Describe the scales on which the actants in your stories “act” in the assemblage. Discuss how these actions can enhance management functions
Outline what issues/questions these actions raise. To achieve this, compare and contrast the viewpoints of a range of theorists and critics explored
throughout the module.
Conclude by summarising how your answers relate to the statement “Contemporary computing can be seen as an assemblage.”
The study is having in depth discussion about the two main live stories related to information technology assemblage. The report is also having explanation of actor network theory in context to live stories. As mentioned by De et al. (2013), the two live stories are mainly based on information technology and the innovation included in it. The study is also having focus on actants of live stories and their effectiveness on society and other factors. Apart from this, the comparison and contrast is also done of both stories actant. With the help of this, the impact of them on each other is evaluated. In perspective to this Pain (2013) stated, actor network theory helps in analyzing the affects the live stories actant. The actor network theory is used in this study to explain the two different actant and their impact on operation of information technology. Moreover, the information technology is the fastest growing sector in current time (Preece, 2015). The main factor on which information technology depends is innovation and development in application of information technology.
In context to this El (2013) stated, the two live stories selected for this study are; first story is object recognition for robots. And second story is first new computer program which has recognized sketches more accurately than human being. Apart from this, the actant in first live story is robots and object. On the other hand, in second story the actant is sketches and computer programs. As mentioned by Holgate (2014), with the help of actor network theory the network in both stories are relation between two actants. That is, the operation relation or relations in context tro technology among actant are network. In addition to this Cong (2015) commented, the operation and dependency of all actant on networks are different. The networks developed by all actants are different. Along with this, the developments of actant of both live stories are done with the help of technology and others (Cong., 2015). The analysis of actant is done taking into consideration all factors of actor network theory.
As stated by Cheng et al. (2015), the recognition of object by robots are done more accurately and faster that any human being. The main factor on which robotics recognition is done is algorithm. The visual power and identification power of robots are more powerful than human beings. On the other hand, the object recognition by robots is based on determining identity and location of object (Preece et al., 2015). The result of identification of object is represented in the form of image or pattern. Apart from this, the robot is developed with the help of technology and algorithm. The algorithm helps in identification, scanning and recognition process of object. In context to this Preece et al. (2015) commented, with the technology innovation and improvement, robot related system are also changing. The recognition of object by robots helps in identification of object in easy manner. As stated by Monk (2014), object recognition or pattern recognition is based on artificial intelligence.
Figure1: Image of object recognition by robots
In relation to this Aggarwal (2013) commented, the robots can also identify the fingerprints, faces, sign and other image. The object identification will be done with the help of a database and algorithm provided to robots. The figure 1 represents the images of object recognized by robot. For example, the robot entered in conference hall and was said to search table full having soda can, cap, coffee mug and other item (Preece, 2015). Thus after recognition of object the image of object which was can be obtained from robot database is as shown in figure 1.
Figure 2: Image pattern of objects recognized by robots
As stated by Tang et al. (2013), the researchers developed first computer which can recognizes sketches more accurately than any human being. Apart from this, the sketches used for this study was human made. In relation to this Lai et al. (2013) commented, the computer program is called as sketch a net, as it helps in recognizing the sketches more easily and accurately. With the help of this computer program the handmade sketches are recognized easily. On the other hand, the identification of sketches is limited to program database. That is, the sketch a net computer program can identify sketches whose images or pattern are stored in its database. As stated by Sermanet et al. (2013), some of the major images identified by sketch a net computer program are flying birds, pigeon, standing birds, seagull and others. The significant of sketch increased with the increase of touch screen. That is, if user is drawing sketches with the help of touch screen devices, the new computer program can identify it easily. The main use of this new computer program is done by police and other defense forces (Pain, 2013). As with the help of sketch a net program they can easily get sketch of criminal and it will also help them to improve their database record. Apart from this with the help of this computer program the social security will also be increased.
In context to this Holgate (2014) commented, the defense officers by viewing the CCTV database or any eye evidence can get sketch. Later on the matching of sketch or recognizing of sketch can be done with this new computer program, which is sketch a net program.
Figure 3: Sample of sketches recognized by new computer program that is, sketch a net program
The figure 3 represents the sample of handmade sketches of birds which was recognized by new computer program sketch a net program. As stated by Monk (2014), sketch a net program is a neural network program. Along with this, it is also designed for analyzing and following the human being brain. This computer program is new and unique as it can easily identify the handmade sketches easily in comparison of human being.
The live stories are based on technology and innovation so the chance of problem is also high. That is, there are several problems related to both the live stories. As mentioned by Cheng et al. (2015), first live story is having problem like, algorithm coordination with the robot operation and database. Along with this, the robots have reduced the human being skills and knowledge level of human beings. In current time, everyone is tried to implement new technologies so organization in big scales are try to implement new robots or technologies (Pain, 2013). Apart from this, human beings are also reducing their physical work, that is, the dependency on internet and robot related technology is increased.
In case of second live story, the problems are as, the human being skills and knowledge level is also decreased. On the other hand, as commented by Monk (2014), in current time people thinks that internet can do every work, so why to do it. The main reason of increase in technical dependency is due to this. Moreover, the employment rate of human being is also reduced due to high level dependency on internet and technology. The main side effects of new technology in current time are that; human work is reduced and their intelligence level is also reduced (Cheng et al., 2015). On the other hand, the recognition of object and sketches is done accurately, but efficiency of human are reduced. As stated by De (2013), in current time human beings are increasing their dependency on robots and information technology. On the other hand, the new computer programming helps in identifying the location of sketches and objects present nearby to it. Moreover, the main problem regarding this live story is it increases the human dependency on technology and new innovative robots.
Conclusion
The two live stories are object recognition for robots and new computer program which can identify sketches more accurately and easily then human being. Both the live stories are based on technology and innovation. On the other hand, with the change in technology the assemblage is also changing. The organizations in current time are changing their technology to increase the competitive advantage. The first live story is based on object recognition for robot, that is, the robot recognizes the object easily. According to ANT theory the actant helps in completion of process and it also helps in developing network. With the help of sensors and other technology robot can recognize object nearby to him. In second live story, the new computer program is discussed, which is expert in recognizing the hand- made sketches. The analysis of both live stories are done taking into consideration actor network theory. On the other hand, the actant in first story is robot and in second story is new computer program as well as sketches.
References
De Montis, A., Pinna, A., Barra, M. and Vranken, E., 2013. Analysis of poultry eating and drinking behavior by software eYeNamic. J Agricult Engineer, 44(2s).
Pain, E., 2013. Networking Your Way to a Job. Science.
El-Mahdy, A., 2013. Recursive Detection of M-ary Signals over Fast Varying Mobile Communication Channel. TELKOMNIKA (Telecommunication, Computing, Electronics and Control), 11(1).
Holgate, S., 2014. Creating Your Own Job: From Engineer to Entrepreneur. Science.
Cong Vinh, P., 2015. Algebraically Autonomic Computing. Mobile Networks and Applications.
Cheng, M., Mitra, N. J., Huang, X., Torr, P. H., and Hu, S. (2015). Global contrast based salient region detection. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 37(3), 569-582.
Preece, J., Sharp, H., and Rogers, Y. (2015). Interaction Design-beyond human-computer interaction. John Wiley and Sons.
Monk, A. F. (2014). Fundamentals of human-computer interaction. Academic Press.
Tang, S., Wang, X., Lv, X., Han, T. X., Keller, J., He, Z. and Lao, S. (2013). Histogram of oriented normal vectors for object recognition with a depth sensor. In Computer Vision–ACCV 2012 (pp. 525-538). Springer Berlin Heidelberg.
Aggarwal, J. K. (2013). Multisensor fusion for computer vision (Vol. 99). Springer Science and Business Media.
Lai, K., Bo, L., Ren, X., and Fox, D. (2013). RGB-D object recognition: Features, algorithms, and a large scale benchmark. In Consumer Depth Cameras for Computer Vision (pp. 167-192). Springer London.
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., and LeCun, Y. (2013). Overfeat: Integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv:1312.6229.
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