On the Challenges and Opportunities in Generative AI
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Date
2024-02-28
Publication Type
Working Paper
ETH Bibliography
yes
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Abstract
The field of deep generative modeling has grown rapidly and consistently over the 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 do not sufficiently address several fundamental issues that hinder their widespread adoption across domains. In this work, we aim to identify key unresolved challenges in modern generative AI paradigms that should be tackled to further enhance their capabilities, versatility, and reliability. By identifying these challenges, we aim to provide researchers with valuable insights for exploring fruitful research directions, thereby fostering the development of more robust and accessible generative AI solutions.
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Publication status
published
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Journal / series
Volume
Pages / Article No.
2403.00025
Publisher
Cornell University
Event
Edition / version
v1
Methods
Software
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Date collected
Date created
Subject
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); FOS: Computer and information sciences
Organisational unit
09682 - Cotterell, Ryan / Cotterell, Ryan
09670 - Vogt, Julia / Vogt, Julia
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Related publications and datasets
Is previous version of: https://doi.org/10.3929/ethz-c-000786864