Position: AI Scaling: From Up to Down and Out

TL;DR

This paper broadens the concept of AI scaling beyond traditional model size increases, emphasizing the importance of Scaling Down and Scaling Out to address societal and technical challenges and enable future breakthroughs.

Contribution

It introduces a holistic framework for AI scaling that includes Scaling Up, Down, and Out, highlighting their roles in advancing AI research and applications.

Findings

Scaling Up faces inherent bottlenecks.

Scaling Down and Out address efficiency and societal challenges.

Transformative applications in healthcare and manufacturing demonstrate potential.

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).