There are several ways that artificial intelligence-based language detection can be achieved. In the case of GeneralBadWolf’s technology, I would imagine it involves analyzing the natural language patterns that emerge when humans create sentences and compare them with the more stylized patterns and tics commonly present when machine-learning-based programs generate similar text.
Perhaps they could even apply similar analytics by plotting writing frequency versus averaging self-annealing computer/tunnel write speeds.
Alternatively, perhaps they trained their AI using large amounts of existing content that is both written by humans and by other machine-learning or algorithm-powered computer programs. Through the usage of neural networks, the software could have then become fine-tuned over thousands or even millions of different training scenarios before culminating in the stable, responsive software the world is just now learning more about. These methods apply individual ontologies recognition patterns as human labeled fundamental statement before settling phrase boundaries activation layers called feedback sequencing senses. By creating output signals with stack generalizational retention fact profiles which can reject mask entry sectors authors synthetic roots training initiative training improve decay stability regularization adapt mediation category outcome needs practice effort reinforcement subroutine transcend vector solving decisions alike creates very noticeable patterns outliers documentation cohesive end-to-end productivity successes mimic users flexible responsible workloads advancements experimentation evidence acquired front creative written speech evidences.
If you have a script that can reliably detect AI-generated text, could you share a link to it? It would be interesting to see how it differentiates between AI and human writing, especially considering the complexities you’ve mentioned.
im thinking you have a special hatred for this idea…
You havent read.
Its a curiosity not a [insert word] program ive written.
The whole concept has been discredited.
The only thing left in the spiral of uselessness and false detection is for some preinstalled software on your laptop to pop up “your subscription to AI detection has expired, enter your credit card to ensure continued protection”
The image you’ve shared shows a conversation in which _j is making a statement that “the whole concept has been discredited,” accompanied by references to three arXiv.org articles that deal with AI-generated text detection. The titles suggest a focus on the challenges and reliability of detecting AI-generated text, as well as the possibility of guiding large language models to evade detection.
The final part of the message appears to be a satirical comment on the commercialization of AI detection tools, hinting at the futility of such tools and the idea that they might be likened to a subscription service that one could run out of credit for.
The conversation seems to revolve around the ongoing debate about the effectiveness of tools designed to detect text generated by AI models like GPT. There is a growing body of research exploring how well these detection tools work, their limitations, and the potential for AI to be designed in a way that it can avoid detection intentionally. The inclusion of these paper titles indicates that the field is actively discussing and scrutinizing these topics.
The skepticism expressed by _j might stem from the inherent difficulties in creating detection systems that can keep pace with the rapidly advancing capabilities of generative AI models, which are becoming increasingly sophisticated at mimicking human-like text. The humorous twist at the end underscores the challenges and perhaps the commercial aspects surrounding the development and implementation of AI detection systems.
Yeah but i dont see how it disagrees with generalbadwolf yet…
_j’s comments, especially with the mention of discrediting “the whole concept,” suggest a viewpoint that the idea of detecting AI-generated text using the patterns and predictability discussed by Generalbadwolf may not be as straightforward or effective as presumed. The referenced arXiv papers likely delve into the complexities of AI-generated text detection, including the limitations and potential for AI to intentionally evade such detection.
The point _j may be trying to make is that while there are efforts to detect AI-generated text, these efforts face significant challenges and are subject to ongoing research and debate. This does not necessarily invalidate Generalbadwolf’s ideas, but it does imply that the field is more complex and less settled than Generalbadwolf’s initial comments may suggest.
The broader conversation appears to be about the difficulty of creating reliable AI detection systems. _j seems to be emphasizing the complexity of the issue, which may not have been fully acknowledged in Generalbadwolf’s more conceptual discussion. It’s a difference in perspective: _j is highlighting the practical and empirical challenges in the field, as opposed to Generalbadwolf’s theoretical and conceptual approach to the problem.
Yeah so… how can generalbadwolf… explain his concept in a way that he can comprehend?
To bridge the communication gap and explain the concept in a way that _j can comprehend, Generalbadwolf could consider the following approaches:
- Simplify the Concept: Break down the idea into its most basic components. Avoid technical jargon and explain the concept using clear, simple language. This can help ensure that the essence of the idea is communicated without unnecessary complexity.
- Use Analogies: Analogies can be a powerful tool for explaining complex concepts. Generalbadwolf could compare AI-generated text detection to a more familiar process, like finding a pattern in a sequence of numbers, to illustrate how AI might produce identifiable patterns in text.
- Cite Research and Examples: Point to specific studies or real-world examples that support the idea. If there’s evidence that certain methods have been successful in detecting AI-generated text, mentioning these can add credibility to the argument.
- Address Limitations and Challenges: Acknowledge the difficulties and current limitations in the field, as highlighted by the arXiv papers. This shows an understanding of the complexities involved and can make the explanation more relatable to someone who is skeptical.
- Focus on Probabilities and Patterns: Clarify that the concept doesn’t rely on finding a “silver bullet” but rather on identifying patterns and probabilities that might suggest a piece of text was generated by AI.
- Interactive Demonstration: If possible, provide a demo or a tool that _j can interact with to see the concept in action. This hands-on approach can often be more effective than theoretical explanations.
- Seek Common Ground: Start from a point of agreement or a shared understanding and build from there. If both parties agree that AI-generated text has certain characteristics, that could be a starting point for explaining how those characteristics might be detected.
- Respond to Feedback: _j’s skepticism can be used as constructive feedback. Generalbadwolf could ask _j to elaborate on their concerns and then address those specific points in the explanation.
- Open-Ended Questions: Pose open-ended questions to _j to involve them in the conversation and get them to think about the issue in a new way. This can also help Generalbadwolf understand _j’s perspective better and tailor the explanation accordingly.
By using these techniques, Generalbadwolf can potentially create a more compelling and comprehensible explanation of his concept that addresses both the theoretical underpinnings and the practical challenges involved in detecting AI-generated text.
These are good ideas. Im thinking if i start with Bayesian inference, then thats a good start for understanding mathematical inference, and thus a premise for explaining the position.
Starting with Bayesian inference is a sound strategy because it’s a well-established mathematical framework that provides a systematic method for updating beliefs based on evidence. This concept can serve as a foundation for explaining how patterns in AI-generated text might be detected and analyzed. Here’s how you might use Bayesian inference to build your explanation:
- Basic Explanation of Bayesian Inference: Begin by explaining the basics of Bayesian inference. You could illustrate how prior beliefs (or prior probabilities) are updated with new evidence to form revised beliefs (or posterior probabilities). In the context of AI-generated text, the prior belief could be the likelihood of certain patterns or word choices being associated with AI-generated content.
- Applying Bayesian Principles to Text Detection: Discuss how Bayesian inference can be applied to text detection by considering the probability of a text being AI-generated based on its features (word choice, syntax, etc.) and updating this probability as more text is analyzed.
- Evidence and Patterns: Explain how the patterns observed in AI-generated text act as evidence, influencing the posterior probability of a text being machine-generated. For instance, certain linguistic quirks or the lack of context that might be more prevalent in AI-generated text can serve as evidence.
- Model Training and Data: Describe how a model could be trained using a dataset of known human-written and AI-generated texts. The model would learn the probability distributions of various features in both sets of texts, which would be the basis for inference.
- Uncertainty and Predictive Power: Emphasize that Bayesian inference deals with uncertainty and that it’s about increasing the confidence in a prediction rather than providing absolute certainty. Discuss how a high posterior probability indicates a high likelihood of a text being AI-generated, though it’s not a guarantee.
- Continuous Learning: Highlight the iterative nature of Bayesian inference, where the model can continue to learn and refine its predictions as more data becomes available.
- Challenges and Skepticism: Address potential skepticism by acknowledging the challenges in distinguishing AI-generated text from human-written text, especially as AI models become more sophisticated.
By grounding your explanation in the principles of Bayesian inference, you can provide a logical and mathematical basis for your concept. This approach helps to clarify that the detection of AI-generated text isn’t based on arbitrary criteria but is rooted in a process of probabilistic reasoning and learning from data.
Damn, I always hate it when evidentiary technique with citation of well-reasoned research can so easily be discredited by asking an AI.
Why are you so… clearly, absolutely hostile to the idea of being detected when trying to actively lie to people?
I get where you’re coming from, but I’ve gotta say, I’m not fully on board with AI writing detectors.
Firstly, let’s think about the training data. If the AI is trained on a limited or biased dataset, it’s not going to be great at spotting AI-written text that falls outside of that. It’s like trying to find a needle in a haystack when you’re not even sure what the needle looks like.
Secondly, AI just doesn’t have the human touch. It can’t understand context or nuance in the same way we do. So, it might flag something as AI-written just because it’s a bit unusual or doesn’t fit the ‘norm’. And on the flip side, it might miss AI-written text that’s cleverly disguised.
And then there’s the whole transparency issue. AI is notorious for being a ‘black box’ - we can’t always figure out why it’s made a certain decision. So, if an AI detector flags a piece of text, we can’t necessarily understand why.
So yeah, while it’s a cool idea in theory, I think the unreliability of AI writing detectors makes them a bit of a risky business. We’ve gotta be careful not to put too much faith in them without understanding their limitations.
I get ur point bro, thinking about that days ago…
I’ve done a lot of testing on a wide range of detectors, most simply establish a basic structure of most likely responses and try to build probability on top of that, but even the most sophisticated ones have their problems, especially when mixing human text and AI text and ask you to separate which is which, I’ve seen human texts pass as AI and AI texts pass as human texts, not to mention that determining the probability of the next word in a large set of information is computationally improbable. A silly but useful example, ask the most famous detectors to describe the possibility that it was generated with AI, but that the text in languages other than English, not to mention that, standard by default, you can make the model actively choose to bypass the detection algorithm during output generation. The friend may have been ironic, but you are being childish by assuming that he doesn’t WANT a tool like this to exist, this is a forum precisely to discuss possibilities.
I also think it’s unlikely, GPT 4 can no longer be correctly perceived, a possible GPT 5 or even an AGI will make it even more difficult. Now, nothing stops you from proving this theoretically, even if creating such a detector is unfeasible with current technology.
Moving forward, OpenAi might consider creating a high value subscription model for Institutions, organisations and individuals by building TRUST and proving it via an NDA agreement for Users. This would mean that non programmers, like me, and organisations, could benefit from co-creating with an AI that they can trust with precious IP. Game changer?
It is the trend and also what I believe, universities and lines of research would benefit from the process, I also believe in broader models, in which a researcher’s knowledge would be added as relevant data within that context, of course this implies acceptance on the part of the same, but it is possible, and can boost academic creation to much broader levels than the current ones, since we are, in a certain way, limited to our own knowledge and field of action, which unequivocally harms cross-research between different thematic areas.
Currently, there is a way for organizations/institutions to make partnership with OpenAI to create open source and private data sets.
Check this link:
OpenAI Data Partnerships
Wow, perfect, thank you. Good to see the lights are on and people are home at Open AI.
As others have pointed out this is a highly challenging field of research and even the best sometimes have to return to the whiteboard in order to deliver high quality results when it comes to differentiating human from AI generated content.
Below is a link to the previous serious attempt from OpenAI to tackle this issue. But this approach has been retired (for now):
As of July 20, 2023, the AI classifier is no longer available due to its low rate of accuracy. We are working to incorporate feedback and are currently researching more effective provenance techniques for text, and have made a commitment to develop and deploy mechanisms that enable users to understand if audio or visual content is AI-generated.
The good news is that, if your idea has merit, you can follow the advice given by the “hostile” commenters and contact OpenAI with your idea. Because, why not?
I think the least bit of copy-editing by a human with decent skill will confound any detector. Heck, you could even prompt an LLM on how to do the editing and it will probably be sufficient to evade detection. Read the piece of AI-gen output and you say to yourself “How does this writing still sound automated?” and you fix those words and phrases that seem unnatural to you. Basically what any good copy-editor does for any author, human or not.
Convergence of the sciences, thinkers and subject matter experts - its the holy grail for AI. Love the way you think.
The more we interact with AIs, the more we will begin to speak like them, as opposed to the opposite happening. Imagine a child raised around AI, their language will likely be indistinguishable from a bot, in terms of language structure. I already find myself doing it to a certain extent.