Artificial intelligence, culture and education

Sequential transformative design of research (Hanson et al. in J Couns Psychol 52(2):224–235, 2015; Groleau et al. in J Mental Health 16(6):731–741, 2007; Robson and McCartan in Real world research: a resource for users of social research methods in applied settings, Wiley, Chichester, 2016) allows testing a group of theoretical assumptions about the connections of artificial intelligence with culture and education. In the course of research, semiotics ensures the description of self-organizing systems of cultural signs and symbols in terms of artificial intelligence as a special set of algorithms. This approach helps to consider the arguments proposed by Searle (Behav Brain Sci 3(3):417–457, 1980) against ‘strong’ artificial intelligence. Searle (Behav Brain Sci 3(3):417–457, 1980) believes that artificial or machine intelligence cannot fully emulate the processes of the human mind. Machine intelligence shows own inevitable weakness. This is non-autonomous tool for computations and data operating. In fact, this tool cannot provide insight into real cognitive conditions. After Lotman and Uspensky (On the semiotics mechanism of culture, Alexandra, Tallinn, 1993), authors expand the meaning of artificial intelligence. The authors identify a cultural type of ‘strong’ artificial intelligence or ‘self-increase of Logos’ in terms by Lotman and Uspensky (On the semiotics mechanism of culture, Alexandra, Tallinn, 1993). The interpretation of human intelligence as imitation of machine intelligence makes possible such immersion of artificial intelligence in culture. The authors reveal a case of self-organizing autonomous generation, encoding, decoding, reception, storage, and transmission of social information in the field of physical training. From the empirical studies it is clear that the organization of collective activities without external control ensures the development of positive emotions and social orientations. Interest in autonomous behavior provides the formation of educational and cognitive motives. As a special set of algorithms, these motives are the most promising and favorable for personal development.