Academic Journal of Computing & Information Science, 2021, 4(8); doi: 10.25236/AJCIS.2021.040806.
Jiaqi Han1, Jianing Han2
1School of Computer Sciencce and Technology, China University of Mining and Technology, Xuzhou 221116, China
2School of Philosophy and Religious Studies, Minzu University of China, Beijing 100081, China
Artificial intelligence (AI) and software engineering are two important areas in computer science. In recent years, researchers are trying to apply AI techniques in various stages of software development to improve the overall quality of software products. Moreover, there are also some researchers who focus on the intersection between software engineering and AI. In fact, the relationship between software engineering and AI is very weak; however, methods and techniques in one area have been adopted in another area. More and more software products are capable of performing intelligent behavior like human beings. In this research project, two cases studies which are IBM Watson and Google AlphaGo that use different AI techniques in solving real-world challenging problems have been analyzed, evaluated and compared. Based on the analysis of both case studies, using AI techniques such as deep learning and machine learning in software systems contributes to intelligent systems. Watson adopts ’decision making support’ strategy to help humans make decisions; whereas AlphaGo uses ’self-decision making’ to choose operations that contribute to the best outcome. In addition, Watson learns from man-made resources such as paper; AlphaGo, on the other hand, learns from massive online resources such as photos. AlphaGo uses neural networks and reinforcement learning to mimic human brain, which might be very useful in medical research for diagnosis and treatment. However, there is still a long way to go if we want to reproduce human brain in machine and view computers as thinkers, because human brain and machines are intrinsically different. It would be more promising to see whether computers and software systems will become more and more intelligent to help with real world challenging problems that human beings cannot do.
Artificial Intelligence, Software Engineering, Intelligent Systems
Jiaqi Han, Jianing Han. Evaluation of Artificial Intelligence Techniques Applied in Watson and AlphaGo. Academic Journal of Computing & Information Science (2021), Vol. 4, Issue 8: 29-36. https://doi.org/10.25236/AJCIS.2021.040806.
 K. H. Shankari and R. Thirumalaiselvi, “A Survey on Using Artificial Intelligence Techniques in Software Development Process,” Int. Journal of Engineering Research and Applications, vol. 4, no. 12, pp. 24–33, December 2014.
 N. Pawar, “Application of Artificial Intelligence in Software Engineering,” IOSR Journal of Computer Engineering, vol. 18, no. 03, pp. 46–51, June 2016.
 P. Jain, “Interaction between Software Engineering and Artificial Intelligence - A Review,” International Journal on Computer Science and Engineering, vol. 3, no. 12, pp. 3774–3779, December 2011.
 D. A. Ferrucci, “Introduction to ”This is Watson”,” International Business Machines Corporation, vol. 56, no. 3/4, pp. 1–15, July 2012.
 D. Silver, J. Schrittwieser, K. Simonyan, I. Antonoglou, A. Huang, A. Guez, T. Hubert, L. Baker, M. Lai, A. Bolton, Y. Chen, T. Lillicrap, F. Hui, L. Sifre, G. van den Driessche, T. Graepel, and D. Hassabis, “Mastering the game of Go without human knowledge,” Springer Nature, vol. 550, pp. 354–359, October 2017.
 F. Meziane and S. Vadera, “Artificial Intelligence Applications for Improved Software Engineering Development: New Prospects,” Information Science Reference, pp. 278– 299, 2009.
 A. Pannu, “Artificial Intelligence and its Application in Different Areas,” International Journal of Engineering and Innovative Technology, vol. 4, no. 10, pp. 79–84, April 2015.
 T. Basu, A. Bhatia, D. Joseph, and R. L, “A Survey on the Role of Artificial Intelligence in Software Engineering,” International Journal of Innovative Research in Computer and Communication Engineering, pp. 7062–7066, April 2017.
 R. H. Kulkarni and P. Padmanabham, “Integration of artificial intelligence activities in software development processes and measuring effectiveness of integration,” The institution of Engineering and Technology, vol. 11, no. 1, pp. 18–26, September 2016.
 S. Russell and P. Norvig, “Artificial Intelligence: A Modern Approach,” 2010.
 G. E. Hinton, “Learning multiple layers of representation,” TRENDS in Cognitive Sciences, vol. 11, no. 10, pp. 428–434, 2007.
 J. F. Sowa, “Building Large Knowledge-based Systems: Representation and Inference in the Cyc Project,” Artificial Intelligence, pp. 95–104, 1992.
 R. Akbari and K. Ziarati, “A multilevel evolutionary algorithm for optimizing numerical functions,” International Journal of Industrial Engineering Computations, pp. 419–430, 2011.
 L. A. Zadeh, “Fuzzy Sets,” Information and Control, pp. 338–353, 1965.
 A. M. Turing, “Computing Machinery and Intelligence,” Mind 49, pp. 433–460, 1950.
 B. W. Sorte, P. P. Joshi, and V. Jagtap, “Using of Artificial Intelligence in Software Development Life Cycle: A state of the art Review,” International Journal of Advanced Engineering and Global Technology, vol. 03, no. 03, pp. 398–403, March 2015.
 D. Ferrucci, E. Brown, J. Chu-Carroll, J. Fan, D. Gondek, A. A. Kalyanpur, A. Lally, J. W. Murdock, E. Nyberg, J. Prager, N. Schlaefer, and C. Welty, “Building Watson: an overview of the DeepQA project,” Association for the Advancement of Artificial Intelligence, pp. 59–79, 2010.
 A. K. Baughman, W. Chuang, K. R. Dixon, Z. Benz, and J. Basilico, “DeepQA Jeopardy! Gamification: A Machine-Learning Perspective,” IEEE Transactions on Computational Intelligence and AI in Games, vol. 6, no. 1, pp. 55–66, March 2014.
 C. Asakiewicz, E. A. Stohr, S. Mahajan, and L. Pandey, “Building a Cognitive Application Using Watson DeepQA,” IEEE Computer Society, pp. 36–44, August 2017.
 G. Liu, “Research and Implementation of Artificial Intelligence Customer Service System,” Modern Science and Technology of Telecommunications, pp. 50–59, March 2009.
 Wikipedia. (2017) Go (game). [Online]. Available: https://en.wikipedia.org/wiki/ Go (game)
 Wiki. (2017) Expert System. [Online]. Available: https://en.wikipedia.org/wiki/ Expert system
 D. AlphaGo. (2017) DeepMind. [Online]. Available: https://deepmind.com/ research/alphago/
 J. X. Chen, “The Evolution of Computing: AlphaGo,” Computing in Science and Engineering, pp. 4–7, August 2011.
 S. AI. (2017) A Holistic Approach to AI. [Online]. Available: https://www.ocf.berkeley.edu/∼arihuang/academic/research/strongai3.html
 DeepMind. (2017) AlphaGo Zero: Learning from Scratch. [Online]. Available: https://deepmind.com/blog/alphago-zero-learning-scratch/
 Semantic Technologies in IBM Watson. Proceedings of the Fourth Workshop, August 2013.
 IBM. (2015, March) Disruption ahead: Deloitte’s point of view on IBM Watson.
 S. R. Grander, A. H. Beck, and D. J. P. Jr, “AlphaGo, Deep Learning, and the future of the Human Microscopist,” Archives of Pathology and Laboratory Medicine, vol. 141, pp. 619–621, May 2017.
 E. Gibney. (2015) DeepMind algorithm beats people at classic video games.
 L. G. Tak. (2017) Difference between Watson and AlphaGo? ’Decision making support’ vs ’Self decision making’.
 T. Kachur, “Human brain structure inspires artificial intelligence,” CBCnews Technology and Science, June 2017.
 C. Chatham, “10 Important Differences Between Brains and Computers,” Science Blogs Developing Intelligence, March 2007.