Abstract
Economic value additions to knowledge and demand provide practical, embedded and extensible meaning to philosophizing cognitive systems. Evaluation of a cognitive system is an empirical matter. Thinking of science in terms of distributed cognition (interactionism) enlarges the domain of cognition. Anything that actually contributes to the specific quality of output of a cognitive system is part of the system in time and/or space. Cognitive science studies behaviour and knowledge structures of experts and categorized structures based on underlying structures. Knowledge representation through understanding of ‘epistemic cultures’ is an evolutionary stage. But cognition goes beyond knowledge representation. Notwithstanding the importance of epistemology of phenomena, the practicability cum philosophical aspects of machine learning needs to be seen in dynamic behaviour in socio-economic-technical value additions if human machine interaction processes that are context specific are incorporated into strong artificial intelligent systems. Cognitive Science is also studied from both computational and biological angles. Evolution of interactive forms of reasoning through understanding of meta-language of computations or biological learning processes is possible. But the limitation of historical cultures predefines the role of interactive processes in user-networks beyond technology networks. Despite this limitation, inclusive development notions of a heterogeneous national society such as India or Europe can be tested and incorporated.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
To implement competitive relationship among neurons (taking cue from Crick’s view that when external stimuli come to brain-specific neurons corresponding to features of same object form dynamic neural assemblies by a temporal synchronous neural oscillation to code objects in external world. So a firing probability of a competitive neuron within a Bayesian P(X/l1, l2….) = P(X) pi j is w yh P(I) where e lk is linking pre-synaptic neuron, X is neuron and w yh = (Pl y/X)/P(l id) where P(X) is prior probe calculated from feeding information; and P(X/l1, l2….) is post probe after getting information from linking P(e lk) is firing probability of l j. Therefore, for assessing firing probability, posit X 1 X 2….X n be n neurons competitive to one another; P before (X i) is firing probability of X i before competition; and so firing probability after competition is P after (X i) = P before (X i))/Summation P before (X j).—From Shi J-Chinese Academy of Science-Institute of Computing Technology-ky Lab of Intelligent Information processing.
Expert Systems Catalogue by Paul Harmon appearing in The Rise of the Expert Systems (Times books-1988).
References
Anat B, Anat M (2011) Ludwig Wittgenstein. In: Zelta E (ed) The Stanford Encyclopedia of Philosophy
Bickard M, Terveen L (1995) Foundational issues in Ai and cognitive science—impasse and solutions. Elsevier Science Publishers, Amsterdam
Bonakdarian E, Whittaker T, Yang Y (2010) Mixing it up: more experiments in hybrid learning. J Comput Sci Coll 25(4):97–103
Bremer SA (1987) Essays on life, literature and method. J Exp Theor Artif Intell 5:285–333
Bunge M (2003) Emergence of convergence. Toronto University Press, Toronto
Chomsky N (1968) Language and the mind-pub by Harcourt Count Jovanovich Inc
Cooley M (1996) On human machine symbiosis. In: Gill K (ed) Human machine symbiosis. Elsevier, UK
Cristiano C (2000) Artificial liars: why computers will (necessarily) deceive us and each other. National Research Council, Institute of Psychology, Division of “Artificial Intelligence, Cognitive and Interaction Modeling”, Rome
Drescher G (1991) Make up minds—a constructivist approach to AI. MIT Press, Cambridge
Ehn P (1988) Work oriented design of computer artifacts. Arbetslivscentrum. Almsqvist & Wicksell International, Stockholm
Friis S (1987) User developed prototype systems. In: Rasmussen J, Pranas Z (eds) Empirical foundations of information and software sciences III. Plenum Press, New York
Gill K (ed) (1996a) ‘Human machine symbiosis’ (the foundation of human centred systems in human centred systems). Elsevier, UK
Gill K (1996) The foundation of human centred. In: Gill K (ed) Human machine symbiosis. Elsevier, pp 1–68
Hofstadter DR (1979) Godel, Escher, Bach: an eternal golden braid. New York
Hopfield JJ (1984) Neuron with graded response have collective computational processes like two state neurons. Proc Natl Acad Sci USA 81:3088–3092
Kaas S, Rayhawk S, Salamon A et al (2010) Economic implications of software minds
Kirsh D (1991) Foundations of AI: the big issues. Department of Cognitive Science, Univ of California, California
Kowalski R (2011) Computational logic and human thinking: how to be artificially intelligent. Cambridge University Press, London
Lindley C (2011) Synthetic intelligence: beyond AI and robotics; Blekings Institute of Technology, SE-371
Marcuse H (1941) Some social Implications of modern technology. Philos Soc Sci IX
Mayo M (1993) Symbol grounding and its implications for AI. Oxford University Press, Oxford
McClelland (2012) Thirteenth anniversary speech in conference on cognitive science. http://www.cse.buffalo.edu/~rapaport/index.html-20120112
Nisbet R (2005) Impact of Programs for adolescent who sexually offend. www.community.nsw.gov.au
Patnaik D (2003) Constitution of man: the key to world harmony through global planning of productive employment and economic freedom. Journal of Indian Council of Philosophical Research, New Delhi
Rasmussen L, Rauner F, Corbett JM (1988) The social shaping of technology and work; human centred computer integrated manufacturing systems. AI Soc 2:47–61
Rummelhart DE, Hinton GE, Williams RJ (1986) Learning internal representation by error propagation. Parallel Distrib Process 1:318–362
Salmi S (2012) Carnap and the unity of science: intellectual and moral formation of a science-technology generalist: a case study. Helsinki Univ
Shi Z (2009) On intelligence science. Int J Adv Intell 1:39–57
Tsang E (2007) Computational intelligence determine effective rationality; WPO-15-07 Centre for Computational Finance and Economic Agents
Turing A (1950) Computing machinery and intelligence. Mind LIX(236):433–460
Wallingford E, Sticklen J (1991) The relationship between task specific architectures for problem solving and the knowledge level. In: Manikopoulous CN (ed) Proceedings of the 8th international congress on cybernetics and systems. NIIT Press, pp 169–175
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Patnaik, D. Theorizing change in artificial intelligence: inductivising philosophy from economic cognition processes. AI & Soc 30, 173–181 (2015). https://doi.org/10.1007/s00146-013-0524-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00146-013-0524-5