Javad B.’s Post

When AI Learns from AI - The Risk of Model Collapse AI models face "model collapse" when trained on data primarily generated by other AI models, leading to repetitive and predictable outputs. This study highlights the risks of relying too heavily on AI-generated content, which can bias the learning process and reduce the model's ability to capture human complexity. As AI-generated content grows, ensuring the diversity and quality of training data becomes increasingly critical for the future of AI. https://lnkd.in/gCpTynGi #AI #MachineLearning #ArtificialIntelligence #DataScience #AIethics #FutureOfAI #AIBias #Innovation #TechResearch #MachineLearningResearch

Gen AI and machine learning are transforming our world, but recent research published in Nature highlights a critical challenge we must address: The model collapse! This study, conducted by Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, Nicolas Papernot, Ross Anderson, and Yarin Gal, reveals that AI models, including large language models (LLMs), suffer from significant degradation when trained on data generated by other AI models. Key insights from the study: - Irreversible Defects: This process, termed 'model collapse', can cause the models to misperceive reality, especially in tasks where the tails of the data distribution are crucial. - Implications for AI Development: As LLM-generated content becomes more prevalent online, maintaining access to genuine human-generated data will be increasingly vital to prevent model degradation. This research underscores the importance of critically evaluating the sources of our training data and ensuring the integrity of AI models over generations. Thanks to the research team for shedding light on this phenomenon and pushing the boundaries of our understanding of AI. #AI #MachineLearning #Research #DataScience #ArtificialIntelligence #Nature #ModelCollapse #womenintech

🚨 Safeguarding AI's Future: The Power of Human Data 🚨 As we witness a surge in AI-generated content, we must address a critical question: Can AI learn from AI without collapsing? 🧠 An experiment published in Nature reveals the potential pitfalls of "circular reasoning" in AI training. If future AI models train on AI-generated data, we risk a catastrophic decline in performance. How do we prevent this? By ensuring AI systems are trained with a mix of human-generated content. Together, we can safeguard the future of AI and unlock its full potential. 🌟 Let’s collaborate and innovate for a brighter AI future. Share your thoughts below! https://lnkd.in/dUCTwSxX #ArtificialIntelligence #AI #FutureTech #Innovation #MachineLearning

♻️ AI models collapse when trained on recursively generated data - @Nature@press.coop ｢ What happens to GPT generations GPT-{n} as n increases? We discover that indiscriminately learning from data produced by other models causes ‘model collapse’—a degenerative process whereby, over time, models forget the true underlying data distribution, even in the absence of a shift in the distribution over time ｣ https://lnkd.in/dk7PaqQU #aislop #ai #llm #chatgpt #research

A new study published in the journal Nature shows that AI models trained on AI-generated material will experience rapid "model collapse." The development of LLMs is very involved and requires large quantities of training data. If the training data of most future models are also scraped from the web, then they will inevitably train on data produced by their predecessors. This paper investigates what happens when text produced by a version of GPT forms most of the training dataset of models. The authors note that access to the original data distribution is crucial and needs access to real human-produced data. In other words, the use of LLMs at scale to publish content on the Internet will pollute the collection of data to train their successors. #llm #ai #generativeai #modelcollapse #model #gpt https://lnkd.in/gNWcr_Vc

GIGO ( Garbage In Garbage Out ) at a whole new level in AI: Training artificial intelligence (AI) models on AI-generated text quickly leads to the models churning out nonsense, a study has found. This cannibalistic phenomenon, termed model collapse, could halt the improvement of large language models (LLMs) as they run out of human-derived training data and as increasing amounts of AI-generated text pervade the Internet. https://lnkd.in/gKRFKg2v https://lnkd.in/gSQux7e8

Interesting read! Stable diffusion and GPT models have revolutionized AI, transforming how we generate text and images. However, relying too much on AI-generated content can cause irreversible defects in the resulting models highlighting the need for genuine original content in training. #AI #GenerativeAI #LLM

https://lnkd.in/eY9CCjtV Learn more about "AI Model Collapses". It will be quite interesting to find out if AI Models become a virtuous or vicious cycle. The cost of training data will become more expensive and finding non-gen AI content to train will be more difficult. Slowing down or changing this phenomena without accelerating cost of effort will be a focus for certain in the near term. #AIModelCollapse

👀 As AI and especially LLMs keep evolving in speed, sophistication, and complexity, there is a new risk and challenge to overcome, and the key is that there is not too much time to solve it or at least mitigate it. In the near future the LLM training will be increasingly trained or fine-tuned with text, images and other synthetic data generated by other AI systems. 🤯 This could potentially pollute data or results with bias or wrong facts or fake facts. 🤔 What is model collapse? Model collapse is a degenerative process affecting generations of learned generative models, in which the data they generate end up polluting the training set of the next generation. Being trained on polluted data, they then misperceive reality. #ai #genai #aitrends #bias #aimodels #reality

Is your #AI snake devouring its own tail? More specifically "AI #modelscollapse when trained on recursively generated data." Interesting paper: https://lnkd.in/gDVGKH9i By Ilia Shumailov, Zakhar Shumaylov, Yiren (Aaron) Zhao, Nicolas Papernot, Ross Anderson cam & Yarin Gal Researchers evaluate: What may happen to GPT-{n} once #LLMs contribute much of the text found online. They found that indiscriminate use of model-generated content in training causes irreversible defects in the resulting models, in which tails of the original content distribution disappear. Sandeep Giri, Setu Chokshi,