In the study of artificial intelligence, the following terms would be very important.
In today’s world, Artificial Intelligence (AI) is replacing human being in some aspects of human endeavors. With growth in technology, AI has been taking new twists within a short period of time according to market demand. In regard to this, there is need to understand the nature and boundaries of AI and the effects it has on individual capacity on design practices. The focus of the paper would be on the future of AI and the roles it will play in some years to come. On the same note, it will be possible to evaluate whether human being are on the verge of being replaced by AI systems as they seem to dominate the market. Additionally, the analysis of AI cannot be done without explaining both the current and future roles of AI in the industrial market. The extent of AI evolution, its adoption in the market and acceptance in various fields has been a major driver of the research. Since its inception in the industry, its contribution has been quite enormous and the debate has been lingering around on its ability to overtake human on some critical areas. It has been quite interesting to know if future of AI would be able to respond to events and environmental conditions in a similar manner to human beings. Finally, the design technology of AI system would be considered as the main player in the AI field because its advancement sheds light to development of more intelligent systems.
Artificial Intelligence (AI) research is somehow complex and limited due to nature of skills and knowledge required. Due to limited nature of skills, the methodology used determines the quality of results to be obtained. When designing the research, some factors such as number of responded to use, availability of resourceful people and time factor made it possible to opt for qualitative methodology. First, qualitative study seemed to be the best because it can be used to evaluate and come up with specific conclusion on the views and perception of people understanding (Vaismoradi, Turunen & Bondas 2013, p. 399). Since the research focuses both on current and future perspective of the study, qualitative methodology would help in developing various concepts and required visions to evaluate on future of AI. It is very clear that, the study of AI would dwell much on the future as a result of what has been achieved through use of AI. This makes qualitative the best method because there is no data to record about the future. To make it more realistic, qualitative would give much light on AI value in the industry after evaluation of its use, the areas that have intensively implemented AI as well as its limits. Finally, its ability to work on both structured and unstructured nature of research design has proved to be the best because it is not possible to predict the nature of the responded (Vaismoradi, Turunen & Bondas 2013, p. 402). Therefore, to make possible to uncover into detail, interviews, questionnaires and observation are some of the practices that would be used in the study.
Implementation and use of AI has been a subject of debate on its ability to replace human being on some aspects such as design. In current world, some work activities have been taken over by robot and the next prediction of AI is on human design. It is on these basis that the discussion of the paper has been based on. Upon evaluation of AI application in the past and the present, it will be possible to give a clear prediction on whether design work would be taken over by robots in near future. To answer the subject question, the design work analysis would focus on possibility of having repetitive activities in the design that can be automated through AI.
The presence of AI in the market cannot be disputed as they are being used to perform various tasks. AI has been implemented in various parts of human life such as; Google AI powered predications applications which are able to use some data from smartphones (Endsley 2018, p. 192-193). A good example is Google maps which has the capability to do some evaluation at any given instance while analyzing traffic control. Through these maps, it has been possible to reduce the commute time through suggestions of traffic free routes. This has been achieved through routing algorithm implementation in the Google maps (Hamill et al 2009, p. 26). Similarly, Uber and scheduler are making use of AI and machine learning to optimize the waiting time. Most of AI processes works through data mining process which helps AI systems make use of existing data to make decisions. Interesting use of AI has been implemented on commercial autopilot airlines. It is real to move on a flight without any pilot and all this has been achieved through use of AI (Holzinger, Dehmer & Jurisica 2014, p. 11). In education sector, scholars are making use of AI without their knowledge. Plagiarism checkers are being used to determine the level of copy and paste materials from the internet. Without AI, implementation of Turntin software which is widely used as a plagiarism tool might not be available. The checkers have been implemented through use of algorithm known as similarity function which does a comparison of numerical closeness of given data with existing values (Samani & Saadatian 2012, p.6).
The future of design is changing with advancement in technology. Dynamic development tools are being released almost every day to help in designing more appealing content (Sheridan 2016, p. 526). Integration of these tools gives designers easy time to bring together various templates. Although these tools are available, their integration to make an appealing design is not automatic. Selection of which tool to use, template to merge, text color to use as well as where to place menu can only be done by human being (Cath et al 2018, p.520). In regard to this, the role of designer remains intact and cannot be subject to robots. Despite the emergency of some development tools that were initially developed by human beings, integration of such tools to come up with a website that can meet specification remains the roles of designers (Sparkes et al 2010, p.1). Additionally, AI can be used to come up with smart and modular designed systems but as of now, it lacks capability to personalize system development to meet specific user interaction. Finally, important to note is the encroachment of AI in the design industry and the role of the AI in the changing technological world. According to Ekbia & Nardi (2014, p. 196), it has become apparent that in near future, if AI would be able to personalize its capability to meet designing user interface, designers would be on verge.
There is impact on the nature of the jobs done by human and what robots can do. With growth in technology, robots are taking shape of the market and the kinds of repetitive jobs are being automated through use of AI (Wong, Seet & Sim 2011, p. 12). To be precise, some jobs such as driving, teaching and receptionist are being phased of the market. Some predictions have it that, by the year 2030, about half of the jobs currently done by human beings would be under control of robots. Despite Robots being the future of so many fields, designing remains to be human centered due to its complexity (Castillo & Melin 2012, p. 13-14). Design is more of capturing human thinking and need to ease the work. Robot is a human design and coming up with new design that suits human requirements is quite difficult. Designers tries to emulate what real system users might need and inquire through requirements and specifications suitable user empathy. This makes human a unique character in designing and robots taking after designers remains to be s mystery. Though some applications are being developed to do some repetitive tasks in the design industry, it would be very difficult to come up with AI that can design accommodative user interface (Ososky et al 2014, p. 908). It is realistic that design work is being adopted by AI in the sense that modular design is being done by AI. Some organizations such as IBM, Salesforce and Google have started to change the work of design teams. These changes are being witnessed as a result of companies trusting designing systems due to their abilities to keep their products more consistent (Lieto, Lebiere & Oltramari 2018, p. 48). Finally, both human and robots will be sharing design duties in near future. Human design remains to surface robotic work because robots cannot exist without human design.
Castillo, O. and Melin, P., 2012. Optimization of type-2 fuzzy systems based on bio-inspired methods: A concise review. Information Sciences, 205, pp.1-19.
Cath, C., Wachter, S., Mittelstadt, B., Taddeo, M. and Floridi, L., 2018. Artificial Intelligence and the ‘Good Society’: the US, EU, and UK approach. Science and engineering ethics, 24(2), pp.505-528.
Ekbia, H. and Nardi, B., 2014. Heteromation and its contents: The invisible division of labor between humans and machines, pp.196.
Endsley, M.R., 2018. Automation and situation awareness. In Automation and human performance (pp. 183-202). Routledge.
Hamill, M., Young, V., Boger, J. and Mihailidis, A., 2009. Development of an automated speech recognition interface for personal emergency response systems. Journal of NeuroEngineering and Rehabilitation, 6(1), p.26.
Holzinger, A., Dehmer, M. and Jurisica, I., 2014. Knowledge discovery and interactive data mining in bioinformatics-state-of-the-art, future challenges and research directions. BMC bioinformatics, 15(6), p.11.
Lieto, A., Lebiere, C. and Oltramari, A., 2018. The knowledge level in cognitive architectures: Current limitations and possible developments. Cognitive Systems Research, 48, pp.39-55.
Ososky, S., Sanders, T., Jentsch, F., Hancock, P. and Chen, J.Y., 2014. Determinants of system transparency and its influence on trust in and reliance on unmanned robotic systems. In Unmanned Systems Technology XVI (Vol. 9084, p. 90840E). International Society for Optics and Photonics.
Samani, H.A. and Saadatian, E., 2012. A multidisciplinary artificial intelligence model of an affective robot. International Journal of Advanced Robotic Systems, 9(1), p.6.
Sheridan, T.B., 2016. Human–robot interaction: status and challenges. Human factors, 58(4), pp.525-532.
Sparkes, A., Aubrey, W., Byrne, E., Clare, A., Khan, M.N., Liakata, M., Markham, M., Rowland, J., Soldatova, L.N., Whelan, K.E. and Young, M., 2010. Towards Robot Scientists for autonomous scientific discovery. Automated Experimentation, 2(1), p.1.
Vaismoradi, M., Turunen, H. and Bondas, T., 2013. Content analysis and thematic analysis: Implications for conducting a qualitative descriptive study. Nursing & health sciences, 15(3), pp.398-405.
Wong, C.Y., Seet, G. and Sim, S.K., 2011. Multiple-robot systems for USAR: key design attributes and deployment issues. International Journal of Advanced Robotic Systems, 8(1), p.12.
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