Challenges for an Ontology of Artificial Intelligence

Of primary importance in formulating a response to the increasing prevalence and power of artificial intelligence (AI) applications in society are questions of ontology. Questions such as: What “are” these systems? How are they to be regarded? How does an algorithm come to be regarded as an agent? We discuss three factors which hinder discussion and obscure attempts to form a clear ontology of AI: (1) the various and evolving definitions of AI, (2) the tendency for pre-existing technologies to be assimilated and regarded as “normal,” and (3) the tendency of human beings to anthropomorphize. This list is not intended as exhaustive, nor is it seen to preclude entirely a clear ontology, however, these challenges are a necessary set of topics for consideration. Each of these factors is seen to present a 'moving target' for discussion, which poses a challenge for both technical specialists and non-practitioners of AI systems development (e.g., philosophers and theologians) to speak meaningfully given that the corpus of AI structures and capabilities evolves at a rapid pace. Finally, we present avenues for moving forward, including opportunities for collaborative synthesis for scholars in philosophy and science

Research and Development Workstation Environment: the new class of Current Research Information Systems

Against the backdrop of the development of modern technologies in the field of scientific research the new class of Current Research Information Systems (CRIS) and related intelligent information technologies has arisen. It was called - Research and Development Workstation Environment (RDWE) - the comprehensive problem-oriented information systems for scientific research and development lifecycle support. The given paper describes design and development fundamentals of the RDWE class systems. The RDWE class system's generalized information model is represented in the article as a three-tuple composite web service that include: a set of atomic web services, each of them can be designed and developed as a microservice or a desktop application, that allows them to be used as an independent software separately; a set of functions, the functional filling-up of the Research and Development Workstation Environment; a subset of atomic web services that are required to implement function of composite web service. In accordance with the fundamental information model of the RDWE class the system for supporting research in the field of ontology engineering - the automated building of applied ontology in an arbitrary domain area, scientific and technical creativity - the automated preparation of application documents for patenting inventions in Ukraine was developed. It was called - Personal Research Information System. A distinctive feature of such systems is the possibility of their problematic orientation to various types of scientific activities by combining on a variety of functional services and adding new ones within the cloud integrated environment. The main results of our work are focused on enhancing the effectiveness of the scientist's research and development lifecycle in the arbitrary domain area.Comment: In English, 13 pages, 1 figure, 1 table, added references in Russian. Published. Prepared for special issue (UkrPROG 2018 conference) of the scientific journal "Problems of programming" (Founder: National Academy of Sciences of Ukraine, Institute of Software Systems of NAS Ukraine

A framework for Thinking about Distributed Cognition

As is often the case when scientific or engineering fields emerge, new concepts are forged or old ones are adapted. When this happens, various arguments rage over what ultimately turns out to be conceptual misunderstandings. At that critical time, there is a need for an explicit reflection on the meaning of the concepts that define the field. In this position paper, we aim to provide a reasoned framework in which to think about various issues in the field of distributed cognition. We argue that both relevant concepts, distribution and cognition, must be understood as continuous. As it is used in the context of distributed cognition, the concept of distribution is essentially fuzzy, and we will link it to the notion of emergence of system-level properties. The concept of cognition must also be seen as fuzzy, but for different a reason: due its origin as an anthropocentric concept, no one has a clear handle on its meaning in a distributed setting. As the proposed framework forms a space, we then explore its geography and (re)visit famous landmarks