Transcendence: Generative Models Can Outperform The Experts That Train Them
Edwin Zhang, Vincent Zhu, Naomi Saphra, Anat Kleiman, Benjamin L. Edelman, Milind Tambe, Sham M. Kakade, Eran Malach·June 17, 2024
Summary
This paper investigates the concept of "transcendence" in generative models, where AI surpasses human performance, using the example of an autoregressive transformer trained on chess games. Low-temperature sampling is found to be crucial for achieving transcendence, as it enables the model to outperform individual human experts by combining and denoising expert knowledge. The study presents two theorems that establish conditions for transcendence, both in the case of a single noisy expert and multiple experts. Experiments with ChessFormer models demonstrate the model's ability to transcend human-level performance, particularly when trained on diverse datasets. The research also connects transcendence to ensemble learning and offline reinforcement learning, highlighting the importance of dataset diversity for optimal performance. The study suggests future research in various domains and addresses the implications of AI models surpassing human abilities, while emphasizing the role of denoising in decision-making.
Introduction
Background
Evolution of AI in chess
Milestones in AI surpassing human performance
Objective
To define and analyze the concept of transcendence in AI
Investigate the role of low-temperature sampling in achieving transcendence
Establish conditions for transcendence in generative models
Theoretical Framework
Overview of autoregressive transformers and their application in chess
Connection to ensemble learning and offline reinforcement learning
Method
Data Collection
Chess game dataset preparation
Diverse datasets and their impact on model performance
Data Preprocessing
Low-temperature sampling techniques
Expert knowledge extraction and denoising
Theorems
Single Noisy Expert Theorem
Multiple Experts Theorem
Model Development
ChessFormer architecture and training process
Experimental setup and evaluation metrics
Results
Model performance surpassing human experts
Impact of dataset diversity on model transcendence
Case studies with ChessFormer models
Discussion
Connection to ensemble learning and decision-making
Future research directions in AI transcendence
Ethical and societal implications of AI surpassing human abilities
Denoising Mechanisms
The role of denoising in enhancing AI performance
Limitations and challenges in achieving transcendence
Conclusion
Summary of key findings
Implications for generative model development and AI ethics
Open questions and future research goals
Basic info
papers
machine learning
artificial intelligence
Advanced features