Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Position: AI Scaling: From Up to Down and Out

Yunke Wang, Yanxi Li, Chang Xu

TL;DR

The paper argues that pursuing Scaling Up alone faces data, energy, and diminishing-return bottlenecks, and therefore advocates a triadic framework of Scaling Up, Scaling Down, and Scaling Out. It details concrete methods across all three paradigms, including data-efficient training, pruning and quantization, retrieval augmentation, and distributed ecosystem coordination. By integrating these approaches, the work aims to democratize access, improve efficiency, and enable collaborative AI systems with potential pathways toward Artificial General Intelligence. The proposed framework emphasizes sustainability, equity, and governance to ensure practical impact across industries and society.

Abstract

AI Scaling has traditionally been synonymous with Scaling Up, which builds larger and more powerful models. However, the growing demand for efficiency, adaptability, and collaboration across diverse applications necessitates a broader perspective. This position paper presents a holistic framework for AI scaling, encompassing Scaling Up, Scaling Down, and Scaling Out. It argues that while Scaling Up of models faces inherent bottlenecks, the future trajectory of AI scaling lies in Scaling Down and Scaling Out. These paradigms address critical technical and societal challenges, such as reducing carbon footprint, ensuring equitable access, and enhancing cross-domain collaboration. We explore transformative applications in healthcare, smart manufacturing, and content creation, demonstrating how AI Scaling can enable breakthroughs in efficiency, personalization, and global connectivity. Additionally, we highlight key challenges, including balancing model complexity with interpretability, managing resource constraints, and fostering ethical development. By synthesizing these approaches, we propose a unified roadmap that redefines the future of AI research and application, paving the way for advancements toward Artificial General Intelligence (AGI).

Position: AI Scaling: From Up to Down and Out

TL;DR

The paper argues that pursuing Scaling Up alone faces data, energy, and diminishing-return bottlenecks, and therefore advocates a triadic framework of Scaling Up, Scaling Down, and Scaling Out. It details concrete methods across all three paradigms, including data-efficient training, pruning and quantization, retrieval augmentation, and distributed ecosystem coordination. By integrating these approaches, the work aims to democratize access, improve efficiency, and enable collaborative AI systems with potential pathways toward Artificial General Intelligence. The proposed framework emphasizes sustainability, equity, and governance to ensure practical impact across industries and society.

Abstract

AI Scaling has traditionally been synonymous with Scaling Up, which builds larger and more powerful models. However, the growing demand for efficiency, adaptability, and collaboration across diverse applications necessitates a broader perspective. This position paper presents a holistic framework for AI scaling, encompassing Scaling Up, Scaling Down, and Scaling Out. It argues that while Scaling Up of models faces inherent bottlenecks, the future trajectory of AI scaling lies in Scaling Down and Scaling Out. These paradigms address critical technical and societal challenges, such as reducing carbon footprint, ensuring equitable access, and enhancing cross-domain collaboration. We explore transformative applications in healthcare, smart manufacturing, and content creation, demonstrating how AI Scaling can enable breakthroughs in efficiency, personalization, and global connectivity. Additionally, we highlight key challenges, including balancing model complexity with interpretability, managing resource constraints, and fostering ethical development. By synthesizing these approaches, we propose a unified roadmap that redefines the future of AI research and application, paving the way for advancements toward Artificial General Intelligence (AGI).

Paper Structure

This paper contains 21 sections, 1 figure.

Table of Contents

  1. Introduction
  2. Scaling Up: Expanding Foundation Models
  3. Scaling in AI models
  4. Bottleneck
  5. Future Trends
  6. Scaling Down: Refining Core Functions
  7. Reducing the Size of Large Models
  8. Reducing the Scale of Training
  9. Optimizing Computational Efficiency
  10. Small Models for Large Impacts
  11. Future Trends
  12. Scaling Out: Advancing AI Ecosystems
  13. Scaling Out builds an AI Ecosystem
  14. Technical Foundations
  15. Future Trends
  16. ...and 6 more sections

Figures (1)

  • Figure 1: The proposed framework for AI Scaling that integrates: (a) Scale Up increases model size and complexity, enhancing performance but demanding more computational resources. (b) Scale Down reduces model size and distills the essence of these systems into a smaller, more efficient core model. (c) Scale Out leverages the core model to derive multiple task-specific interfaces, enabling adaptation to diverse tasks and interaction with the environment.