Communication Systems and Network Technologies †MyAssignmenthelp

Question: Discuss about the Communication Systems and Network Technologies. Answer: Introduction: Technology has already changed the worlds face to a huge extent. It plays a major role in the day to day life of the normal human beings. This advancements in the technology have become of the most used thing for making the lives of the human beings a lot easier. Artificial Intelligence and the use of the robots are the two examples of the same (Xu et al. 2015). The ability of the machines to learn new things without the help of the human beings is known as the AI. The mixture of the AI and the robots have helped the people to do task that were once near to impossible for the humans. The purpose of this report is to evaluate few articles. This article focuses about the one of the major phenomenon of the recent generation that is robots working alongside workers for making the products more precise (Shukla, Khare and Deo 2015). This paper also focus on three different sites where the robots works with the human foe producing the better results. While robots have long been anticipated as global assistants that work in day-to-day human atmospheres, the prime use of robotic technologies have been in manufacturing works and field settings for automating monotonous work or performing tasks that are difficult to get to or dangerous for humans (Zhou et al. 2013). In order to better understand how the collaborative robots are effecting the work and insights of manufacturing employees, the authors conducted an ethnographic study at three industrial plants that had picked up a particular robotic platform which is a much important tactic to understand the relationships This paper is basically about the ethics related to robotics and how the machine learning performances are related to the ethical impact in the society. Advances in information technologies have given rise to many technical improvements that have paved the way for the spread of robots in today's world. The authors brought to notice the history of the machine learning pros and cons. This can be explained as of the area which is much needed in the paper for highlighting the factors that were held in the past. The author brought to light the issues of the present day machine learning objectives (Mohri, Rostamizadeh and Talwalkar 2012). The social are gaining influence day by day in many of the sectors of the industry due to their high potential of the social good such as the healthcare and the engineering (Kanda and Ishiguro 2016). But the problem in this are bearing the high responsibility and the profound understanding the degree of the sociological responsibity. In compare to robots in the environment of industry, social robots are specially designed to co-operate with humans, transportation, healthcare, and retail, housework. Smart Machines Are Not a Threat to Humanity: There are huge concerns for the factor that the smart machines are a huge threats to the human beings. One of the major thing is that the machines are taking away the jobs of the human beings (Mohri, Rostamizadeh and Talwalkar 2012). According to Stephen Hawking is quoted in Cellan-Jones1 as saying: The development of full artificial intelligence could spell the end of the human race. Also amazon Founder Jeff Bezos expressed something similar to this. But such concerns are part if the long history as new researches explains that the AI will make the life of normal human beings easier and comfortable. In the table below, social impact of the robots, the industrial settings the resource is an authenticated source for understanding the concept of the industrial analysis (Lee et al. 2012). The article is not freely available and the users have to buy the same in order to get information about the topic. The researchers are very famous in their respective field making the more accurate. The robotic platform that the authors chose was the Baxter robot, developed and manufactured by Rethink Robotics (Singh and Chandra 2014). In order to get the best results the author conducted different method of analysing which included the study of the working of the robots and that is fly on the wall observations and interviews of the management staffs, workers and the maintenance staffs. The finding that came up were huge (Kanda and Ishiguro, 2016). The table below provides with the information about the research, the analysis done in this report is one of the best that can be done in the similar papers. The paper is a paid paper and cannot be accessed by everyone. Making the value of the paper higher. The paper also highlighted the applications and the advantages of the machine learning concepts, variety of the fields and how they can be used for the betterment of the day to day work. One of the major analysis that the author highlighted in the paper is the information and the system security application (Xu et al. 2015). Evaluation of the Smart Machines Are Not a Threat to Humanity: This paper is one of the freely available paper in the Google scholar. People who are willing to study the concept of the AI and the future of the AI can related to this paper. Some of the serious claims that are made by the paper are true. AS claimed by the author AI systems are, of course, by no means unique in having bugs or limited expertise (Genc, Dag and Ardiclioglu 2015). Any computer system deployed in a safety or security critical situation potentially poses a threat to health, privacy, finance, and other realms. Some of the threats are also mentioned in the paper making it one of the best paper available in the market for getting proper information. Also there are enough evidences for the claims that are made in the paper. Criteria Social Robots' Impact on Human Judgment Social Impact of a Robot Ethics and Robotics Smart Machines Are Not a Threat to Humanity Reliability Allison Sauppe and Bilge Mutlu Daniel Ullrich LMU Munich daniel.ullrich@ifi.lmu.de Andreas Butz LMU Munich andreas.butz@ifi.lmu.de Sarah Diefenbach LMU Munich sarah.diefenbach@lmu.de Sarah Bouazzaouri* Megan A. Witherow Kaitlynn M. Castelle Alan Bundy Relevance Yes , authenticated Yes , authenticated Yes , authenticated Yes , authenticated Accuracy yes, highly accurate accurate sources -data and references - no error yes, highly accurate - accurate sources - no error yes, highly accurate -accurate - data and references - no error yes, highly accurate -accurate sources - data and references - no error Lack of Bias Lack of bias Potential bias Highly biased Not biased Completeness Completed Completed Completed Partial Up to date Published - 2014 Published - 2018 Published - 2015 Published - 2012 Conclusion: Thus concluding the topic it can be said that the articles are some of the best articles that are available in the market and the users can relate to this articles. This reports not only gives proper information about the topics but also provides with enough evidences for the claims that are made. References: Genc, O., Dag, A., and Ardiclioglu, M., 2015. A novel study for the modeling of monthly evaporation using k-nearest neighbor algorithms for a semi-arid continental climate. 2015 IEEE 14th International Conference on Machine Learning and Applications, 341-346. doi:10.1109/ICMLA.2015.74. Kanda, T. and Ishiguro, H., 2016.Human-robot interaction in social robotics. CRC Press. Lee, M. K., Kiesler, S., Forlizzi, J., and Rybski, P. Ripple effects of an embedded social agent: A field study of a social robot in the workplace. In Proc. CHI 12 (2012), 695704. Mohri, M., Rostamizadeh, A., and Talwalkar, A. 2012. Foundations of Machine Learning. Cambridge, MA: The MIT Press. Shukla, R., Khare, D., and Deo, R., 2015. Statistical Downscaling of Climate Change Scenarios of Rainfall and Temperature over Indira Sagar Canal Command area in Madhya Pradesh, India. 2015 IEEE 14th International Conference on Machine Learning and Applications, 313-317. doi:10.1109/ICMLA.2015.75 Singh, N., and Chandra, N., 2014. Integrating Machine Learning Techniques to Constitute a Hybrid Security System. 2014 Fourth International Conference on Communication Systems and Network Technologies, 1082-1087. doi:10.1109/CSNT.2014.221 Xu, K., Yue, H., Guo, L., Guo, Y., and Fang, Y. 2015. Privacy-preserving Machine Learning Algorithms for Big Data Systems. 2015 IEEE 35th International Conference on Distributed Computing Systems, 318-327. doi:10.1109/ICDCS.2015.40 Zhou, J., Huang, W., Xiong, W., Chen, W., and Venkatesh, K., 2013. Segmentation of Hepatic Tumor from Abdominal CT Data Using an Improved Support Vector Machine Framework. 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 3347-3350. doi:10.1109/EMBC.2013.6610258