The aim of this paper is to grasp the relevant distinctions between various ways in which models and simulations in Artificial Intelligence (AI) relate to cognitive phenomena. In order to get a systematic picture, a taxonomy is developed that is based on the coordinates of formal versus material analogies and theory-guided versus pre-theoretic models in science. These distinctions have parallels in the computational versus mimetic aspects and in analytic versus exploratory types of computer simulation. The proposed taxonomy cuts across the (...) traditional dichotomies between symbolic and embodied AI, general intelligence and symbol and intelligence and cognitive simulation and human/non-human-like AI. -/- According to the taxonomy proposed here, one can distinguish between four distinct general approaches that figured prominently in early and classical AI, and that have partly developed into distinct research programs: first, phenomenal simulations (e.g., Turing’s “imitation game”); second, simulations that explore general-level formal isomorphisms in pursuit of a general theory of intelligence (e.g., logic-based AI); third, simulations as exploratory material models that serve to develop theoretical accounts of cognitive processes (e.g., Marr’s stages of visual processing and classical connectionism); and fourth, simulations as strictly formal models of a theory of computation that postulates cognitive processes to be isomorphic with computational processes (strong symbolic AI). -/- In continuation of pragmatic views of the modes of modeling and simulating world affairs, this taxonomy of approaches to modeling in AI helps to elucidate how available computational concepts and simulational resources contribute to the modes of representation and theory development in AI research—and what made that research program uniquely dependent on them. (shrink)

This discussion paper proposes that a meaningful distinction between science and technoscience can be found at the level of the objects of research. Both notions intermingle in the attitudes, intentions, programs and projects of researchers and research institutions—that is, on the side of the subjects of research. But the difference between science and technoscience becomes more explicit when research results are presented in particular settings and when the objects of research are exhibited for the specific interest they hold. When an (...) experiment is presented as scientific evidence which confirms or disconfirms a hypothesis, this agrees with traditional conceptions of science. When organic molecules are presented for their capacity to serve individually as electric wires that carry surprisingly large currents, this would be a hallmark of technoscience. Accordingly, we propose research on the ontology of research objects. The focus on the character and significance of research objects makes this a specifically philosophical project. (shrink)