  Artificial Intelligence has been developed for decades with the achievement
of great progress. Recently, deep learning shows its ability to solve many real
world problems, e.g. image classification and detection, natural language
processing, playing GO. Theoretically speaking, an artificial neural network
can fit any function and reinforcement learning can learn from any delayed
reward. But in solving real world tasks, we still need to spend a lot of effort
to adjust algorithms to fit task unique features. This paper proposes that the
reason of this phenomenon is the sparse feedback feature of the nature, and a
single algorithm, no matter how we improve it, can only solve dense feedback
tasks or specific sparse feedback tasks. This paper first analyses how sparse
feedback affects algorithm perfomance, and then proposes a pattern that
explains how to accumulate knowledge to solve sparse feedback problems.
