  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.
