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On the challenges and opportunities in generative AI.
Trans. Machine Learn. Res. 2025, accepted (2025)
The field of deep generative modeling has grown rapidly in the last few years. With the availability of massive amounts of training data coupled with advances in scalable unsupervised learning paradigms, recent large-scale generative models show tremendous promise in synthesizing high-resolution images and text, as well as structured data such as videos and molecules. However, we argue that current large-scale generative AI models exhibit several fundamental shortcomings that hinder their widespread adoption across domains. In this work, our objective is to identify these issues and highlight key unresolved challenges in modern generative AI paradigms that should be addressed to further enhance their capabilities, versatility, and reliability. By identifying these challenges, we aim to provide researchers with insights for exploring fruitful research directions, thus fostering the development of more robust and accessible generative AI solutions.
Impact Factor
Scopus SNIP
0.000
0.000
Anmerkungen
Besondere Publikation
Auf Hompepage verbergern
Publikationstyp
Artikel: Journalartikel
Dokumenttyp
Wissenschaftlicher Artikel
Sprache
englisch
Veröffentlichungsjahr
2025
HGF-Berichtsjahr
2025
ISSN (print) / ISBN
2835-8856
e-ISSN
2835-8856
Zeitschrift
Transactions on Machine Learning Research
Quellenangaben
Band: 2025
Verlag
Journal of Machine Learning Research Inc.
Begutachtungsstatus
Peer reviewed
POF Topic(s)
30205 - Bioengineering and Digital Health
Forschungsfeld(er)
Enabling and Novel Technologies
PSP-Element(e)
G-530015-001
Scopus ID
105014820251
Erfassungsdatum
2025-10-22