QUANTITATIVE ASSESSMENT OF TECHNOLOGICAL SINGULARITY

The article deals with the topical issue of quantitative assessment of technological singularity. The authors made an analysis of artificial intelligence tools and approaches affecting the development of superintelligence, which allowed for the first time to develop a general multifactor model of technological singularity and present it in the space of direct and indirect indicators of development. The developed approach makes it possible to move from expert judgments on the issue of technological singularity in the form of extrapolated complexity curves of various systems or qualitative description of possible scenarios of technological development to quantitative assessment of the state of technological singularity. The links between the relevant functional areas of human intelligence and modern expert systems are formalized, a structural-functional model of knowledge acquisition is developed. A conclusion is made about the real limits of the processes of modern "intelligent" systems at the level of artificial thinking and logical cognition, which corresponds to a weak artificial intelligence. The state and ways of hardware development were analyzed, which allowed making a conclusion about the complex use of different hardware architectures and information processing principles: supercomputer, neurosynaptic and quantum computers to implement the concept of technological singularity. Formalized in the form of a structural model the areas of research most influential in the development of artificial intelligence, and their relationship to existing approaches and methods of processing big data. For the first time proposed the classification of indicators of development of artificial intelligence within two classes: direct and indirect, grouped into three groups: the intensity of research and public activity; the level of applied (technological) solutions; practical implementation, most affecting the development of general artificial intelligence. The correlation between the formalized groups of indicators was revealed, which confirms the correctness of the hypothesis about the cause-effect relationship between the groups: theoretical research → applied solutions → practical implementation and their mutual influence.