هوش مصنوعی و آینده کار
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هوش مصنوعی و آینده کار

عنوان فارسی مقاله: هوش مصنوعی و آینده کار: همزیستی انسان-AI در تصمیم گیری سازمانی
عنوان انگلیسی مقاله: Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making
مجله/کنفرانس: افق های تجاری - Business Horizons
رشته های تحصیلی مرتبط: مدیریت، مهندسی کامپیوتر
گرایش های تحصیلی مرتبط: مدیریت فناوری اطلاعات، هوش مصنوعی
کلمات کلیدی فارسی: هوش مصنوعی؛ تصمیم گیری سازمانی؛ همبستگی انسان و ماشین؛ تقویت انسانی؛ تصمیم گیری تحلیلی و بصری
کلمات کلیدی انگلیسی: Artificial intelligence، Organizational decision making، Human-machine symbiosis، Human augmentation، Analytical and intuitive decision making
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus - Master Journal List - JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.bushor.2018.03.007
دانشگاه: School of Information & Library Sciences - University of North Carolina - U.S.A
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2018
ایمپکت فاکتور: 3/86 در سال 2017
شاخص H_index: 62 در سال 2019
شاخص SJR: 1/24 در سال 2017
شناسه ISSN: 0007-6813
شاخص Quartile (چارک): Q1 در سال 2017
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 10
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
کد محصول: E10697
فهرست انگلیسی مطالب

Abstract


1- The buzz around artificial intelligence


2- The uncertainty of decision making


3- Addressing complexities in decision making: The AI advantage


4- Overcoming equivocality in decision making


5- Implications for managers and organizations


6- Summary: Call for a new human-machine collaboration


References

نمونه متن انگلیسی مقاله

Abstract


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).

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