Looking back on the development of computer technology, particularly in the context of manufacturing, we can distinguish three big waves of technological exuberance with a wave length of roughly 30 years: In the first wave, during the 1950s, mainframe computers at that time were conceptualized as “electronic brains” and envisaged as central control unit of an “automatic factory”. Thirty years later, during the 1980s, knowledge-based systems in computer-integrated manufacturing were adored as the computational core of the “unmanned factory”. Both waves (...) dismally stranded on the contumacies of reality. Nevertheless, again thirty years later, we now experience the departure of the “smart factory” based on networks of “artificially intelligent” multi-agent or “cyber-physical systems”. From the very beginning, these technological exuberances rooted in mistaken metaphors describing the artifacts and, hence, in delusions about the true nature of computer systems. The behaviour of computers is, as computing science teaches us, strictly restrained to executing computable functions by means of algorithms, it thus neither resembles the performance of a brain as part of a complex sensitive living body nor is it in any meaningful sense “knowledgeable” or “intelligent”. When the delusion of being able to implement “smart factories”, despite the countless accomplishment failures before, gains momentum anew, it appears absolutely essential to reflect on underlying misconceptions. (shrink)

Coming from Hubert Dreyfus’ recent book ‘‘Retrieving Realism”, the paper presents embodied pre-conceptual perception and representational cognition as two contrasting perspectives on accessing the world. It further characterises the ‘different forms of knowledge emerging from these perspectives and how they dynamically relate to each other. Taking up the Peircean theory of signs and abductive reasoning as methods of discovery, computers are analysed as semiotic machines that formally model and objectify explicit knowledge about social practices and that can be embedded in (...) the sign processes of thereby restructured practices. This practice theoretical perspective allows for both, understanding the limits of AI and pointing to options for productively combining the performance of ‘‘cognitive artifacts” with the tacit skills of knowledge workers. (shrink)

Sequential transformative design of research :224–235, 2015; Groleau et al. in J Mental Health 16:731–741, 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 :417–457, 1980) against ‘strong’ artificial intelligence. Searle :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, 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. 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. (shrink)