However, most of previous ML algorithms do not interact with the environment, or other helpful resources that may improve the learning ability. A new topic appears recently, which is called interactive learning. Its idea has close connection with active learning and self-taught learning. The computer agent not only analyzes the data by utilizing its powerful computation ability, but also develops a kind of intelligent ability to actively seek new resources and interact with other objects in the world to improve learning ability. Human has this ability. When a baby is learning to speak, the first step he/she is trying to mimic the sound from his/her parents. At the same time, he/she can feel the feedback from the parents, such as appraise or disappointment. Based on such feedback, a baby will adjust his/her speaking. After the baby grows more mature, he/she is able to infer the intent of parents and proactively takes some action to attract parents or probe the feedback of parents.
Computer of course has larger advantage in computation capacity than human being, while it needs more advanced algorithms to become as intelligent as human being. That is an important purpose of artificial intelligence. The new research on interactive learning topic is challenging, while it is also very promising if we can develop some practical algorithms in this area.
