How far from automatically interpreting deep learning

Safe, controllable and credible artificial intelligence has been the goal which the humanity has been pursuing. In the field of deep learning, in order to achieve this goal, it is needed for learning algorithm to really interact with the humanity and it is also indispensable for the learning algorithm to have the ability to correct errors, so as to avoid a prediction model with serious errors caused by unnecessary deviation in training data. So, it is necessary to establish a learning algorithm for capturing and learning causal relationships in the world around us. However, recently, all of this is out of reach. The reason is that the prediction model and its training process are not yet understood by human being. In other words, there is a gap between the deep learning model and the cognitive modes from human being. For shrinking the gap, two general interpretation methods about deep learning model were identified by Lipton [1]: posting interpretation and transparent interpretation. For deep learning, the current mainstream methods are mainly from three aspects: hidden layer analysis method [2-4], simulation model method [5], attention mechanism[6-8]. We posits that how to make the prediction model and training process understood by us ascribe to an optimization problem which can promote the interpretability of the prediction model and make the model more suitable to its causality or discover faults in the causality.