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MOD NOTICE- "I ASKED AI [SOMETHING ABOUT AUDIO]- WHAT DO YOU THINK?" TYPE THREADS. PLEASE DON'T

Jimbob54

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We are seeing more of the above type threads. Please, do not create such threads or even respond to them.

ASR isnt here to fact check AI nor help their models improve. If you (the human you) have an interest in an area of audio tech you want to know about, by all means ask an AI, do a search on Google or whatever, search for threads here and other audio forums. And by all means create a thread on here to ask based on your own research.

But from now on all such threads will be dumped into this thread. https://www.audiosciencereview.com/...g-about-audio-what-do-you-think-thread.60475/

Thanks

The Mods.
 
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I agree!
But sometimes I find it interesting to see AI answers to get an idea of the quality of an AI.
There are now so many AI chatbots that you lose track. Which one should you trust?
 
Thank you!
 
I asked ChatGPT about this...

Under the premise that the majority of training data for large language models is derived from the internet and that as time passes an increasing fraction of all internet content has been generated by LLMs, what are the ultimate implications for future LLM accuracy in such a circular system?

This scenario—where LLMs are increasingly trained on data generated by other LLMs—leads to a phenomenon often referred to as model collapse or recursive degradation. The core issue is that, over time, the quality, diversity, and accuracy of the training data degrade due to a feedback loop of synthetic content. Here are the key implications:


1. Loss of Originality and Diversity


  • Initially, LLMs are trained on a mix of high-quality human-generated data and some lower-quality content.
  • As more internet content is generated by LLMs, future models will be trained on a dataset that contains a progressively larger proportion of their own outputs.
  • This leads to a narrowing of language diversity, stylistic homogenisation, and a loss of nuance found in human-generated text.

2. Accumulation of Errors and Biases


  • Errors or biases in early LLM-generated content will be reinforced and amplified when used as training data.
  • Even small inaccuracies will compound over generations, leading to systematic distortions in knowledge.
  • Biases inherent in the original training data (or introduced by alignment processes) may become exaggerated over iterations.

3. Semantic Drift and Degradation of Meaning


  • Without a fresh influx of high-quality human data, meanings of words and concepts can subtly shift.
  • LLMs rely on statistical associations rather than true understanding, so repeated training on synthetic outputs may result in linguistic degradation—phrases may become stilted, overly generic, or lose their original meaning.
  • This mirrors real-world linguistic drift but happens artificially and without cultural intent.

4. Reduced Reliability and Increased Hallucination


  • Since LLM-generated content often lacks rigorous fact-checking, training future models on this data can increase the frequency of hallucinations.
  • Over time, models may become increasingly detached from reality, generating plausible-sounding but entirely false information with greater confidence.
  • This could create an internet ecosystem saturated with unreliable AI-generated text, making it harder to extract verifiable facts.

5. Diminishing Returns and Stagnation


  • As training data becomes more synthetic, the marginal improvement of new LLM generations decreases.
  • Current AI development relies on massive and diverse datasets—if these become increasingly AI-generated, innovation slows because the models will struggle to learn new, truly novel patterns.
  • This could lead to a plateau in LLM capabilities unless countermeasures are introduced.

Potential Solutions to Mitigate the Issue


To prevent this downward spiral, AI developers could implement several strategies:


  • Human-Curated Data Sources: Continuously collecting and prioritising human-generated text for training.
  • Synthetic Data Filtering: Developing mechanisms to detect and remove AI-generated content from future training datasets.
  • Hybrid Approaches: Combining AI-generated data with verified human-written text to maintain diversity and quality.
  • Fact-Verification Models: Enhancing AI’s ability to fact-check itself and rely on external trusted sources rather than past AI-generated content.
  • Regulation and Ethical AI Use: Enforcing standards for labelling AI-generated content and controlling its proliferation online.

Conclusion


Without intervention, future LLMs risk becoming increasingly self-referential, less reliable, and less useful. The challenge will be ensuring that AI-generated content does not dominate training data, allowing models to retain their accuracy, creativity, and factual grounding.

The above is intended as a joke by the way. I agree with new the policy. The majority of threads of this type don't seem to realise that it is very easy to bias the question to get whatever answer you want.
 
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What would happen if you asked an AI to ask a question as a reply to a question and then feed that question to another AI and put that reply into an AI again etc.
It will keep them busy.
 
What would happen if you asked an AI to ask a question as a reply to a question and then feed that question to another AI and put that reply into an AI again etc.
It will keep them busy.
They decide we are yanking their chains and obliterate us? ;-)
 
Will the threads already started and doing just that be dumped? There are some very big names associated with some.
 
Will the threads already started and doing just that be dumped? There are some very big names associated with some.
Such as ? Not a bait , just interested
 
A few AI treads have started by Amir with him putting info in to see if was correct and I have contributed posts with interest.

I have participated in this study for a year now https://lmarena.ai/
 
A few AI treads have started by Amir with him putting info in to see if was correct and I have contributed posts with interest.

I have participated in this study for a year now https://lmarena.ai/
Messing with Amir's threads might result in him docking my wages.....
 
Messing with Amir's threads might result in him docking my wages.....
Haha I think what you are doing is good but I worried about those who might think there is a double standard, like me telling others to lose weight.
 
Haha I think what you are doing is good but I worried about those who might think there is a double standard, like me telling others to lose weight.
Pm me links to those threads please . They didn't pop up on my search so perhaps we are at cross purposes .
 
AI doesn't know anything humans don't already know. Its responses will be based on training. I 100% welcome ASR's decision to ban AI response threads. Nothing impedes people interested in those to pursue that on their own and contribute to train those models all by themselves.
 
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