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Journal of Research in Engineering and Computer Sciences
June 2023, Vol. 1, No. 2, pp. 1-9
Exploring Challenges in Applying Foundation and
Generative Models in AI
Ayse Kok Arslan
Abstract:
This study provides a comprehensive review on generative models, and basic
components both from the perspective of unimodality and multimodality. The analysis
aims to distinguish contemporary generative AI models from their predecessors. After
providing a brief historical background the study discusses the recent applications of
generative AI models, commonly used techniques in AIGC, and addresses concerns
surrounding trustworthiness and responsibility in the field. Finally, it explores open
problems and future directions for AIGC, highlighting potential avenues for innovation
and progress.
INTRODUCTION
In recent years, Artificial Intelligence Generated Content (AIGC) has gained much attention
beyond the computer science community.
AIGC refers to content that is generated using advanced Generative AI (GAI) techniques, as
opposed to being created by human authors, which can automate the creation of large amounts
of content in a short amount of time.
Generally, GAI models can be categorized into two types: unimodal models and multimodal
models (Fig. 1.0). Unimodal models receive instructions from the same modality as the generated
content modality, whereas multimodal models accept cross-modal instructions and produce
results of different modalities.
Technically, AIGC refers to, given human instructions which could help teach and guide the model
to complete the task, utilizing GAI algorithms to generate content that satisfies the instruction.
Fig. 1. Overview of AIGC (Credit: Yenala et al (2019))
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