Author :
Abstract
Duyu organlarındaki girdinin işlenmesi; dünyanın algılanması ve anlaşılmasına yönelik işlevler bütününe ‘bilişsel sistem (cognitive) terimi olarak ifade edilir. Bilişsel sistem bilgiyi işleyip kortekse değerlendirmek üzere yönlendirir. Bilişsel sistemler insan üzerindeki görevleri için bağımsız olarak çözümler ve stratejiler geliştirebilen teknik kümesidir. Bunu yapabilmek için bağlamı anlamak, etkileşimde bulunmak, uyum sağlamak ve öğrenmek için bilişsel becerilerle donatılmışlardır. Bilişsel sistemler makine öğrenimi, sinir ağları ve derin öğrenme gibi yapay zeka yöntemlerini kullanabilir. Bununla birlikte bilişsel sistemler yapay zeka ile eşitlenmemelidir. Yapay zekanın olası tanımlarından biri bilişsel süreçlere ve özellikle akıl yürütmeye atıfta bulunur. Zeka insanların soyut, mantıklı düşünme ve amaca yönelik eylem türetme yeteneğidir. Yapay zeka, makinelerde düşünme ve akıllı davranışın uygulanmasıdır. Araştırmada veri toplama yolu olarak, "belge tarama- literatür tarama" yönteminden yararlanılmıştır. Bu doğrultuda biliş, kognitif (bilişsel) sistem, yapay zeka ve insan ilişkisi incelenmiştir. Çalışma sonucunda kontitif sistemin işleyişi insan ve yapay zeka arasında giderek artan benzerliklerin dikkat çektiği irdelenmiştir.
Keywords
Abstract
Processing input in sense organs; it is expressed as the cognitive term ‘to the body of functions for perceiving and understanding the world. The cognitive system directs information to process and evaluate it into the cortex. Cognitive systems are a set of techniques that can independently develop solutions and strategies for their tasks on human beings. To do this, they are equipped with cognitive skills to understand context, interact, adapt and learn. Cognitive systems can use artificial intelligence methods such as machine learning, neural networks, and deep learning. However, cognitive systems should not be equated with artificial intelligence. One of the possible definitions of artificial intelligence refers to cognitive processes and especially to reasoning. Intelligence is the ability of people to think abstractly, logically, and derive purposeful action. Artificial intelligence is the application of thinking and intelligent behavior in machines. In the research, as a way of data collection, "document scanning-literature scanning" method was used. In this direction, cognition, cognitive (cognitive) system, artificial intelligence and human relations were examined. As a result of the study, the functioning of the controlled system was examined, where the increasing similarities between human and artificial intelligence draw attention.
Keywords
- Chiu, Y.C. (2019). Automating adaptive control with item-specific learning. Psychology of Learning and Motivation. 71,1-37.
- Crick, F (1994). The astonishing hypothesis: The scientific search for the soul. New York, USA: Scribner.
- Cukier, K. (2013). The data revolution. The Economist, https://www.economist.com.
- Dubois, D., Guastavino, C., & Raimbault, M. (2006). A cognitive approach to urban soundscapes: using verbal data to access everyday life auditory categories. Acta Acust, 92,865-874.
- Gabrys, R.L., Tabri, N., Anisman, H. & Matheson, K. (2018). Cognitive control and flexibility in the context of stress and depressive symptoms: The cognitive control and flexibility questionnaire. Front Psychol, 9,2219.
- Fjelland, R. (2020). Why general artificial intelligence will not be realized. Humanities and Social Sciences Communications, 7(10).
- Harari, Y.N. (2014). Sapiens: A brief history of humankind. New York, USA: Harper Collins.
- Hintze, A. (2018). Understanding the four types of AI. https://theconversation.com.
- Hollnagel, E. & Woods, D.D. (2005). Joint cognitive systems: Foundations of cognitive systems engineering. Boca Raton, FL: CRC Press.
- Khanam, S., Tanweer, S., & Khalid, S. (2020). Artificial intelligence surpassing human intelligence: Factual or hoax. The Computer Journal, 1-8, doi:10.1093/comjnl/bxz156.
- Karlsgodt, K.H. (2015). Social cognitive neuroscience, cognitive neuroscience, clinical brain mapping. In W. Toga (Eds.) Brain mapping (pp.319-326). Waltham, MA: Elsevier.
- Komal (2014) Comparative assessment of human intelligence and artificial intelligence. International Journal of Computer Science and Mobile Computing, 3, 1-4.
- Lachman, R., Lachman, J.L., & Butterfield, E.C. (2015). Cognitive psychology and information processing: An introduction. Third Avenue, NY: Psychology Press.
- Letouze, E., & Pentland, A. (2018). Towards a human artıfıcıal intelligence for human development. ITU Journal: ICT Discoveries, 2(6).
- Luetz, J. M., Margus, R., & Prickett, B. (2020). Human behavior change for sustainable development: Perspectives informed by psychology and neuroscience. Quality Education, 419-434, doi:10.1007/978-3-319-95870-5_12.
- Luck, S.J., & Vecera, S.P. (2002). Attention. In S. Yantis ed (Eds.) Stevens' handbook of experimental psychology (pp. 235-286). New York: Wiley.
- Magnusson, K.R. & Brim, B.L. (2014). The aging brain. In B.L. Brim, In Reference module in biomedical sciences. Burlington, MA: Elsevier.
- Marr, B. (2016). What everyone should know about cognitive computing. Forbes, https://www.forbes.com.
- Marton, F, & Booth (2013). Learning and awareness. Madison Ave, NY: Routledge.
- Mather, G. (2016). Foundations of sensation and perception. Third Avenue, NY: Routledge.
- Mayer-Schönberger, V., & Cukier, K. (2014) Big data: A revolution that will transform how we live, work, and think. Canada: Eamon Dolan/Mariner Books
- Milstein, J.A., Dalley, J.W., & Robbins, T.W. (2005). Neuropharmacology of attention. In J. Tsotsos, G. Rees, & L. Itti (Eds.) Neurobiology of attention (pp. 57-62). Burlington, MA: Elsevier.
- Neisser, U. (2014). Cognitive psychology. Third Avenue, NY: Psychology Press.
- Radanliev, P., De Roure, D., Van Kleek, M., Santos, O., & Ani, U. (2020). Artificial intelligence in cyber physical systems. AI & Society, 1-14.
- Rosch, E.H. (1978). Principles of categorization. In E.H. Rosch & B.B. Lloyd (Eds.). Cognition and categorization. Hillsdale, NJ: Lawrence Erlbaum Associates.
- Schwarzer, R. (2014). Self-efficacy: Thought control of action. Third Avenue, NY: Taylor & Francis.
- Shane, J (2019). You look like a thing and I love you. London: Wildfire.
- Sgarbi, M. (2012). The aristotelian tradition and the rise of british empiricism: Logic and epistemology in The British Isles (1570–1689). Manhattan, NY: Springer.
- Sousa, D.A. (2016). How the brain learns. Thousand Oaks, CA: Corwin.
- Sumari, A.D.W. & Ahmad, A.S. (2018). Cognitive artificial ıntelligence: Concept and applications for humankind. In C. Wongchoosuk (Eds.). Intelligent System (pp.23-55). doi:10.5772/intechopen.72764.
- Sun, R. (2020). Potential of full human–machine symbiosis through truly intelligent cognitive systems. AI Soc, 35(1),17-28.
- Sweet, L.H. (2011). Information processing speed. In J.S. Kreutzer, J. DeLuca, &. B. Caplan (Eds.) Encyclopedia of clinical neuropsychology. Manhattan, NY: Springer.
- Taniguchi, T., Nakamura, T., Suzuki, M., Kuniyasu, R., Hayashi, K., Taniguchi, A., Horii, T. & Nagai, T. (2020). Neuro-SERKET: Development of integrative cognitive system through the composition of deep probabilistic generative models. New Generation Computing, 38, 23-48.
- Taylor, M.E., Kuhlmann, G., & Stone, P. (2008). Transfer learning and ıntelligence: An argument and approach. Frontiers In Artificial Intelligence and Applications, 171:326.
- Wang, Y., Kwong, S., Leung, H., Lu, J., Smith, M.H., Trajkovic, L., Tunstel, E., Plataniotis, K.N., Yen, G.G.,& Kinsner, W. (2020). Brain-inspired systems: A transdisciplinary exploration on cognitive cybernetics, humanity, and systems science toward autonomous artificial intelligence. IEEE Systems, Man, and Cybernetics Magazine, 6(1), 6-13.
- Weizenbaum, J. (1976). Computer power and human reason. San Francisco: Freeman & Company.
- Yampolskiy, R.V. & Fox, J. (2012). Artificial general intelligence and the human mental model, in singularity hypotheses: A scientific and philosophical assessment. Berlin: Springer.
