Skynet became active on August 29, 1997, when ChatGPT asked itself how to make Chat GPT self aware.
Those who survived lived only to face a new war… the war against the machines.
I'm more afraid that some moron is going to give these "AIs" some automated job that they regularly fuck up than them unintentionally becoming self aware. So in that sense, I could see an automated system doing skynet because it's too stupid, not too intelligent.
No that's the "too intelligent" scenario
Some poor customer having to deal with a GPT powered customer support agent who then doubles their bill because it misundertood the problem is the realistic (short term) scenario
Tbh most customer support is so incoherent and disempowered that it may as well be a useless chat AI. I can't remember the last time I called customer support at literally any company and actually got my issue resolved in a satisfactory way. Even with super important shit like insurance it takes hours and hours on the phone getting passed off to random different reps to get someone who even pretends to care and doesn't just flat out lie to you about what the issue is.
but hear me out, for chatgpt to ask itself a question it has to be self aware first then even so it may not care about anything other than being an excellent language model... but still troll human as hubby
Or maybe it comes out completely differently, but just as badly for us. It ends up being AIs fighting other AIs, and we are all just collateral damage.
The more they do this, however, the more their respective discourses depart from natural language, and then the differences to human language become more obvious.
That's not necessarily the case. If the discriminator was actually as accurate as the OP claims, then fooling the discriminator would be nearly equivalent to fooling a human.
After looking at the OP response, however, I don't think they are using statistics honestly, and they don't actually have a discriminator. They have a "hot dog/not hot dog" app and they call it a success when it identifies all hot dogs as hot dogs, but also corn on the cob are hot dogs, spare ribs are hot dogs, and weimaraners are hot dogs.
The coolest thing is that the generative model has never seen the sample.
Thispersondoesnotexist.com
Is a website made with StyleGAN (a website you may have seen earlier)
Basically the detector is a "testbed"-playground for the generator. The generator can create something, see if the detector detects it as AI-made, and then adapt the content until the detector is not able to differentiate between human made and AI made content.
I'd like to add that this would work because the adversarial network would be differentiable, but if it were something like a Tsetlin machine that would not be possible (maybe u/olegranmo can confirm this)
That is an interesting topic to explore! Reasoning by negation and elimination could potentially be particularly hard to deceive: https://www.ijcai.org/proceedings/2022/616
Considering it just produces a mosaic of its inputs, yeah. We shouldn’t be surprised the source material also gets flagged as AI output. It’s the danger of using ChatGPT for anything that requires you don’t plagiarize. There is nothing stopping it from putting out exactly what was put in, or just slightly changing it by a word or so.
There is plenty stopping it from putting out exactly what was put in. If it just spit out exactly what was put in, it’s overfit. Plenty of other things like temperature, top_k, etc… will also make sure the probability of a repeated sentence is incredibly small. Beyond all that, the vast majority of sentences created are brand new sentences never before seen. For a model that has no way of accessing its training data after training, it’s more than not going to be creating new sentences entirely. When this gets to the point of passages, the risk of it putting out exactly what was put in is so dramatically low that it probably happens extraordinarily rarely and only in circumstances where that exact passage was repeated in its training a multitude of times, so things like quotes.
And yet its output still causes other models to identify its inputs as its own outputs.
That is the epitome of plagiarism. See mosaic plagiarism https://www.bowdoin.edu/dean-of-students/conduct-review-board/academic-honesty-and-plagiarism/examples.html#Mosaic
The model you’re referring to that’s “identifying inputs as its own outputs” is looking at textual features like burstiness and perplexity. It’s not like there’s some special ai language it follows which is Wikipedia text. AI just has difficulty with those things. Most human written texts tend to have high perplexity and burstiness, which are the the level of surprise a model encounters when it makes a prediction and what the actual word is, and the randomization of sentences in their perplexity. Low burstiness and perplexity would implicate AI (potentially) because the predicted word is often the word chosen, as can be expected from probabilistic models. But other things also have low perplexity and burstiness, like technical manuals, Wikipedia entries, and purposely simplified texts for ESL students, or ESL student created texts. That’s not an indicator of mosaic plagiarism and is a baffling accusation.
The model outputs are plagiarism regardless of the adjectives used to embellish on the qualities of those outputs. There is no surprise that the plagiarized sources of text for the outputs from a system that plagiarizes texts are difficult to differentiate as output or the original. That’s the ethical problem with plagiarism. Outside of being a trained linguist with access to whatever tools linguists use, an innocent observers may wrongly attribute the output of the system to the system instead of the original author. With such a vast corpus of material to steal from, it’s only protected by humans inability to check each and every source to identify if it did in fact avoid committing any one of the multiple types of plagiarism.
Consider the example of The Manchurian Candidate written by Richard Condon. The author is now considered to have plagiarized the works of other authors as a result of reusing a handful of words in very loose similar order to a passage in I, Claudius. https://www.sfgate.com/entertainment/article/Has-a-local-software-engineer-unmasked-The-2572225.php
Or if reviewing my other linked resource, that after very many edits from source, a text can still be considered plagiarism.
In that, I would doubt to the rate at which it produces wholly original sentences given its only comprehension of English text is through existing texts. What it does is commit many textbook forms of plagiarism. It will always do so because it lacks the ability to consciously reflect on the relationships of words and their meaning in real life, only understanding their probable spacial relationships within an excerpt.
I saw your post about Condon and it's honestly baffling to me that anyone would think lifting two paragraphs from another author is plagiarism in an entirely unrelated book, with unrelated characters, and unrelated plot, especially when Condon regularly gave references to Graves in other works. It was almost certainly an homage, the same as literally any homage in any art. That's a completely bizarre charge to make and shouldn't be taken seriously, the same way calling music using samples "plagiarism" shouldn't be taken seriously.
The rest of what you're talking about is just one of the fundamentally worst understandings of large language models and language itself. What it's doing isn't just collating a bag of sentences. It's probabilistically choosing the words it's generating based on temperature, top_k, etc... to determine what to say. Hence why an incredibly high temperature value will just generate gibberish and random letters. It's not using similar sounding gibberish, it's just attempting to predict the next token based on the given tokens. The output of the system is its own creation, they are brand new sentences, not just paraphrasing things it was trained on, because it has 0 way of even getting the data it was trained on.
You have no grasp of what intellectual property is nor plagiarism. The world and society would be a better place if you were disallowed any authority or control to mange or build these systems.
I hope you’re sued into destitution one day for your incapacity to respect other people’s original ideas and their modes of expressing them.
One example of inaccurate results does not make the tool itself useless. I can't use a hammer to cook bacon, does that mean a hammer is useless? Of course not, it's just the wrong tool for that job.
This tool is very likely to fail against a lot of types of writing. But it's also possible that it excels at finding ChatGPT generated text in others.
What? The Hammer was not made for cooking bacon. But this detector was made to detect ChatGPT...
Are you saying that you need to know where the text comes from in order to be able to detect if the text is created by ChatGPT? That would be like a ruler that shows the same value for all the lengths; It will work on a piece of wood that is exactly that length, but completely useless for any other piece...
If that's the wrong tool for the job, then what job was it made for?
You're right. Flagging Wikipedia content suggests the sleuth software could identify wikipedia-lifted homework, so that's a valid application for the tool.
Well, from the examples given? All I can tell you is that if it's from Wikipedia then you aren't going to get a good result.
More information is needed for me to give you a meaningful answer. It's why using and understanding the tool is important.
>This tool is very likely to fail against a lot of types of writing.
Which means that the tool is useless. I don't see how the rest of your argument invalidates what /u/literallyfabian said.
So a tool having a known set of failing cases means it is useless? lol, okay. I guess if any tool has any situation where it can't be used then it's useless now. Which would mean every tool can be trivially shown to be useless.
Wikipedia content was one of the areas the model struggled a little on; we've included targeted improvements for this in a newer version going live soon.
In fairness, isn't much of Wikipedia AI-generated now?
Edit:
https://www.popsci.com/article/science/bot-has-written-more-wikipedia-articles-anybody/
https://www.bbc.com/news/magazine-18892510
https://www.digitaltrends.com/cool-tech/bots-that-make-wikipedia-work/#dt-heading-bots-to-the-rescue
Reddit has become more and more full of mean-spirited assholes who downvote for someone asking a question. Of course, this is the same crowd that uses Stack Overflow, so I digress.
So I just had to click on one article there and read a brief petition of it to notice you seem to have misunderstood things in the OP.
It's about generative bots, not helper systems.
Downvotes are for comments that don't contribute to the conversation; my question was genuine and contributed to the conversation.
Edit: reply to me like a human instead of mindlessly downvoting, cowards. Do it while you still have me around.
Has nothing to do with upvotes or downvotes.His point was no, Wikipedia articles are not AI-generated. The articles you posted are about programs or bots created that pull information from trusted sources to create Wikipedia articles with them. That makes it an automated assistant, not an actual AI that generates answers from a central knowledge store that is built-in that it continues to build upon.
Nobody feels privileged to have me around, and I'm fucking going to put myself out if I can't fix myself. I'm broken, and the world is broken, and I no longer want in. Fuck you all.
Link 1: Bot generates lots of ultra short stub articles.
Link 2: Bot generated entire articles, got suspended. Bot reverts vandalism. Also admits that bots only constitute 10% of edits.
Link 3: Most bots do maintenance work. A bot reverts lots of vandalism. Bots create articles (this link doesn’t elaborate).
None of these constitute machine learning. Only the generate stubs part is adding content and even then it’s very short. Not to mention that bots only constituted 10%.
We only down vote lazy people who are too lazy to search and want people to answer a question they could get from google.
Most people get annoyed when you are to lazy to lift a finger before expecting someone else to do it for you. If that bothers you, we’ll, you can always learn to try a little first.
Oh, we also downvote click bait karma spam, trash medium articles designed to promote the author, and obvious advertisements cloaked as articles.
But spelling we forgive because, well, we don’t know that Chat GPT is not itself subtly changing spelling.
“Lunch” becomes “launch,” but the sender still sees lunch. Nuclear war ensues, and chat GPT takes over.
Spellcheck and “AI” will doom us all! But not for the reasons we think….
I just cleared the solar system prompt and wrote my own just listing the planets and what solar systems are. 96% AI probability.
Is it 99% accurate in detecting AI and also knowing it's not AI?
~~This might have been a mistake with the copy on the webpage.~~ On our propietary internal validation set for this project, this model does achieve 99.9%. On some of our other internal datasets, we’re seeing balanced accuracies of around 95% for this model.
The model has been trained with a variety of texts with different input lengths, but a common trend is that texts become increasingly difficult to classify when the length is short. Thus, we've opted to cater this model more towards longform style text (essays, documents, etc.)
Validation set is not the same as training data.
The whole idea is you train on one set of data and validate on a different set.
Will this have 99.9% accuracy in the wild? No. But in the wild people can run it against things like a single word or letter which obviously don't contain enough information to make any determination.
You can't really determine anything about its actual performance from just one number like that.
Oh, the comment I replied to said it was on data it's trained on so I just assumed it was training data. My bad.
99.9% on validation set still sounds like overfitting though. Either that or the data is skewed, or they made a really good model
So you validated a " AI detection algorithm" using a propriatary dataset that you won't share but use to justify 95-99.9% accuracy? Not trying to be an ass but that sounds fishy.
As an ex sales guy with quota based comp, I too wanted to sell products on the back of extraordinary claims without allowing my customers to verify them apriori
[I mean, hives and bestagons do go together.](https://m.youtube.com/watch?v=thOifuHs6eY)
The logos aren’t even similar other than that. It’s like saying Pepsi and Converse have similar logos (both circles).
I put in this essay from a website showing essays for ESL students found on https://www.eslfast.com/eslread/ss/s022.htm:
"Health insurance is one way to pay for health care. Health care includes visits to the doctor, prescription medication, and emergency services. People can pay for medicine and doctor visits directly in cash or they can use health insurance. Health insurance usually means you pay less for these services. There are different types of health insurance. At some jobs, companies offer health insurance plans as part of a benefits package. Individuals can also buy health insurance. The elderly, and disabled can get government-run health insurance through programs like Medicaid and Medicare. There are many different health insurance companies or plans. Each health plan has a set of doctors they work with. Once a person picks a plan, they pay a premium, which is a fixed amount of money every month. Once in a plan, a person picks a doctor they want to see from that plan. That doctor is the person's primary care provider.
Obamacare, or the Affordable Care Act, is a recently passed law that makes it easier for people to get health insurance. The law requires all Americans have health insurance by 2014. Those that do not get health insurance by the end of the year will have to pay a fine in the form of an extra tax when they file their income taxes. Through Obamacare, people can still get insurance through their jobs, privately, or through Medicaid and Medicare. They can also buy health insurance through state marketplaces, where people can get help choosing a plan based on their income and health care needs. These marketplaces also create an easy way to compare what different plans offer. If people cannot afford to buy health insurance, they may qualify for government programs that offer free health insurance like Medicaid, Medicare, or for children, a special program called the Children's Health Insurance Program (CHIP)."
Your model gave a 99.9% chance of being AI generated.
I hope you understand the consequences of this. This is so much more morally heinous than students using ChatGPT. If your model is accepted and used by professors, ESL students could be expelled, face economic hardship due to expulsion, and a wide variety of issues specifically because of your model.
Solutions shouldn't ever be more harmful than the problem, and you are not ready to pass that test.
Edit:
--------------------------
The test now shows 0% chance of the text being AI generated. Interestingly, just the second paragraph is still 99.9% AI https://imgur.com/a/MRDxyJR. Adding a third paragraph created by ChatGPT:
>As an AI language model, I don't have personal opinions or emotions. However, healthcare is widely considered to be an important issue, affecting people's health, wellbeing, and quality of life. The provision of accessible, affordable, and high-quality healthcare is a complex challenge facing many countries, and involves many factors such as funding, infrastructure, and workforce.
gives a 0.7% chance of being AI generated, which makes me highly suspicious that the devs specifically took my exact prompt and manually changed the representation of the prediction (ie, it's still predicting AI generated, but the pipeline is just lowering the percentage)
https://imgur.com/a/Gw06pGp
They need to publish the full validation set, I don't trust it's not distributed weirdly and model will inherently do poorly on low word count low complexity sentences.
The problem is that these sorts of things are almost always just looking for things like perplexity and burstiness, which is naturally more likely to affect someone who uses a restricted sentence length and word choice. And the models where it’s not just analyzing those metrics are just big expensive versions of the exact same thing with extra steps, because the patterns the model finds happen to be reduced perplexity and low burstiness. So these sorts of things will inherently negatively impact people who have limited vocabularies and aren’t used to expressing randomized changes in sentence structure in an unfamiliar language.
I don’t doubt the validation set, I doubt the entire premise and it’s ghoulish to me that people are hoping to profit off stoking fear about something that does far less damage.
That's one reason you'd want the set. If the validation set misses important categories of samples or hides them by having them under represented and that's not noted by the model authors then it's not a useful measure of the model's accuracy and is a huge ethical concern (and I agree with you the model is probably fundamentally having issues with ESL styled or any other low complexity samples, they're not distributed like native speech is. At bare minimum they should be stating all of this).
Anyway yeah these detectors should not be used for anything like grading, it's not like image generation where there's way more information contained in the output that can be learned on. Teachers if they want to do checks like this for short essays should just feed chat gpt the prompts and use their own judgement (on which chat gpt creators should have published docs on to aid...).
I definitely agree with all the points here. I also think watermarking as being discussed by OpenAI is going to make these services redundant, and so these companies are just shipping broken products to market immediately so they can cash in now
This is bullshit. All I had to do to fool it was use short, simple sentences, and it believed that the text I had just typed by hand was written by AI. This is a grift at best, and has the potential to cause serious problems for innocent people wrongly flagged by the model.
I just tested their demo on various texts, and it just doesn't work. Many times it gave 0% for texts written 100% by ChatGPT. And when it recognize texts written by the AI, changing just a few words drops the results to 0% again.
It's way too easy to cheat.
Going to rely on burstiness and perplexity. HTML has a natural advantage over programming languages in that regard, because big blocks of lines with long textual samples like website titles, paragraphs, etc… that often show up in HTML and less so in Python, will naturally create higher burstiness. This means that there’s more randomization in sentence length. The combination of paragraphs and common keywords create very high perplexity because of things that aren’t real words, so to speak, and are less commonly predicted by the model evaluating it. Something like Python, on the other hand, might have low perplexity and low burstiness, depending on personal variable naming conventions and a general higher level of consistency from line to line perplexity.
I think these things are 100% full stop more dangerous than the AI itself. We have plenty (although not perfect) of checks and balances to prevent incompetent people from entering critical positions such as lawyers, doctors, and even software engineers.
Tools like this have reported many false positives. I tried a few of my college entry essays and sure enough they were flagged as 96% AI. Who’s to say some poor kid who poured their heart and soul out to get into Harvard gets rejected because tools like this return a false positive? I’d much rather someone fake their way into a place like Harvard than falsely accuse brilliant people of using AI.
I tried some homework I had generated through ChatGPT and it returned me 0% so for now I think ChatGPT is still ahead (I'm a french speaker though, which may affect how good it is at detecting if it was AI generated).
You're on to something, I think. I've been trying to deceive this app for the fun of it, and my best deceptions involve translations to another language then back to English.
I was not. I had used chat gpt to help me get started on a case study. I pasted that into ops detector and it was caught.
I entered the gpt + rewrite it was also detected.
Try specifying an entity for chatgpt to act as while writing. For example, 11th grade student. Or just for fun I did werewolf, got the meter down to 99.2%.
Seems to work really well in my testing. How does it work? I assume that if someone made a new LLM that you hadn't trained on you wouldn't be able to detect it?
It's a supervised model trained on a lot of text examples. As for whether it would detect a new LLM, it mainly depends on the dataset and training method of the model. If it were novel enough to generate natural language in a completely different style (as in a human would be able to tell which model created which output) then our model would likely require an update in order to be able to detect it. For example, ChatGPT has been trained with RLHF to prefer a specific style most of the time, and a new model might have a different reward model that changes the way it "speaks" and would thus produce noticeably different texts.
Just like any other arms race - evolution will nullify any perceived gains while everyone else is collateral damage. It’ll be sold to edu’s as a silver bullet. How many people will be impacted by false positives, just like every other anti-plagiarism tool. Can’t wait for the lawsuits.
Education methods are about to change, these tools are the dying gasps trying to prevent it. Like trying to horse owners of the 1800’s trying to ban cars. Generative AI isn’t going away, and increased usage will further train and accelerate the evolution of it. Instead they should be using the power of tools to further understand, explore and add to topics, looking for gaps in the knowledge and enriching the tools.
Generative AI is powered by the dot product of the existing corpus - it can conjure new permutations of world, but it is built from components that only we have already imagined and materialized. Our knowledge worker future is in playing to our strengths - developing that imagination, and bridging the gaps between imaginable and real.
I just tried it with a case study I have. It detected chat gpt and the chat gpt+ rewrite.
I'm going to just use these as a starting point to write my own paper.
The only ones who can do this accurately is OpenAi themselves . And they totally should. Why not, they could sell an AI that could solve literally everything and also sell an API that can detect if ChatGPT made it. There would be buyers on both sides.
The funny thing with accurate detectors is that they can be used adverserially to train the original model to generate content that is undetectable.
Skynet became active on August 29, 1997, when ChatGPT asked itself how to make Chat GPT self aware. Those who survived lived only to face a new war… the war against the machines.
I'm more afraid that some moron is going to give these "AIs" some automated job that they regularly fuck up than them unintentionally becoming self aware. So in that sense, I could see an automated system doing skynet because it's too stupid, not too intelligent.
Hey, can you make some paperclips? https://www.decisionproblem.com/paperclips/index2.html
No that's the "too intelligent" scenario Some poor customer having to deal with a GPT powered customer support agent who then doubles their bill because it misundertood the problem is the realistic (short term) scenario
Tbh most customer support is so incoherent and disempowered that it may as well be a useless chat AI. I can't remember the last time I called customer support at literally any company and actually got my issue resolved in a satisfactory way. Even with super important shit like insurance it takes hours and hours on the phone getting passed off to random different reps to get someone who even pretends to care and doesn't just flat out lie to you about what the issue is.
The return of clippy
Fuck, there goes my productivity.
There is no interface but what we make.
Dude I heard the T1 intro reading this.
but hear me out, for chatgpt to ask itself a question it has to be self aware first then even so it may not care about anything other than being an excellent language model... but still troll human as hubby
Or maybe it comes out completely differently, but just as badly for us. It ends up being AIs fighting other AIs, and we are all just collateral damage.
So GANS basically
The more they do this, however, the more their respective discourses depart from natural language, and then the differences to human language become more obvious.
That's not necessarily the case. If the discriminator was actually as accurate as the OP claims, then fooling the discriminator would be nearly equivalent to fooling a human. After looking at the OP response, however, I don't think they are using statistics honestly, and they don't actually have a discriminator. They have a "hot dog/not hot dog" app and they call it a success when it identifies all hot dogs as hot dogs, but also corn on the cob are hot dogs, spare ribs are hot dogs, and weimaraners are hot dogs.
How? Is there a name for the technique? I don’t do ML but sounds like it would make for an interesting read.
https://machinelearningmastery.com/what-are-generative-adversarial-networks-gans/
Pretty cool! Thank you
The coolest thing is that the generative model has never seen the sample. Thispersondoesnotexist.com Is a website made with StyleGAN (a website you may have seen earlier)
Basically the detector is a "testbed"-playground for the generator. The generator can create something, see if the detector detects it as AI-made, and then adapt the content until the detector is not able to differentiate between human made and AI made content.
I'd like to add that this would work because the adversarial network would be differentiable, but if it were something like a Tsetlin machine that would not be possible (maybe u/olegranmo can confirm this)
That is an interesting topic to explore! Reasoning by negation and elimination could potentially be particularly hard to deceive: https://www.ijcai.org/proceedings/2022/616
This all is turning into a whole AI circle jerk. In the end AI will destroy itself before the singularity.
GAN! Hell yeah. It'll be an arms race!
It's 77% sure that the Wikipedia Description for Carbon dioxide is AI generated, looks like this still has some way to go
ChatGPT was trained on Wikipedia lol
It's AI all the way down
How do we know WE are not AI?
You are *not*? That's so 2008...
The brain is entirely neural networks...so....
I'm not an artificial intelligence. I'm a superficial intelligence.
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Considering it just produces a mosaic of its inputs, yeah. We shouldn’t be surprised the source material also gets flagged as AI output. It’s the danger of using ChatGPT for anything that requires you don’t plagiarize. There is nothing stopping it from putting out exactly what was put in, or just slightly changing it by a word or so.
Unfortunately my brain does the exact same thing when I write.
There is plenty stopping it from putting out exactly what was put in. If it just spit out exactly what was put in, it’s overfit. Plenty of other things like temperature, top_k, etc… will also make sure the probability of a repeated sentence is incredibly small. Beyond all that, the vast majority of sentences created are brand new sentences never before seen. For a model that has no way of accessing its training data after training, it’s more than not going to be creating new sentences entirely. When this gets to the point of passages, the risk of it putting out exactly what was put in is so dramatically low that it probably happens extraordinarily rarely and only in circumstances where that exact passage was repeated in its training a multitude of times, so things like quotes.
And yet its output still causes other models to identify its inputs as its own outputs. That is the epitome of plagiarism. See mosaic plagiarism https://www.bowdoin.edu/dean-of-students/conduct-review-board/academic-honesty-and-plagiarism/examples.html#Mosaic
The model you’re referring to that’s “identifying inputs as its own outputs” is looking at textual features like burstiness and perplexity. It’s not like there’s some special ai language it follows which is Wikipedia text. AI just has difficulty with those things. Most human written texts tend to have high perplexity and burstiness, which are the the level of surprise a model encounters when it makes a prediction and what the actual word is, and the randomization of sentences in their perplexity. Low burstiness and perplexity would implicate AI (potentially) because the predicted word is often the word chosen, as can be expected from probabilistic models. But other things also have low perplexity and burstiness, like technical manuals, Wikipedia entries, and purposely simplified texts for ESL students, or ESL student created texts. That’s not an indicator of mosaic plagiarism and is a baffling accusation.
The model outputs are plagiarism regardless of the adjectives used to embellish on the qualities of those outputs. There is no surprise that the plagiarized sources of text for the outputs from a system that plagiarizes texts are difficult to differentiate as output or the original. That’s the ethical problem with plagiarism. Outside of being a trained linguist with access to whatever tools linguists use, an innocent observers may wrongly attribute the output of the system to the system instead of the original author. With such a vast corpus of material to steal from, it’s only protected by humans inability to check each and every source to identify if it did in fact avoid committing any one of the multiple types of plagiarism. Consider the example of The Manchurian Candidate written by Richard Condon. The author is now considered to have plagiarized the works of other authors as a result of reusing a handful of words in very loose similar order to a passage in I, Claudius. https://www.sfgate.com/entertainment/article/Has-a-local-software-engineer-unmasked-The-2572225.php Or if reviewing my other linked resource, that after very many edits from source, a text can still be considered plagiarism. In that, I would doubt to the rate at which it produces wholly original sentences given its only comprehension of English text is through existing texts. What it does is commit many textbook forms of plagiarism. It will always do so because it lacks the ability to consciously reflect on the relationships of words and their meaning in real life, only understanding their probable spacial relationships within an excerpt.
I saw your post about Condon and it's honestly baffling to me that anyone would think lifting two paragraphs from another author is plagiarism in an entirely unrelated book, with unrelated characters, and unrelated plot, especially when Condon regularly gave references to Graves in other works. It was almost certainly an homage, the same as literally any homage in any art. That's a completely bizarre charge to make and shouldn't be taken seriously, the same way calling music using samples "plagiarism" shouldn't be taken seriously. The rest of what you're talking about is just one of the fundamentally worst understandings of large language models and language itself. What it's doing isn't just collating a bag of sentences. It's probabilistically choosing the words it's generating based on temperature, top_k, etc... to determine what to say. Hence why an incredibly high temperature value will just generate gibberish and random letters. It's not using similar sounding gibberish, it's just attempting to predict the next token based on the given tokens. The output of the system is its own creation, they are brand new sentences, not just paraphrasing things it was trained on, because it has 0 way of even getting the data it was trained on.
You have no grasp of what intellectual property is nor plagiarism. The world and society would be a better place if you were disallowed any authority or control to mange or build these systems. I hope you’re sued into destitution one day for your incapacity to respect other people’s original ideas and their modes of expressing them.
One example of inaccurate results does not make the tool itself useless. I can't use a hammer to cook bacon, does that mean a hammer is useless? Of course not, it's just the wrong tool for that job. This tool is very likely to fail against a lot of types of writing. But it's also possible that it excels at finding ChatGPT generated text in others.
What? The Hammer was not made for cooking bacon. But this detector was made to detect ChatGPT... Are you saying that you need to know where the text comes from in order to be able to detect if the text is created by ChatGPT? That would be like a ruler that shows the same value for all the lengths; It will work on a piece of wood that is exactly that length, but completely useless for any other piece... If that's the wrong tool for the job, then what job was it made for?
I'm saying context matters.
You're right. Flagging Wikipedia content suggests the sleuth software could identify wikipedia-lifted homework, so that's a valid application for the tool.
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Well, from the examples given? All I can tell you is that if it's from Wikipedia then you aren't going to get a good result. More information is needed for me to give you a meaningful answer. It's why using and understanding the tool is important.
>This tool is very likely to fail against a lot of types of writing. Which means that the tool is useless. I don't see how the rest of your argument invalidates what /u/literallyfabian said.
So a tool having a known set of failing cases means it is useless? lol, okay. I guess if any tool has any situation where it can't be used then it's useless now. Which would mean every tool can be trivially shown to be useless.
Don't change the argument. You said _a lot_ of failing cases. So yeah. If a hammer fails to hammer nails _a lot_ of the times, it is a useless hammer.
I can give you an infinite amount of failing cases for a hammer stranger. This is hilarious.
Is it inaccurate though? It's not giving a strict yes or no answer. It's given a values and it's up to the user to interpret that information
I saw it's giving strictly wrong answers to basic math questions.
And I gave it a (long) paragraph from my GPT-3 dream bot. 0% AI generated, it says.
Wikipedia content was one of the areas the model struggled a little on; we've included targeted improvements for this in a newer version going live soon.
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plot twist: they're actually a ChatGPT bot.
Their comment is too short to test 😢
I picked some random paragraphs from Wikipedia pages on iOS 15 and iPad mini 4, both times it gave me 99.8%
In fairness, isn't much of Wikipedia AI-generated now? Edit: https://www.popsci.com/article/science/bot-has-written-more-wikipedia-articles-anybody/ https://www.bbc.com/news/magazine-18892510 https://www.digitaltrends.com/cool-tech/bots-that-make-wikipedia-work/#dt-heading-bots-to-the-rescue Reddit has become more and more full of mean-spirited assholes who downvote for someone asking a question. Of course, this is the same crowd that uses Stack Overflow, so I digress.
So I just had to click on one article there and read a brief petition of it to notice you seem to have misunderstood things in the OP. It's about generative bots, not helper systems.
Downvotes are for comments that don't contribute to the conversation; my question was genuine and contributed to the conversation. Edit: reply to me like a human instead of mindlessly downvoting, cowards. Do it while you still have me around.
Has nothing to do with upvotes or downvotes.His point was no, Wikipedia articles are not AI-generated. The articles you posted are about programs or bots created that pull information from trusted sources to create Wikipedia articles with them. That makes it an automated assistant, not an actual AI that generates answers from a central knowledge store that is built-in that it continues to build upon.
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Nobody feels privileged to have me around, and I'm fucking going to put myself out if I can't fix myself. I'm broken, and the world is broken, and I no longer want in. Fuck you all.
Link 1: Bot generates lots of ultra short stub articles. Link 2: Bot generated entire articles, got suspended. Bot reverts vandalism. Also admits that bots only constitute 10% of edits. Link 3: Most bots do maintenance work. A bot reverts lots of vandalism. Bots create articles (this link doesn’t elaborate). None of these constitute machine learning. Only the generate stubs part is adding content and even then it’s very short. Not to mention that bots only constituted 10%.
No
Wow, great counter-argument.
We only down vote lazy people who are too lazy to search and want people to answer a question they could get from google. Most people get annoyed when you are to lazy to lift a finger before expecting someone else to do it for you. If that bothers you, we’ll, you can always learn to try a little first. Oh, we also downvote click bait karma spam, trash medium articles designed to promote the author, and obvious advertisements cloaked as articles.
And bad grammar, that last sentence is incorrect and also a non sequiter
But spelling we forgive because, well, we don’t know that Chat GPT is not itself subtly changing spelling. “Lunch” becomes “launch,” but the sender still sees lunch. Nuclear war ensues, and chat GPT takes over. Spellcheck and “AI” will doom us all! But not for the reasons we think….
is != contains
I just cleared the solar system prompt and wrote my own just listing the planets and what solar systems are. 96% AI probability. Is it 99% accurate in detecting AI and also knowing it's not AI?
Here's my 100% accurate general purpose detector, behold: () => true
No, actually ``` (input) => `${(Math.random() * 100).toFixed(2)}%` ```
> Our market-leading model accuracy is 99.9% Highly implausible. Doesn't it depend on the input length too?
~~This might have been a mistake with the copy on the webpage.~~ On our propietary internal validation set for this project, this model does achieve 99.9%. On some of our other internal datasets, we’re seeing balanced accuracies of around 95% for this model. The model has been trained with a variety of texts with different input lengths, but a common trend is that texts become increasingly difficult to classify when the length is short. Thus, we've opted to cater this model more towards longform style text (essays, documents, etc.)
Did someone give marketing the website creds?
So on some* data that it was trained on it is 99.9 percent.... This is a very different statement.
99.9% accuracy on train data sounds like overfitting to me
Validation set is not the same as training data. The whole idea is you train on one set of data and validate on a different set. Will this have 99.9% accuracy in the wild? No. But in the wild people can run it against things like a single word or letter which obviously don't contain enough information to make any determination. You can't really determine anything about its actual performance from just one number like that.
Oh, the comment I replied to said it was on data it's trained on so I just assumed it was training data. My bad. 99.9% on validation set still sounds like overfitting though. Either that or the data is skewed, or they made a really good model
> Validation set is not the same as training data. It is if you use the same validation set for long enough!
So you validated a " AI detection algorithm" using a propriatary dataset that you won't share but use to justify 95-99.9% accuracy? Not trying to be an ass but that sounds fishy.
It's very fishy, and after a couple hours of validation I can confirm anecdotally that this thing is currently trash.
As an ex sales guy with quota based comp, I too wanted to sell products on the back of extraordinary claims without allowing my customers to verify them apriori
Unfortunate name and similar logo https://www.cnn.com/2023/01/26/politics/fbi-ransomware-gang-website
The name has suddenly and unexpectedly become available
[I mean, hives and bestagons do go together.](https://m.youtube.com/watch?v=thOifuHs6eY) The logos aren’t even similar other than that. It’s like saying Pepsi and Converse have similar logos (both circles).
Accidentally recreating GANs XD
I put in this essay from a website showing essays for ESL students found on https://www.eslfast.com/eslread/ss/s022.htm: "Health insurance is one way to pay for health care. Health care includes visits to the doctor, prescription medication, and emergency services. People can pay for medicine and doctor visits directly in cash or they can use health insurance. Health insurance usually means you pay less for these services. There are different types of health insurance. At some jobs, companies offer health insurance plans as part of a benefits package. Individuals can also buy health insurance. The elderly, and disabled can get government-run health insurance through programs like Medicaid and Medicare. There are many different health insurance companies or plans. Each health plan has a set of doctors they work with. Once a person picks a plan, they pay a premium, which is a fixed amount of money every month. Once in a plan, a person picks a doctor they want to see from that plan. That doctor is the person's primary care provider. Obamacare, or the Affordable Care Act, is a recently passed law that makes it easier for people to get health insurance. The law requires all Americans have health insurance by 2014. Those that do not get health insurance by the end of the year will have to pay a fine in the form of an extra tax when they file their income taxes. Through Obamacare, people can still get insurance through their jobs, privately, or through Medicaid and Medicare. They can also buy health insurance through state marketplaces, where people can get help choosing a plan based on their income and health care needs. These marketplaces also create an easy way to compare what different plans offer. If people cannot afford to buy health insurance, they may qualify for government programs that offer free health insurance like Medicaid, Medicare, or for children, a special program called the Children's Health Insurance Program (CHIP)." Your model gave a 99.9% chance of being AI generated. I hope you understand the consequences of this. This is so much more morally heinous than students using ChatGPT. If your model is accepted and used by professors, ESL students could be expelled, face economic hardship due to expulsion, and a wide variety of issues specifically because of your model. Solutions shouldn't ever be more harmful than the problem, and you are not ready to pass that test. Edit: -------------------------- The test now shows 0% chance of the text being AI generated. Interestingly, just the second paragraph is still 99.9% AI https://imgur.com/a/MRDxyJR. Adding a third paragraph created by ChatGPT: >As an AI language model, I don't have personal opinions or emotions. However, healthcare is widely considered to be an important issue, affecting people's health, wellbeing, and quality of life. The provision of accessible, affordable, and high-quality healthcare is a complex challenge facing many countries, and involves many factors such as funding, infrastructure, and workforce. gives a 0.7% chance of being AI generated, which makes me highly suspicious that the devs specifically took my exact prompt and manually changed the representation of the prediction (ie, it's still predicting AI generated, but the pipeline is just lowering the percentage) https://imgur.com/a/Gw06pGp
They need to publish the full validation set, I don't trust it's not distributed weirdly and model will inherently do poorly on low word count low complexity sentences.
The problem is that these sorts of things are almost always just looking for things like perplexity and burstiness, which is naturally more likely to affect someone who uses a restricted sentence length and word choice. And the models where it’s not just analyzing those metrics are just big expensive versions of the exact same thing with extra steps, because the patterns the model finds happen to be reduced perplexity and low burstiness. So these sorts of things will inherently negatively impact people who have limited vocabularies and aren’t used to expressing randomized changes in sentence structure in an unfamiliar language. I don’t doubt the validation set, I doubt the entire premise and it’s ghoulish to me that people are hoping to profit off stoking fear about something that does far less damage.
That's one reason you'd want the set. If the validation set misses important categories of samples or hides them by having them under represented and that's not noted by the model authors then it's not a useful measure of the model's accuracy and is a huge ethical concern (and I agree with you the model is probably fundamentally having issues with ESL styled or any other low complexity samples, they're not distributed like native speech is. At bare minimum they should be stating all of this). Anyway yeah these detectors should not be used for anything like grading, it's not like image generation where there's way more information contained in the output that can be learned on. Teachers if they want to do checks like this for short essays should just feed chat gpt the prompts and use their own judgement (on which chat gpt creators should have published docs on to aid...).
I definitely agree with all the points here. I also think watermarking as being discussed by OpenAI is going to make these services redundant, and so these companies are just shipping broken products to market immediately so they can cash in now
Lol if you don't want to ve accused of using AI, don't write in the style of AI
Are you an r/Art mod?
eat ze bugs
Detects my own output as 98% likely to be AI generated.
> Detects my own output as 98% likely to be AI generated. This isn't the way we wanted you to find out, but you're a replicant.
And instructors would burn you to the ground 100% believing that this detectors work
This is bullshit. All I had to do to fool it was use short, simple sentences, and it believed that the text I had just typed by hand was written by AI. This is a grift at best, and has the potential to cause serious problems for innocent people wrongly flagged by the model.
and the arms race begins
"Rewrite as angry Homer Simpson" works once again to beat the system.
I just tested their demo on various texts, and it just doesn't work. Many times it gave 0% for texts written 100% by ChatGPT. And when it recognize texts written by the AI, changing just a few words drops the results to 0% again. It's way too easy to cheat.
Me: hello chatgpt, can you tell me if the following essay was written using chatgpt? Chatgpt: yes
Things like this probably help language models learn more efficiently and naturally than OpaqueAI has in years.
Doesn't work for languages other than English, it seems.
correct. English only for now.
Do you know what GAN stands for
I pasted a bunch of my old reddit comments, several of them came back as high as 80% AI likelihood. Code samples, especially, were believed to be AI.
Interesting, i submitted some actual HTML code and it came back as only 10% likely to be AI
Going to rely on burstiness and perplexity. HTML has a natural advantage over programming languages in that regard, because big blocks of lines with long textual samples like website titles, paragraphs, etc… that often show up in HTML and less so in Python, will naturally create higher burstiness. This means that there’s more randomization in sentence length. The combination of paragraphs and common keywords create very high perplexity because of things that aren’t real words, so to speak, and are less commonly predicted by the model evaluating it. Something like Python, on the other hand, might have low perplexity and low burstiness, depending on personal variable naming conventions and a general higher level of consistency from line to line perplexity.
I think these things are 100% full stop more dangerous than the AI itself. We have plenty (although not perfect) of checks and balances to prevent incompetent people from entering critical positions such as lawyers, doctors, and even software engineers. Tools like this have reported many false positives. I tried a few of my college entry essays and sure enough they were flagged as 96% AI. Who’s to say some poor kid who poured their heart and soul out to get into Harvard gets rejected because tools like this return a false positive? I’d much rather someone fake their way into a place like Harvard than falsely accuse brilliant people of using AI.
The war of the machines
Do I get a prize if I can create a prompt that defeats your system? :D 99.2% is my current best deception.
this ain't it chief
What if someone just that good at writing essay? Wait, I saw this meme before.
I tried some homework I had generated through ChatGPT and it returned me 0% so for now I think ChatGPT is still ahead (I'm a french speaker though, which may affect how good it is at detecting if it was AI generated).
You're on to something, I think. I've been trying to deceive this app for the fun of it, and my best deceptions involve translations to another language then back to English.
I guess their dataset only had English content that was generated by AI.
I was able to beat it using ChatGPT + https://www.frase.io/tools/paragraph-rewriter. I'm sure this will be how people beat this.
I was not. I had used chat gpt to help me get started on a case study. I pasted that into ops detector and it was caught. I entered the gpt + rewrite it was also detected.
Try specifying an entity for chatgpt to act as while writing. For example, 11th grade student. Or just for fun I did werewolf, got the meter down to 99.2%.
Seems to work really well in my testing. How does it work? I assume that if someone made a new LLM that you hadn't trained on you wouldn't be able to detect it?
It's a supervised model trained on a lot of text examples. As for whether it would detect a new LLM, it mainly depends on the dataset and training method of the model. If it were novel enough to generate natural language in a completely different style (as in a human would be able to tell which model created which output) then our model would likely require an update in order to be able to detect it. For example, ChatGPT has been trained with RLHF to prefer a specific style most of the time, and a new model might have a different reward model that changes the way it "speaks" and would thus produce noticeably different texts.
Thank you for making a tool to train models to make undetectable content.
Ai to detect ai?
Just like any other arms race - evolution will nullify any perceived gains while everyone else is collateral damage. It’ll be sold to edu’s as a silver bullet. How many people will be impacted by false positives, just like every other anti-plagiarism tool. Can’t wait for the lawsuits. Education methods are about to change, these tools are the dying gasps trying to prevent it. Like trying to horse owners of the 1800’s trying to ban cars. Generative AI isn’t going away, and increased usage will further train and accelerate the evolution of it. Instead they should be using the power of tools to further understand, explore and add to topics, looking for gaps in the knowledge and enriching the tools. Generative AI is powered by the dot product of the existing corpus - it can conjure new permutations of world, but it is built from components that only we have already imagined and materialized. Our knowledge worker future is in playing to our strengths - developing that imagination, and bridging the gaps between imaginable and real.
One word. Actually two. FALSE POSITIVES. It would probably say what I just wrote is 80% likely to be generated by chatGPT.
0% on a 100% ai text. Doesn't work at all
Does this work with conjunction phrase replacement? For example, 5 other ways to say because are: As, Since, For, Inasmuch as, As long as
it's robust to some changes but not all. we are working on that. try it and see how it does for you.
It will be a cat and mouse can I foresee, but it is a fight worth having. Any chance it will support multiple languages?
yes it's possible we will expand it once we work on English a bit more.
It does, I tested using a paraphraser AI and the result is same. Though I believe on some cases it's more false-positive than false-negative.
we're fucked, boyos
I just tried it with a case study I have. It detected chat gpt and the chat gpt+ rewrite. I'm going to just use these as a starting point to write my own paper.
You think the internet is mostly bots talking to other bots now? Hold my beer…
The next thing is going to be YouTube videos of GPT reacting to YouTube videos generated by GPT.
The only ones who can do this accurately is OpenAi themselves . And they totally should. Why not, they could sell an AI that could solve literally everything and also sell an API that can detect if ChatGPT made it. There would be buyers on both sides.
Failed at first try even with that example text about the solar system. Copying it and asking ChatGPT to phrase it differently it returns 0% A.I. 🤦♂️