User-Oriented Smart General AI System under Causal Inference

TL;DR

This paper introduces UOGASuCI, a causal inference-based system that personalizes AI model design by leveraging user-specific tacit knowledge and training experiences to enhance performance.

Contribution

It proposes a novel framework that extracts and optimizes user characteristics influencing tacit knowledge for improved AI model performance.

Findings

Identifies user characteristics linked to model performance.

Recommends updates to user knowledge for better model design.

Demonstrates improved model performance through personalized adjustments.

Abstract

General AI system solves a wide range of tasks with high performance in an automated fashion. The best general AI algorithm designed by one individual is different from that devised by another. The best performance records achieved by different users are also different. An inevitable component of general AI is tacit knowledge that depends upon user-specific comprehension of task information and individual model design preferences that are related to user technical experiences. Tacit knowledge affects model performance but cannot be automatically optimized in general AI algorithms. In this paper, we propose User-Oriented Smart General AI System under Causal Inference, abbreviated as UOGASuCI, where UOGAS represents User-Oriented General AI System and uCI means under the framework of causal inference. User characteristics that have a significant influence upon tacit knowledge can be extracted from observed model training experiences of many users in external memory modules. Under the framework of causal inference, we manage to identify the optimal value of user characteristics that are connected with the best model performance designed by users. We make suggestions to users about how different user characteristics can improve the best model performance achieved by users. By recommending updating user characteristics associated with individualized tacit knowledge comprehension and technical preferences, UOGAS helps users design models with better performance.