机器之爱

摘要：

虽然ML产生了巨大的经济价值，但我们中的许多人与社交媒体和其他ML驱动的应用程序的关系存在问题。其中一个原因是，机器学习经常优化我们当前想要的东西，这很容易量化，但与科学上对人类繁荣的认识不一致。因此，通过它对我们的贫困模型，ML目前远远没有达到其令人兴奋的潜力，即帮助我们实现我们的目标。虽然在定义人类繁荣方面没有达成共识，但从心理学，哲学和精神传统的不同角度来看，爱被认为是其主要催化剂之一。受这种观点的启发，本文探讨了是否存在适合机器体现的有用的爱的概念，因为历史上，探索一个模糊的概念，如生命或智能，是否可以被深思熟虑地抽象和重新想象，就像在机器智能或人工生命领域一样。本文提出了一个机器之爱的候选概念，特别是受到积极心理学和心理治疗工作的启发：提供无条件的支持，使人类能够自主地追求自己的成长和发展。通过概念验证实验，本文旨在强调需要更丰富的模型，人类蓬勃发展的ML，提供一个示例框架，通过它可以将积极心理学与ML相结合，以实现一个粗略的概念，机器爱，并证明目前的语言模型开始能够体现定性的人文原则。结论是，尽管目前ML可能经常让我们上瘾，分散注意力或分裂我们，但另一条道路可能正在开辟：我们可以调整ML来支持我们的增长，通过它帮助我们实现我们的最高愿望。

英文摘要：

While ML generates much economic value, many of us have problematic relationships with social media and other ML-powered applications. One reason is that ML often optimizes for what we want in the moment, which is easy to quantify but at odds with what is known scientifically about human flourishing. Thus, through its impoverished models of us, ML currently falls far short of its exciting potential, which is for it to help us to reach ours. While there is no consensus on defining human flourishing, from diverse perspectives across psychology, philosophy, and spiritual traditions, love is understood to be one of its primary catalysts. Motivated by this view, this paper explores whether there is a useful conception of love fitting for machines to embody, as historically it has been generative to explore whether a nebulous concept, such as life or intelligence, can be thoughtfully abstracted and reimagined, as in the fields of machine intelligence or artificial life. This paper forwards a candidate conception of machine love, inspired in particular by work in positive psychology and psychotherapy: to provide unconditional support enabling humans to autonomously pursue their own growth and development. Through proof of concept experiments, this paper aims to highlight the need for richer models of human flourishing in ML, provide an example framework through which positive psychology can be combined with ML to realize a rough conception of machine love, and demonstrate that current language models begin to enable embodying qualitative humanistic principles. The conclusion is that though at present ML may often serve to addict, distract, or divide us, an alternative path may be opening up: We may align ML to support our growth, through it helping us to align ourselves towards our highest aspirations.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。