Professional career aspirations after studying Sociology
The study of Sociology is considered is considered a necessary facet in the present highly-evolved world. We are moving towards an era where the social life of human kind is becoming highly complicated. To deal with this situation, it is important for people to find a way to understand and react to this highly-changing complexities. The best and only way to do so is through study of Sociology. Personally, I strongly believe Sociology can solve issues related to social adjustments, social work and social problems. I am confident that Sociology will help me to professionally navigate my future professional career as a nurse. Actually, this is my motivation for pursuing a course in Sociology. Sociology is important in a nursing career since it lays the foundation of human interactions and provides an accurate way of analyzing and predicting human conduct. Sociology equips one with the skills to improve the quality of the lives of patients by understanding human reactions and behavior thereby allowing one to perform accurate diagnoses and provide genuine care to patients. The importance of Sociology in professional career of a nurse is demonstrated by the fact that it enables a profession to comprehend and appreciate the healing process as a whole rather than only giving medicine.
The knowledge obtained from the study of Sociology expands the mind of a nurse to understand and appropriately to deal with social issues that outside their scope of work but related to their career and in need of their intervention. For instance, nurses that have grasped the concepts of sociology are able to deal with a myriad of social issues involving patients, i.e. psychological stress, isolation, poor economy, housing problems and other social issues in the absence of social workers. Sociology is important to a nurse’s career because it enables one to know and understanding of social factors that pose as a barrier to rehabilitation and treatment. Thus, a nurse is able to apply supportive social factors when rehabilitating and treating a patient.
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How artificial Intelligence is related to sociology
Introduction
After 50 years of continuously working on machines and concepts of neural networks, scientists have successfully devised a way for machines to understand natural language, recognize sounds and view the world around them. Notable scientists have even been awarded the Turing Award for their breakthroughs in the field of artificial intelligence. The Turin Award is the equivalent of the Nobel Prize but in computing and comes with a $1 million prize for its global award winner (Hey, 2016). Initially, most of the artificial intelligence inventions focused on robot, automobiles, and face recognition. However, the artificial intelligence inventions have extended to social studies such as sociology. Presently, researchers have come up with ways through which artificial intelligence can be applied to health and social interactions and education in order to enable humans become more connected to the society. Therefore, this paper will investigate how artificial intelligence is related to sociology.
Relationship between Artificial Intelligence (AI) and Sociology
Contrasting between the meaning of sociology and artificial intelligence, one can observe a relationship between their definitions. For instance, artificial intelligence is defined as the science of making computers acts as humans with creative reasoning abilities. For that to be possible, artificial intelligence (AI) involves a comprehensive study of how the human mind works, decides, learns and thinks when solving problems. The objective of AI is to improve functions that are related to human knowledge (Mlynář, et al., 2018). On the other hand, the meaning of sociology is not different from that of AI since it involves the human social interactions. Ideally, it is the systematic study of all aspects of human social life, i.e. what makes a person walk along the right side of a sidewalk? What made him to go towards the right side rather than the left? It is such kind of questions that form the unique puzzle that sociology aims to understand and explore (Horowitz, 2017). Therefore, artificial intelligence is related to sociology since both studies explore the creative reasoning abilities of computers and human life.
As scientists continue to research and come up with sophisticated AI inventions it will be possible to understand more about the functioning of the human brain. AI heavily draws its concepts from the renowned 10% brain theory which states human beings use a small fraction of their mental capacity during their entire lives. AI scientists have embarked on a journey toward understanding both the structure and the functioning of the human brain, their chief desire being to accurately establish what human know and what the thing that makes them human (Bloomfield, 2018). On a human level, it is virtually impossible to understand how the billions of neurons in the human body are wired to behave within their quasi-existential rag of channels. However, AI analysts strongly believe that it will be possible to human beings to finally understand who they are using Machine Learning. The wave has already begun and the technology is already in application (Hey, 2016). A perfect illustration is an AI system developed in 2015 that enables health practitioners to correctly identify young adults that will develop psychosis later on in their lives through the analysis of their speech transcripts. Similarly, the aim of sociology is to study how and why humans act the way they do and how they are influenced by historical and social forces. Hence, sociology and AI are similar to each other their aim is to investigate the nature of the human reasoning and answer question that philosophers have been trying to settle for a very long time.
AI and sociology are related in the sense that their discussions are carried out in the ‘hopeful tense.’ Due to this, their arguments revolve around what they will be able to achieve and effects that might possibly occur do to their application. Usually, their aim is to propose the achievements and lay claim to the development that such effects will entitle them to. For instance, renowned sociologist Nick Bostrom in collaboration with a renowned AI scientist by the name Vincent Muller, predicted that by the year 2050, there will be a 50% improvement in Artificial General Intelligence, which would further increase to 90% by the year 2075 (Müller and Bostrom, 2016). Bostrom also predicted that within a few decades, AI will achieve superintelligence – intellect that outperforms the cognitive abilities of humans. Quite similar to the AI, discussions about sociology are also are largely optimistic. Sociologists concentrate on establishing the relationship between the truth and ‘hype’. They argue that since anticipation is constitutive of value, one cannot distinguish between people expectations (nanotechnologies, stem cells, bio technologies) and the practically of such expectation (Umpleby, Wu and Hughes, 2017). From an entirely constructive viewpoint, it is only fair to research and talk about ‘hype’ historically since it is virtually impossible to separate the present from future reality. Both AI and Sociology are based on a contingent future that must be embraced, fought for and imagined in order to bring the imagined future toward the present. Hence, it is correct to argue that both AI and sociology are based on optimistic (hopeful tense) projections that are years away from the present.
Artificial intelligence is also related to sociology since involves the study of the social settings where it is to be implemented. Thus, artificial intelligence systems are categorized as social actors performing social roles. Going back to the definition of artificial environment, it involves developing machines that function intelligently and appropriately with foresight on a particular environment/region. From the above observation is not possible of scientists to develop AI technology in a vacuum. Notably, they intensively consult with social scientists and other stakeholders, that is policy makers, to ensure that social and economic benefits of AI are shared within the society. For over two decades now, scientists have been trying to establish the relationships that exists between sociosystemic theories and AI systems so that they can develop intelligent system designs. One of their chief aims is to transcribe or develop second generation sociological theories that can be used to forecast the effects of artificial intelligent systems that are overlooked by system designers. One goal of AI systems is to come up with a sociology of open systems. In order to model the human brain, AI systems must be fed with unambiguous and thorough instructions so that they can understand external reality (Bloomfield, 2018). Hence, it is impossible to distinguish AI from since the use of intelligent devices results in the creation of new sociopsychological and sociocultural dynamics.
Niklas Luhmann, a German sociologist in the field of social science introduced the Systems Theory which led to major advancements in sociocybernetics and sociology systems theory thereby redefining the world’s understanding of AI and Sociology. Luhmann’s system theory is the motivation behind the current form of AI programming commonly referred to as Distributed Artificial Intelligence (DAI). DAI is largely derived from Luhmann’s perceptions on second order observation. Rather than use codes derived from symbolic or Classical observation of AI, modern DAI technology employs the use of nodes to generate activities in AI systems. The nodes change depending on their interaction with other nodes. As a result of Luhmann’s Systems Theory, it is possible for AI scientists to reformulate certain human concepts, i.e. communication in order to come up with AI systems that consider certain sociological characteristics such as interactions with the environment (Kåhre, 2010). By doing so, they are able to directly document the opportunities emanating from these interactions and make the necessary improvement to AI systems. Therefore, through the System Theory, AI technologies are able to mimic the human interactions by considering the influence of sociological features in the environment. This demonstrates the strong relationship that exists between AI and sociology.
Conclusion
The study conducted proves the existence of a strong relationship between artificial intelligence and sociology. The first relationship is observed in definition of the two studies; exploration of the creative reasoning abilities of people and computers. The second relationship is observed in the desire of both studies to study the functioning of the human brain. While the focus of AI is to study the structure and behavior of human brain, sociology focuses on studying behavior of human in relation to their environment. Discussions of AI and sociology are conducted in the ‘hopeful tense,’ that is based on a contingent future. Similar to sociology, AI considers the impact of the social setting on execution of interactions. Finally, application of System Theory in AI technology leads to mimicking of human interactions through recreating sociological setups. Hence, the study illustrates relationship between sociology and artificial intelligence.
References
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Hey, T., (2016). Richard Feynman, Data-Intensive Science and the Future of Computing. In Proceedings of the 34th annual International Symposium on Lattice Field Theory (LATTICE2016). P. 24-30 July 2016. University of Southampton, UK. Online at http://pos . sissa. It/cgi-bin/reader/conf. cgi? confid= 256, id. 19 .
Horowitz, I., (2017). Professing sociology: Studies in the life cycle of social science . Routledge.
Kåhre, P., (2010). Luhmanns massmedieteori och Internet som ett artificiellt intelligent semiotiskt system [Luhmann’s mass-media theory and Internet as an artificial intelligent semiotic system]. MedieKultur: Journal of media and communication research , 26 (49), pp.13-p.
Müller, V.C. and Bostrom, N., (2016). Future progress in artificial intelligence: A survey of expert opinion. In Fundamental issues of artificial intelligence (pp. 555-572). Springer, Cham.
Mlynář, J., Alavi, H.S., Verma, H. and Cantoni, L., (2018), August. Towards a Sociological Conception of Artificial Intelligence. In International Conference on Artificial General Intelligence (pp. 130-139). Springer, Cham.
Umpleby, S., Wu, X.H. and Hughes, E., (2017). Advances in Cybernetics Provide a Foundation for the Future. International Journal of Systems and Society (IJSS) , 4 (1), pp.29-36.
