论文标题
一种机器学习软件系统的方法来捕获社会，监管，治理和气候问题
A machine-learning software-systems approach to capture social, regulatory, governance, and climate problems
论文作者
论文摘要
本文将讨论人工智能计算机系统作为基于批判的，隐式组织和固有的设备的作用，它与平行的政府政策同步部署，作为一种以定量形式捕捉民族谋求复杂性的真正手段，在社会上的经济群体中的公共满足感，监管群体，法规构造，委托和委托领域的公共满足感。它将讨论一种涉及众所周知的算法的解决方案，并提供改进的知识代理机制，从而提高效用范围，影响力范围（就阶级阶层而言）和运营效率。它将结束对这些和其他历史含义的讨论。
This paper will discuss the role of an artificially-intelligent computer system as critique-based, implicit-organizational, and an inherently necessary device, deployed in synchrony with parallel governmental policy, as a genuine means of capturing nation-population complexity in quantitative form, public contentment in societal-cooperative economic groups, regulatory proposition, and governance-effectiveness domains. It will discuss a solution involving a well-known algorithm and proffer an improved mechanism for knowledge-representation, thereby increasing range of utility, scope of influence (in terms of differentiating class sectors) and operational efficiency. It will finish with a discussion of these and other historical implications.
