On the caveats of AI autophagy
File(s)
Accepted version
Author(s)
Type
Journal Article
Abstract
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 minimize 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 optimize 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 unlabelled 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 Perspective examines the existing literature, delving into the consequences of AI autophagy, analysing 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.
Date Issued
2025-02-10
Date Acceptance
2025-01-03
Citation
Nature Machine Intelligence, 2025, 7, pp.172-180
ISSN
2522-5839
Publisher
Nature Research
Start Page
172
End Page
180
Journal / Book Title
Nature Machine Intelligence
Volume
7
Copyright Statement
Copyright © 2025, Springer Nature Limited. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Sponsor
British Heart Foundation
Commission of the European Communities
European Research Council Horizon 2020
Commission of the European Communities
Innovative Medicines Initiative
Boehringer Ingelheim Ltd
Medical Research Council (MRC)
Medical Research Council (MRC)
Imperial College Healthcare NHS Trust- BRC Funding
Wellcome Leap
EU Underwrite - EPSRC
Grant Number
PG/16/78/32402
952172
H2020-SC1-FA-DTS-2019-1 952172
101005122
101005122
PO:4700244755 Study:1199-0457
MR/V023799/1
MC_PC_21013
RDA01
104617
EP/Z002206/1