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Transformation vs Tradition: Artificial General Intelligence (AGI) for Arts and Humanities

Zhengliang Liu, Yiwei Li, Qian Cao, Junwen Chen, Tianze Yang, Zihao Wu, John Hale, John Gibbs, Khaled Rasheed, Ninghao Liu, Gengchen Mai, Tianming Liu

TL;DR

<3-5 sentence high-level summary>

Abstract

Recent advances in artificial general intelligence (AGI), particularly large language models and creative image generation systems have demonstrated impressive capabilities on diverse tasks spanning the arts and humanities. However, the swift evolution of AGI has also raised critical questions about its responsible deployment in these culturally significant domains traditionally seen as profoundly human. This paper provides a comprehensive analysis of the applications and implications of AGI for text, graphics, audio, and video pertaining to arts and the humanities. We survey cutting-edge systems and their usage in areas ranging from poetry to history, marketing to film, and communication to classical art. We outline substantial concerns pertaining to factuality, toxicity, biases, and public safety in AGI systems, and propose mitigation strategies. The paper argues for multi-stakeholder collaboration to ensure AGI promotes creativity, knowledge, and cultural values without undermining truth or human dignity. Our timely contribution summarizes a rapidly developing field, highlighting promising directions while advocating for responsible progress centering on human flourishing. The analysis lays the groundwork for further research on aligning AGI's technological capacities with enduring social goods.

Transformation vs Tradition: Artificial General Intelligence (AGI) for Arts and Humanities

TL;DR

<3-5 sentence high-level summary>

Abstract

Recent advances in artificial general intelligence (AGI), particularly large language models and creative image generation systems have demonstrated impressive capabilities on diverse tasks spanning the arts and humanities. However, the swift evolution of AGI has also raised critical questions about its responsible deployment in these culturally significant domains traditionally seen as profoundly human. This paper provides a comprehensive analysis of the applications and implications of AGI for text, graphics, audio, and video pertaining to arts and the humanities. We survey cutting-edge systems and their usage in areas ranging from poetry to history, marketing to film, and communication to classical art. We outline substantial concerns pertaining to factuality, toxicity, biases, and public safety in AGI systems, and propose mitigation strategies. The paper argues for multi-stakeholder collaboration to ensure AGI promotes creativity, knowledge, and cultural values without undermining truth or human dignity. Our timely contribution summarizes a rapidly developing field, highlighting promising directions while advocating for responsible progress centering on human flourishing. The analysis lays the groundwork for further research on aligning AGI's technological capacities with enduring social goods.
Paper Structure (49 sections, 11 figures)

This paper contains 49 sections, 11 figures.

Table of Contents

  1. Introduction
  2. Background
  3. Generative AI: From GAN to ChatGPT
  4. Opportunities and Challenges of General AIGC
  5. Text Analysis and Generation
  6. Technical Advances
  7. Transformer Architectures
  8. BERT (Bidirectional Encoder Representations from Transformers)
  9. Autoregressive Language Models
  10. Real-world Applications
  11. Literature Search and Analytics
  12. Linguistics and Communication
  13. Creative Endeavors
  14. Graphics Analysis and Generation
  15. Technical Advances
  16. ...and 34 more sections

Figures (11)

  • Figure 1: Some examples of AGI-generated images. Left: A heavily deep-dream-style photograph expressing "three men in a pool", which is difficult for humans to understand. Middle: An image generated by DALL-E through translation from "an illustration of a baby hedgehog in a christmas sweater walking a dog" ramesh2021zero. Right: Image created by DALL-E 3 with the prompt "vintage 1940s cartoon featuring a robot holding a steaming coffee mug with a lightning bolt symbol on it, text bubble that reads 'Need my charge', sitting at a table by bay window in a coffee shop interior". The model can generate the high-quality image, and correctly understand the instruction.
  • Figure 2: An example of using GPT-3.5 for learning history. The right part shows a follow-up question regarding the answer of the first question in the left part.
  • Figure 3: An example of using GPT-4 to analyze the background and design philosophy of the lyrics of the UEFA (The Union of European Football Associations) Champions League Anthem. The AI model can easily handle the multilingual content, and even point out the "spirit of unity" and "diversity" behind the design.
  • Figure 4: An example of using GPT-4 to write poems (left) and personalized advertisement (right).
  • Figure 5: Google trends (top) and subreddit subscriber growth (bottom) for the past 12 months of the top 3 AI art generation tools: Midjourney, Stable Diffusion, and DALL-E. Data source: https://trends.google.com/trends/ and https://subredditstats.com/.
  • ...and 6 more figures