Position: AI Scaling: From Up to Down and Out
Authors: Yunke Wang, Yanxi Li, Chang Xu
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | 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. 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). |
| Researcher Affiliation | Academia | 1School of Computer Science, The University of Sydney, Sydney, Australia. Correspondence to: Chang Xu <EMAIL>. |
| Pseudocode | No | The paper describes concepts and methodologies related to AI scaling but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is a position paper discussing various AI scaling paradigms and does not present new methodology that would require open-source code from the authors. There is no explicit statement or link provided for code release by the authors. |
| Open Datasets | No | The paper references existing well-known public datasets (e.g., Common Crawl, LAION-5B) as examples of data used by the large models it discusses, but it does not present new experimental work or datasets created by the authors for which access information would be provided. |
| Dataset Splits | No | This paper is a position paper discussing AI scaling paradigms and does not present new experimental results with specific dataset splits. Therefore, no information on training/test/validation splits is provided by the authors. |
| Hardware Specification | No | The paper discusses hardware requirements for training large models developed by others (e.g., GPT-3 on NVIDIA V100 GPUs) but does not specify the hardware used by the authors for any experiments presented in this position paper. |
| Software Dependencies | No | The paper discusses various AI models and techniques but does not provide specific software dependencies with version numbers for any experimental setup by the authors, as it is a position paper. |
| Experiment Setup | No | As a position paper, this document outlines a framework for AI scaling and does not include details on specific experimental setups, hyperparameters, or training configurations from the authors' own work. |