نمونه متن انگلیسی مقاله
Artificial intelligence (AI) has penetrated many organizational processes, resulting in a growing fear that smart machines will soon replace many humans in decision making. To provide a more proactive and pragmatic perspective, this article highlights the complementarity of humans and AI and examines how each can bring their own strength in organizational decision-making processes typically characterized by uncertainty, complexity, and equivocality. With a greater computational information processing capacity and an analytical approach, AI can extend humans’ cognition when addressing complexity, whereas humans can still offer a more holistic, intuitive approach in dealing with uncertainty and equivocality in organizational decision making. This premise mirrors the idea of intelligence augmentation, which states that AI systems should be designed with the intention of augmenting, not replacing, human contributions.
The buzz around artificial intelligence
Artificial Intelligence’s (AI’s) visibility and rapid momentum in recent years is best reflected in IBM’s Watson’s 1 defeat of Jeopardy’s top human contenders and Google DeepMind’s AlphaGo,2 which trounced one of the world’s best at the board game Go. There are many variations of AI but the concept can be defined broadly as intelligent systems with the ability to think and learn (Russell, Norvig, & Intelligence, 1995). AI embodies a heterogeneous set of tools, techniques, and algorithms. Various applications and techniques fall under the broad umbrella of AI, ranging from neural networks to speech/pattern recognition to genetic algorithms to deep learning. Examples of common elements that extend AI cognitive utilities and can augment human work include natural language processing (the process through which machines can understand and analyze language as used by humans), machine learning (algorithms that enable systems to learn), and machine vision (algorithmic inspection and analysis of images). Natural language processing affords IBM’s Watson the ability to understand nuanced human-composed sentences and assign multiple meanings to terms and concepts. Machine learning capabilities empower Watson to learn from experience and interaction with data, and to develop intelligent solutions based on past experiences. Through machine learning techniques and access to medical research articles, electronic medical records, and even doctors’ notes at Memorial Sloan Kettering, Watson has learned to discern cancer patterns. The AI has made headway in offering promising courses of treatment. AI-powered machine vision, finally, has enabled Watson to rapidly processmyriads ofMRIimages ofthebrain andtomark very small hemorrhages in the image for doctors (Captain, 2017).