When AI Eats Itself: On the Caveats of AI Autophagy
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866915009261469696 |
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| author | Xing, Xiaodan Shi, Fadong Huang, Jiahao Wu, Yinzhe Nan, Yang Zhang, Sheng Fang, Yingying Roberts, Mike Schönlieb, Carola-Bibiane Del Ser, Javier Yang, Guang |
| author_facet | Xing, Xiaodan Shi, Fadong Huang, Jiahao Wu, Yinzhe Nan, Yang Zhang, Sheng Fang, Yingying Roberts, Mike Schönlieb, Carola-Bibiane Del Ser, Javier Yang, Guang |
| contents | Generative Artificial Intelligence (AI) technologies and large models are producing realistic outputs across various domains, such as images, text, speech, and music. Creating these advanced generative models requires significant resources, particularly large and high-quality datasets. To minimise training expenses, many algorithm developers use data created by the models themselves as a cost-effective training solution. However, not all synthetic data effectively improve model performance, necessitating a strategic balance in the use of real versus synthetic data to optimise outcomes. Currently, the previously well-controlled integration of real and synthetic data is becoming uncontrollable. The widespread and unregulated dissemination of synthetic data online leads to the contamination of datasets traditionally compiled through web scraping, now mixed with unlabeled synthetic data. This trend, known as the AI autophagy phenomenon, suggests a future where generative AI systems may increasingly consume their own outputs without discernment, raising concerns about model performance, reliability, and ethical implications. What will happen if generative AI continuously consumes itself without discernment? What measures can we take to mitigate the potential adverse effects? To address these research questions, this study examines the existing literature, delving into the consequences of AI autophagy, analyzing the associated risks, and exploring strategies to mitigate its impact. Our aim is to provide a comprehensive perspective on this phenomenon advocating for a balanced approach that promotes the sustainable development of generative AI technologies in the era of large models. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2405_09597 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | When AI Eats Itself: On the Caveats of AI Autophagy Xing, Xiaodan Shi, Fadong Huang, Jiahao Wu, Yinzhe Nan, Yang Zhang, Sheng Fang, Yingying Roberts, Mike Schönlieb, Carola-Bibiane Del Ser, Javier Yang, Guang Machine Learning Artificial Intelligence Generative Artificial Intelligence (AI) technologies and large models are producing realistic outputs across various domains, such as images, text, speech, and music. Creating these advanced generative models requires significant resources, particularly large and high-quality datasets. To minimise training expenses, many algorithm developers use data created by the models themselves as a cost-effective training solution. However, not all synthetic data effectively improve model performance, necessitating a strategic balance in the use of real versus synthetic data to optimise outcomes. Currently, the previously well-controlled integration of real and synthetic data is becoming uncontrollable. The widespread and unregulated dissemination of synthetic data online leads to the contamination of datasets traditionally compiled through web scraping, now mixed with unlabeled synthetic data. This trend, known as the AI autophagy phenomenon, suggests a future where generative AI systems may increasingly consume their own outputs without discernment, raising concerns about model performance, reliability, and ethical implications. What will happen if generative AI continuously consumes itself without discernment? What measures can we take to mitigate the potential adverse effects? To address these research questions, this study examines the existing literature, delving into the consequences of AI autophagy, analyzing the associated risks, and exploring strategies to mitigate its impact. Our aim is to provide a comprehensive perspective on this phenomenon advocating for a balanced approach that promotes the sustainable development of generative AI technologies in the era of large models. |
| title | When AI Eats Itself: On the Caveats of AI Autophagy |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2405.09597 |