The problem of neural networks communication

In spite of the successful application of artificial neural networks (ANNs) for the solution of multiple problems (forecasting, language translation, image classification, voice recognition etc.), ANNs are still autonomous entities incapable of communication or exchange of their knowledge. Meanwhile, the ability to communicate is critical for further development of methods of artificial intelligence. We propose and test several methods of communication and knowledge fusion of ANNs. These methods do not require the presence of the initial training data and use only the internal parameters of ANNs. We propose generative iterative and non-iterative methods of ANNs communication. Noniterative methods show the classification accuracy similar to that provided by an ensemble of ANNs. The accuracy of generative methods is similar to a network trained on the joint dataset.