人性化机器智能与人工智能系统综述

Juby Abraham, George Joseph Cherian, N. Jayapandian
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引用次数: 2

摘要

在这个时代,机器学习正在以一种非常不同的方式改变着人类的生活。在人类比以往任何时候都更加分裂的情况下,是需要赋予机器做决定的能力,还是需要赋予道德指南针,这是一个巨大的难题。法律和人工智能的互动主要有两种方式。人工智能可能受到法律限制,并可用于法庭程序。我们周围的世界正因人工智能的各种表现形式而发生重大而迅速的变化。公法包括非歧视法和劳动法等重要方面。这与人工智能(AI)应用于机器人等有形技术的方式类似。在某些情况下,人工智能(AI)可能很难被客户注意到,但对那些构建和使用它的人来说却是显而易见的。行为研究为如何在智能机器人和人之间建立持久和有益的互动提供了建议。人类的进步是人工智能发展和实施的主要障碍。这一领域的最佳实践不受任何一种普遍认可的策略的支配。正如我们所知,机器学习即将彻底改变社会。现在比以往任何时候都更有必要给智能计算机一个道德指南针,因为人类是多么的分裂。尽管机器学习具有无限的潜力,但不当使用可能会产生有害的长期影响。例如,它将思考早期文化如何通过创造性地回答机器学习现在提出的许多道德问题来建立信任和改善社会互动。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Systematic Review on Humanizing Machine Intelligence and Artificial Intelligence
In this era, Machine Learning is transforming human lives in a very different way. The need to give machines the power to make decisions or giving the moral compass is a big dilemma when humanity is more divided than it has ever been. There are two main ways in which law and AI interact. AI may be subject to legal restrictions and be employed in courtroom procedures. The world around us is being significantly and swiftly changed by AI in all of its manifestations. Public law includes important facets such as nondiscrimination law and labor law. In a manner similar to this when artificial intelligence (AI) is applied to tangible technology like robots. In certain cases, artificial intelligence (AI) might be hardly noticeable to customers but evident to those who built and are using it. The behavior research offers suggestions for how to build enduring and beneficial interactions between intelligent robots and people. The human improvement is main obstacles in the development and implementation of artificial intelligence. Best practices in this area are not governed by any one strategy that is generally acknowledged. Machine learning is about to revolutionize society as it is know it. It is crucial to give intelligent computers a moral compass now more than ever before because of how divided mankind is. Although machine learning has limitless potential, inappropriate usage might have detrimental long-term implications. It will think about how, for instance, earlier cultures built trust and improved social interactions via creative answers to many of the ethical issues that machine learning is posing now.
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