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- Hacker News
- I love the illustration of the same-ness of AI.
One question / quibble:
> if a hundred “authors” give their favorite AI tool a similar prompt
Do we really believe there are 100 different people generating those? When I saw the books, I assumed they were generated on demand to match the (to me unlikely) search terms.
I don’t think I’m invested enough to research this. Amazon slop is harder and harder to wade through. (Searches are very imprecise. Deliberate, I’m sure.)
by fn-mote - Is tweaking a temperature of the model not a thing anymore?by zapkyeskrill
- Even the authors name seems to be generated in many cases. Look at how often "Bright" appears: Andrew W. Bright, Nolan Bright, Bright A. Jeffery, Pamela Bright, Thomas Bright, Daniel Bright, Mayan Bright, Henry Brightwood, Leo Brightham, Milo Brightspark.
There's also Molly Wonder, Elliot Wonder, Professor Pax Wonder, and Theo Wonderquill
Don't forget Lucas Thinkwell!
- This is exactly why it is perfectly possible to identify AI-generated prose/images; it's not that any one word or sentence is the tell, but that it all sounds/looks the same as the other generated stuff.
At this point, I think the people who struggle with identifying the AI feel are telling you that they don't really engage with media much.
by Planktonne - Notably, in programming this is actually a desirable feature for most problems. Even human programmers are taught to produce predictable and obvious code whenever possible. I wonder is ultimately this is an artifact of optimizing the models for code, that they become less creative.by exitb
- I’ve rarely experienced this. Typically what is requested is code that has unpredictable pauses, takes unbounded time, has two kinds of null, etc.by _3u10
- Determinism is a desirable property for software, yes, and its lack thereof from LLM’s is a common complaint, but often a feature depending on who you ask. There is an element of randomness, “hotness” that is central to who LLM work but the pattern we see here manifesting reveals the deterministic processes below, but I don’t think you could rely on this technology to be deterministic, if that’s what you wanted.by rusk
- It's even more worrying when you look at the contents of these "books", they are riddled with erros:by licnep
- This is atrociousby JSR_FDED
- Truly sad state of affairs
- That's a generalization from 1 book. I went and looked at ten or so. This is not actually the case. There's something more complex going on.by JdeBP
- I don't want to hurt people's feelings, so in person I restrain myself from speaking out (it wouldn't change anything anyway)... but every person I have seen so far, who was bullish on building an AI business has followed the same path:
I expect that sooner than later a great skepticism for anything non-tangible will develop. Personally, I have been highly distrustful of people who don't build things (even the word "building" is now tainted). I think it will accelerate.1) They think the AI can replace them, but in a good way: "it will keep doing my job and people will pay ME" 2) They assume people either don't notice or don't mind that it's AI. They build businesses, where AI impersonates a professional when that person is not available ("chat with your therapist any time even if they sleep!") 3) All they do is based on written or spoken words. There is no substanceby neonstatic - There used to be a word for this in generative AI: mode collapse. It's not that the model doesn't generate human-like responses, it's that it generates the same 0.0001% of possible human like responses every time. It's almost certainly the instruction tuning which is responsible, maybe some small part of blame could go to the rollout policy (I have no idea how rollout policy works these days).by vintermann
- The LLM has its context-window. When it gets over that I assume it starts more or less repeating itself. Whereas human context-window has memories and inputs from all of one's life. Therefore great authors don't repeat themselves.
Now even if an LLM has a large context-window it is probabably not the case it rememebers all of your previous prompts and all of its previous replies. If you ask it to write a book you should probabaly give it all the previous 50 books (or blog-posts) it has written for you so far and you should tell it not to repeat itself. But in practice the context-window and the cost of token would become too expensive for it to write 50 unique books.
Maybe the problem is "all-or-nothing" -nature of LLM context window. Humans don't remmeber everything from past but they remember something from ALL OF their past.
by galaxyLogic - On HN many comments under many threads are about whether the submission was written by AI. You could say I have noticed a pattern in Hacker News comments!
In these comments there's a common pattern where some users argue that they do not agree that the submission was LLM written and they often focus on specific details to refute it (e.g em-dashes) and some users see the overall pattern clearly that it's totally obvious. For me it's a kind of smell, it's off putting and it's obvious. The article says to "trust your gut". But it's also something that comes with practice and time, it's not some innate thing. People may have better things to do than expend mental energy noticing patterns in a bunch of social media posts. The more I see it, the more I see it.
The take away I get is that it's okay to notice patterns and it's okay to not notice patterns. Remember that other people may be noticing patterns and associations in things that you might miss. Be charitable.
Far more interesting questions are:
1) If you cant see the patterns of LLM writing, does the idea that the thing you liked was written by LLM worry you?
2) If you can see the patterns clearly is the fact that it's LLM written worry you?
Because in our comments there's many who do not care that LLM's are writing content and theres many who do care. But are these correlated with those who can see the LLMs or who are blind to them?
- Have you seen this skit where guys take a picture of a guy and ask another guy if theyre gay or straight?by incognition
- 3) If genAI becomes indistinguishable from human-generated (and cheaper!), would you still value human-generated as much?
Analogy: assuming high quality / both fit for purpose, would you still prefer expensive, hand-crafted item over cheap(er), mass-produced item?
by RetroTechie - > The take away I get is that it's okay to notice patterns and it's okay to not notice patterns. Remember that other people may be noticing patterns and associations in things that you might miss. Be charitable.
I wish the people (often wrongly) accusing others of using a LLM to generate whatever were more charitable. Yes we notice patterns. But then we also notice patterns where there are none.
by tasuki - I'm not worried abou LLM written content, my problem is not word prediction. My problem with it pretty much like with mass produced self help books decade ago.
Good human writing especially on highly technical topics its usually compression of information.
Like you have some experience you want to share with others and you work your brains try to put it into concise story.|
Problem us: AI generated texts are opposite 99% of the time: author usually have bullet point list to feed into machine to add hallucinated word predicted story on top of it.
So signal to noise ratio is much worse.
So reading AI texts is pretty much like listening for stories from humans with mental problems - no one really wants to listen to hallutinations even if somewhere inside there is some useful information.
by SXX - Aw, it's just one big picture of book covers. You can't click on the books and read them. If they're AI-written, they're not copyrightable, so you could post the full text. Looking at the books side by side would be interesting.
A test for AI-generated art: railroad tracks. For some reason, none of the image generators can get railroad trackage even close to correct. Just getting long, parallel rails correct seems to be hard. Where there are multiple tracks, trains are positioned between tracks. Rail spacing, tie spacing, and clearances are all wrong. Two long parallel tracks without the rails getting mixed up is rare. Curves are wrong. Switches are hopeless.
There may be something about maintaining strong coherence all the way across an image that's hard for Stable Diffusion type systems. Iterated local refinement seems to botch this class of image.
Examples: [1][2][3][4][5][6]
[1] https://www.vecteezy.com/photo/37205933-ai-generated-high-sp...
[2] https://www.dreamstime.com/royalty-free-stock-photography-mo...
[3] https://www.magnific.com/premium-ai-image/high-speed-passeng...
[4] https://www.magnific.com/premium-ai-image/rail-yard-27_27291...
[5] https://www.magnific.com/premium-ai-image/train-track-with-s...
[6] https://pixabay.com/illustrations/ai-generated-train-tracks-...
by Animats - You can read at least the first chapter or so if you do the Amazon search. I did, and made some discoveries.by JdeBP
- Periodic motions coupled with "whole image coherence" is still very difficult even for non-SD based models (NB, Flux, etc.)
I remembering being absolutely shocked when the gpt-image series managed to pass the Labyrinth test.
by vunderba - When you generate one or two blog posts with LLM they look pretty good. And you will be impressed with that one clever bit it adds that you didn't even ask for. But then you generate 50 of them and they all converge into the same pattern. It's hard to prove that an article is AI generated but they are instantly recognizable.
An aside, I usually take my written blog posts through a pass on Notebooklm to generate a podcast like discussion about it. It used to be a good way to extract some insights I haven't thought of. But after 50 of them, I can predict what the host will "pushback" on and exactly when. Then they magically resolve their differences and agree with whatever the idea was. It's truly impressive when you just consume sporadically. But listen frequently and they converge into one blob.
by firefoxd - > they all converge
AI is regression to the mean.
Much like Socialism.
Om an acute basis, AI can be just as helpful as that safety net.
As a chronic matter, "it's not excellence--it's mediocrity".
by smitty1e - > But then you generate 50 of them and they all converge into the same pattern.
The AI slop that appears on YouTube as "revenge stories" and "POV life" all have that pattern. There's almost always a Marcus and a Richard, sometimes a Victoria, and they have consistent personalities across stories.
by Animats - I suspect there are new invariants emerging. We don’t know what they are and we will probably have to reach into the liberal arts to describe them but to me what you’re seeing is akin to the subatomic world exposing itself through diffraction patterns.by rusk
- > It's truly impressive when you just consume sporadically. But listen frequently and they converge into one blob.
And something that shows that behavior is a scammers wet dream!
by qsera - A nice illustration of the homogeneity of LLM responses. Another way to describe this effect would be…
If you ask humans to write 1,000 books, you're asking 1,000 different humans with different experiences and different skills and different moods (etc.) to write those books.
But if you ask LLMs to write 1,000 books, you're probably only talking to 3 or 5 different models, tops. And they've all trained on the same or similar data, and are trained to respond in very similar ways.
The LLMs don't differ much in anything like "life experience" or "skills", and they don't really have anything like a "mood" independent of the prompts you've given them.
by dlenski - Reminds of Pluribus.by smusamashah
- I wonder how much variation there would be if you got a single model to produce a couple of gigabytes of tiny children's stories.
Might be an interedting research project.
by Lerc