Evaluation of Artificial Intelligence Techniques Applied in Watson and AlphaGo

Abstract

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.

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