We Need Positive Visions for AI Grounded in Wellbeing
We Need Positive Visions for AI Grounded in Wellbeing
我们需要基于福祉的积极人工智能愿景
Introduction / 引言
Imagine yourself a decade ago, jumping directly into the present shock of conversing naturally with an encyclopedic AI that crafts images, writes code, and debates philosophy. Won’t this technology almost certainly transform society — and hasn’t AI’s impact on us so far been a mixed-bag? Thus it’s no surprise that so many conversations these days circle around an era-defining question: How do we ensure AI benefits humanity? These conversations often devolve into strident optimism or pessimism about AI, and our earnest aim is to walk a pragmatic middle path, though no doubt we will not perfectly succeed.
试想一下十年前的你，如果直接跳跃到今天，面对能自然对话、博学多才、能创作图像、编写代码并探讨哲学的人工智能，会感到怎样的震撼？这项技术几乎肯定会改变社会，而人工智能迄今为止对我们的影响难道不是喜忧参半吗？因此，如今许多讨论都围绕着一个定义时代的命题也就不足为奇了：我们如何确保人工智能造福人类？这些讨论往往陷入对人工智能的盲目乐观或悲观，而我们真诚的目标是走一条务实的中庸之道，尽管毫无疑问我们无法做到尽善尽美。
While it’s fashionable to handwave towards “beneficial AI,” and many of us want to contribute towards its development — it’s not easy to pin down what beneficial AI concretely means in practice. This essay represents our attempt to demystify beneficial AI, through grounding it in the wellbeing of individuals and the health of society. In doing so, we hope to promote opportunities for AI research and products to benefit our flourishing, and along the way to share ways of thinking about AI’s coming impact that motivate our conclusions.
虽然空谈“有益的人工智能”很时髦，许多人也希望为其发展做出贡献，但要明确“有益的人工智能”在实践中具体意味着什么并不容易。本文代表了我们试图揭开“有益人工智能”神秘面纱的尝试，即将其建立在个人福祉和社会健康的基础之上。在此过程中，我们希望促进人工智能研究和产品的发展，使其造福于人类的繁荣，并在此过程中分享我们对人工智能未来影响的思考方式，这些思考也正是我们得出结论的动力所在。
The Big Picture / 全局视角
By trade, we’re closer in background to AI than to the fields where human flourishing is most-discussed, such as wellbeing economics, positive psychology, or philosophy, and in our journey to find productive connections between such fields and the technical world of AI, we found ourselves often confused (what even is human flourishing, or wellbeing, anyways?) and from that confusion, often stuck (maybe there is nothing to be done? — the problem is too multifarious and diffuse). We imagine that others aiming to create prosocial technology might share our experience, and the hope here is to shine a partial path through the confusion to a place where there’s much interesting and useful work to be done. We start with some of our main conclusions, and then dive into more detail in what follows.
从职业背景来看，我们更接近人工智能领域，而非那些深入探讨人类繁荣的领域，如福祉经济学、积极心理学或哲学。在寻找这些领域与人工智能技术世界之间有效联系的过程中，我们常常感到困惑（究竟什么是人类繁荣或福祉？），并因这种困惑而陷入停滞（也许什么也做不了？——问题太复杂且分散了）。我们设想，其他旨在创造亲社会技术的人可能也有类似的经历，我们希望通过本文在困惑中开辟一条路径，通往一个有许多有趣且有益的工作可以开展的地方。我们先从一些主要结论开始，随后再深入探讨细节。
One conclusion we came to is that it’s okay that we can’t conclusively define human wellbeing. It’s been debated by philosophers, economists, psychotherapists, psychologists, and religious thinkers, for many years, and there’s no consensus. At the same time, there’s agreement around many concrete factors that make our lives go well, like: supportive intimate relationships, meaningful and engaging work, a sense of growth and achievement, and positive emotional experiences. And there’s clear understanding, too, that beyond momentary wellbeing, we must consider how to secure and improve wellbeing across years and decades — through what we could call societal infrastructure: important institutions such as education, government, the market, and academia.
我们得出的一个结论是：我们无法给人类福祉下一个定论，这没关系。哲学家、经济学家、心理治疗师、心理学家和宗教思想家多年来一直在争论这个问题，且至今没有达成共识。与此同时，人们对许多让生活变得美好的具体因素达成了共识，例如：相互支持的亲密关系、有意义且引人入胜的工作、成长与成就感，以及积极的情感体验。此外，人们也清楚地认识到，除了瞬时的福祉，我们还必须考虑如何通过所谓的“社会基础设施”——即教育、政府、市场和学术界等重要机构——来保障和提升跨越数年乃至数十年的福祉。
One benefit of this wellbeing lens is to wake us to an almost-paradoxical fact: While the deep purpose behind nearly everything our species does is wellbeing, we’ve tragically lost sight of it. Both by common measures of individual wellbeing (suicide rate, loneliness, meaningful work) and societal wellbeing (trust in our institutions, shared sense of reality, political divisiveness), we’re not doing well, and our impression is that AI is complicit in that decline. The central benefit of this wellbeing view, however, is the insight that no fundamental obstacle prevents us from synthesizing the science of wellbeing with machine learning to our collective benefit.
这种福祉视角的一个好处是让我们意识到一个近乎矛盾的事实：虽然我们人类所做的一切背后的深层目的都是为了福祉，但我们悲剧性地忽视了这一点。无论是通过个人福祉的常用指标（自杀率、孤独感、有意义的工作），还是社会福祉的指标（对机构的信任、共同的现实感、政治分歧），我们的现状都不容乐观，而我们的印象是，人工智能在这一衰退中难辞其咎。然而，这种福祉观的核心益处在于它提供了一个洞见：没有任何根本性障碍能阻止我们将福祉科学与机器学习相结合，从而造福我们集体。
This leads to our second conclusion: We need plausible positive visions of a society with capable AI, grounded in wellbeing. Like other previous transformative technologies, AI will shock our societal infrastructure — dramatically altering the character of our daily lives, whether we want it to or not. For example, Facebook launched only twenty years ago, and yet social media’s shockwaves have already upended much in society — subverting news media and our informational commons, addicting us to likes, and displacing meaningful human connection with its shell. We believe capable AI’s impact will exceed that of social media. As a result, it’s vital that we strive to explore, envision, and move towards the AI-infused worlds we’d flourish within — ones perhaps in which it revitalizes our institutions, empowers us to pursue what we find most meaningful, and helps us cultivate our relationships. This is no simple task, requiring imagination, groundedness, and technical plausibility — to somehow dance through the minefields illuminated by previous critiques of technology. Yet now is the time to dream and build if we want to actively shape what is to come.
这引出了我们的第二个结论：我们需要基于福祉、且具备可行性的积极社会愿景，以应对强大的人工智能。像以往其他变革性技术一样，人工智能将冲击我们的社会基础设施——无论我们是否愿意，它都将剧烈改变我们日常生活的性质。例如，Facebook 仅在二十年前推出，但社交媒体的冲击波已经颠覆了社会的许多方面——颠覆了新闻媒体和我们的信息共享空间，让我们沉迷于点赞，并用虚壳取代了有意义的人际联系。我们相信，强大的人工智能的影响力将超过社交媒体。因此，我们必须努力探索、构想并迈向一个我们能蓬勃发展的人工智能世界——在这个世界里，它或许能重振我们的机构，赋予我们追求最有意义事物的能力，并帮助我们培养人际关系。这不是一项简单的任务，它需要想象力、脚踏实地和技术上的可行性，以便在以往技术批判所揭示的雷区中翩翩起舞。然而，如果我们想积极塑造未来，现在正是梦想与建设的时刻。
This segues into our final conclusion: Foundation models and the arc of their future deployment is critical. Even for those of us in the thick of the field, it’s hard to internalize how quickly models have improved, and how capable they might become given several more years. Recall that GPT-2 — barely functional by today’s standards — was released only in 2019. If future models are much more capable than today’s, and competently engage with more of the world with greater autonomy, we can expect their entanglement with our lives and society to rachet skywards. So, at minimum, we’d like to enable these models to understand our wellbeing and how to support it, potentially through new algorithms, wellbeing-based evaluations of models and wellbeing training data. Of course, we also want to realize human benefit in practice — the last section of this blog post highlights what we believe are strong leverage points towards that end.
这引出了我们的最后一个结论：基础模型及其未来的部署轨迹至关重要。即使对于我们这些身处该领域的人来说，也很难内化模型进步的速度，以及在未来几年内它们可能变得多么强大。回想一下，以今天的标准来看几乎无法使用的 GPT-2，仅仅是在 2019 年发布的。如果未来的模型比现在的强大得多，并且能以更高的自主性胜任更多世界事务，我们可以预见它们与我们生活和社会的纠缠将急剧上升。因此，至少我们希望使这些模型能够理解我们的福祉以及如何支持它，这可能通过新的算法、基于福祉的模型评估以及福祉训练数据来实现。当然，我们也希望在实践中实现人类的利益——本博文的最后一部分重点介绍了我们认为实现这一目标的有力杠杆点。
The rest of this post describes in more detail (1) what we mean by AI that benefits our wellbeing, (2) the need for positive visions for AI grounded in wellbeing, and (3) concrete leverage points to aid in the development and deployment of AI in service of such positive visions. We’ve designed this essay such that the individual parts are mostly independent, so if you are interested most in concrete research directions, feel free to skip there.
本文的其余部分将更详细地描述：(1) 我们所说的“造福人类福祉的人工智能”的含义；(2) 对基于福祉的积极人工智能愿景的需求；以及 (3) 助力开发和部署服务于此类积极愿景的人工智能的具体杠杆点。我们将本文设计为各部分相对独立，因此如果您最感兴趣的是具体的研究方向，请随意跳至相关章节。