In the talk, I will introduce a model of learning with Intelligent Teacher. In this model, Intelligent Teacher supplies (some) training examples $\mathcal{(}{\mathcal{x}}_{\mathcal{i}}\mathcal{,}{\mathcal{y}}_{\mathcal{i}}\mathcal{)}\mathcal{,}\mathcal{i}\mathcal{=}\mathcal{1}\mathcal{,}\dots \mathcal{,}\mathcal{l}\mathcal{,}{\mathcal{x}}_{\mathcal{i}}\in \mathcal{X}\mathcal{,}{\mathcal{y}}_{\mathcal{i}}\in \{\mathcal{-}\mathcal{1}\mathcal{,}\mathcal{1}\}$ with additional (privileged) information) ${\mathcal{x}}_{\mathcal{i}}^{\mathcal{\ast }}\in {\mathcal{X}}^{\mathcal{\ast }}$ forming training triplets $\mathcal{(}{x}_i,x_i^{\ast },y_i),i,\dots ,l$. Privileged information is available only for training examples and $notavailablefortextexamples$. Using privileged information it is required to find a better training processes (that use less examples or more accurate with the same number of examples) than the classical ones. In this lecture, I will present two additional mechanisms that exist in learning with Intelligent Teacher * The mechanism to control Student's concept of examples similarity and * The mechanism to transfer knowledge that can be obtained in space of privileged information to the desired space of decision rules. Privileged information exists for many inference problem and Student-Teacher interaction can be considered as the basic element of intelligent behavior.