英文标题：
《Development of cloud, digital technologies and the introduction of chip
  technologies》
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作者：
Ali R. Baghirzade
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最新提交年份：
2020
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英文摘要：
  Hardly any other area of research has recently attracted as much attention as machine learning (ML) through the rapid advances in artificial intelligence (AI). This publication provides a short introduction to practical concepts and methods of machine learning, problems and emerging research questions, as well as an overview of the participants, an overview of the application areas and the socio-economic framework conditions of the research.   In expert circles, ML is used as a key technology for modern artificial intelligence techniques, which is why AI and ML are often used interchangeably, especially in an economic context. Machine learning and, in particular, deep learning (DL) opens up entirely new possibilities in automatic language processing, image analysis, medical diagnostics, process management and customer management. One of the important aspects in this article is chipization. Due to the rapid development of digitalization, the number of applications will continue to grow as digital technologies advance. In the future, machines will more and more provide results that are important for decision making. To this end, it is important to ensure the safety, reliability and sufficient traceability of automated decision-making processes from the technological side. At the same time, it is necessary to ensure that ML applications are compatible with legal issues such as responsibility and liability for algorithmic decisions, as well as technically feasible. Its formulation and regulatory implementation is an important and complex issue that requires an interdisciplinary approach. Last but not least, public acceptance is critical to the continued diffusion of machine learning processes in applications. This requires widespread public discussion and the involvement of various social groups.
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中文摘要：
最近，通过人工智能（AI）的快速发展，几乎没有任何其他研究领域像机器学习（ML）那样受到如此多的关注。本出版物简要介绍了机器学习的实用概念和方法、问题和新出现的研究问题，并概述了参与者、应用领域和研究的社会经济框架条件。在专家圈中，ML被用作现代人工智能技术的关键技术，这就是为什么AI和ML经常互换使用，尤其是在经济环境中。机器学习，尤其是深度学习（DL），为自动语言处理、图像分析、医疗诊断、过程管理和客户管理开辟了全新的可能性。本文的一个重要方面是芯片化。由于数字化的快速发展，随着数字技术的进步，应用程序的数量将继续增长。未来，机器将越来越多地提供对决策非常重要的结果。为此，从技术方面确保自动化决策过程的安全性、可靠性和足够的可追溯性非常重要。同时，有必要确保ML应用程序符合法律问题，如算法决策的责任和责任，以及技术上的可行性。它的制定和监管实施是一个重要而复杂的问题，需要跨学科的方法。最后但并非最不重要的一点是，公众接受对机器学习过程在应用中的持续扩散至关重要。这需要广泛的公众讨论和各种社会团体的参与。
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分类信息：

一级分类：Economics        经济学
二级分类：General Economics        一般经济学
分类描述：General methodological, applied, and empirical contributions to economics.
对经济学的一般方法、应用和经验贡献。
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一级分类：Quantitative Finance        数量金融学
二级分类：Economics        经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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