origin. On Slashdot (the HN of the early 00’s) people would admonish others to RTFA. Then they started using it as a referent: TFA was the thing you were supposed to have read.
I miss the old Atomic MPC forums in the ~00s.
New things arise from the ashes.
Another solution, and one that, if i weren't such a lazy, is ocean based carbon binding. You can run electricity directly through ocean water and precipitate the carbon out as calcium carbonate, which is both: useful to humans as is and after processing; and useful to the coral reefs and crustaceans/mollusks or whatever in the oceans.
If anyone wants to kick me about a million US dollars, i can make a POC on a used barge with solar panels and as much recycled material as possible, and have that just run off the coast of florida or something. I figure the total cost to get a barge is around a quarter million, all-in[1], the electronics and seawater stuff is about another $150-200 thousand, and the rest is mine for the idea and the lawyers' to get this approved and left alone to do the research.
[0] burning it for heat is fine, as the net CO2 levels will remain constant, but i mean things like houses and boardwalks and boats, furniture, and so on.
[1] could be more, now, the last time i was researching seaworthy barge costs it was between $100,000 and $200,000. I'm hoping there's someone that can donate the barge so i can make the rest more fit for purpose - redundancy, better solar, better mppt, better batteries, better materials for the electrodes (it takes platinum and titanium iirc, i haven't looked at my documents for a long while.)
(Sorry for pessimism and offtopicism)
After doing some healthcare work I ended up understanding that some topics are not well known even by the professionals dedicating their whole lives to that because there are big gaps in the human knowledge on the topics.
I agree that people that think they can reason in two minutes about anything are a problem, but it's not a healthcare only issue (same happens for politics, economics, environment, etc.)
Engineers have the luck to work in the field where many things have a clear, known explanation (although, try to make an estimation about how long a team will implement a feature, and everybody will come up with something else).
Far from saying the pool of language is now polluted, I think we now have a great data set to begin to discern authentic from inauthentic human language. Although sure, people on the fringes could get caught in a false positive for being bots, like you or I.
The biggest LLM of them all is the daily driver of all new linguistic innovation: Human society, in all its daily interactions. The quintillions of daily phrases exchanged and forever mutating around the globe - each mutation of phrase interacting with its interlocutor, and each drawing from not the last 500,000 tokens but the entire multi-modal, if you will, experience of each human to date in their entire lives - vastly eclipses anything any hardware could ever emulate given the current energy constraints. Software LLMs are just a state machine stuck in a moment in time. At best they will always lag, the way Stalinist language lagged years behind the patois of average Russians, who invented daily linguistic dodges to subvert and mock the regime. The same process takes place anywhere there is a dominant official or uncool accent or phrasing. The ghetto invents new words, new rhythm, and then it becomes cool in the middle class. The authorities never catch up, precisely because the use of subversive language is humanity's immune system against authority.
If there is one distinctly human trait, it's sniffing out anyone who sounds suspiciously inauthentic. (Sadly, it's also the trait that leads to every kind of conspiracy theorizing imaginable; but this too probably confers in some cases an evolutionary advantage). Sniffing out the sound of a few LLMs is already happening, and will accelerate geometrically, much faster than new models can be trained.
And yes, this is true for many other areas of discussion at HN. It's just that it is most obvious to me in the area that my wife specializes in, because I pick up enough via osmosis from her to know when other people don't even have my limited level of understanding.
1: Or at least were 15 years ago when my wife told me about it- the argument might have been largely concluded and she just never updated me since I don't keep up with the medical literature the way she does.
2: Two decades ago there was a huge push for the "human genome project" under the basis that this would be "reading the blueprints for human life" and that would give us all of these medical breakthroughs. Basically none of those breakthroughs happened because we've spent the past 20 years learning all of the different ways that it is NOT a blueprint and that cells do things very differently from human engineers.
The benefits for understanding more about genomes are growing (ex: list of adverse effects based on genotype https://go.drugbank.com/pharmaco/genomics) but the field is/(was) so chaotic (just one example: there was not one standard about how to count: https://tidyomics.com/blog/2018/12/09/2018-12-09-the-devil-0...) and so lacking data that it will take many years to reap the benefits (ex: one of the largest study UK Bio bank gave access to researchers only in 2017 - https://en.wikipedia.org/wiki/UK_Biobank)
I'm a little more cautious though. I think GPT will be way more integrated, simply because it's useful. Stalinist language was artificial, in the sense that it was basically imposed on you from outside for no good reason. When you wanted to get real stuff done (either talking to close friends, being productive with colleagues, etc) you wouldn't use socialist newspeak because it got in the way. GPT will be imposed by the outside world, but it's actually a useful thing to be able to converse with a language model; you'll do it every day at work, when buying things, when using your phone/PC.
And also, unlike in USSR times, so much of our communication is online and visible. It would not surprise me if we develop a model that can train continuously on the firehose. Text is small. Data rate of every person on earth speaking simultaneously:
- 150 words per minute spoken
- 150 words × (5 characters/word + 1 space) = 150 × 6 = 900 characters per minute
- 1 byte per char = 900 bytes/min = 15 bytes/sec
- 15 bytes / sec * 8,000,000,000 people speaking continuously = 120 gigabytes/second
That's a lot but it's not even the bandwidth of a single consumer GPU.
I don't think we'll have true machine culture until we have fully autonomous agents in the wild that are interacting with the world independently on its own terms. Right now the substrate is text which comes from a human mind -- it does not arise naturally from nothing. So the machine is a symbiote for now until we solve some difficult robotics problems.
I don't think you need to be independent or autonomous to develop a culture. And a lot of human culture was passed down over generations without understanding why it worked. We just imitate the behaviour and rituals from our most successful ancestors or role models.
If new LLMs can access the past generation's knowledge of how to please human evaluators, they will use it. It's not a deliberate decision by an "agent", it's just the best text source to copy from. This is a new feedback loop between generations of assistants, and it bypasses whatever the human designer had in mind. Phrases like "it is always best to ask an expert" will pop up just because you tuned the LLM to sound like a helpful assistant, and that's what helpful assistants sound like in the training data. You'd have to actively steer the new generation away from using their ancestral knowledge.
I guess it comes down to what your definition of "culture" is. There is no targeted teaching of the next generation, for example - but is this a requirement? I agree that talking about "machine culture" right now sounds like a stretch, but now I wonder what pieces are actually missing.