你准备好面对工作的终结了吗，Anthropic幕僚长Avital Balwit最近写了一篇文章，分享了她对人工智能的发展对未来工作的影响，作为世界最前沿的人工智能公司参谋所思所想
接下来的三年可能是我工作的最后几年。我既没有生病，也不是要成为全职妈妈，也没有财务自由到可以自愿退休的地步。我正站在一项技术发展的边缘，如果这种技术到来，可能会终结我现在所知的就业形式
— Avital Balwit
这里分享一下全文，趁着周末大家有兴趣的话看看，全当消磨时间
I work at a frontier AI company. With every iteration of our model, I am confronted with something more capable and general than before. At this stage, it can competently generate cogent content on a wide range of topics. It can summarize and analyze texts passably well. As someone who at one point made money as a freelance writer and prided myself on my ability to write large amounts of content quickly, a skill which—like cutting blocks of ice from a frozen pond—is arguably obsolete, I find it hard not to notice these advances. Freelance writing was always an oversubscribed skillset, and the introduction of language models has further intensified competition.
我在一家前沿的人工智能公司工作。随着我们模型的每一次迭代，我都会面对比之前更强大和通用的技术。现在，它能够胜任生成广泛主题的连贯内容，并能够很好地总结和分析文本。作为一个曾经靠自由写作赚取收入并以快速写大量内容为傲的人，这种技能如今就像从冰冻的池塘中切割冰块一样，已经可以说是过时了，我很难不注意到这些进步。自由写作一直是一个供过于求的技能，而语言模型的引入进一步加剧了竞争
The general reaction to language models among knowledge workers is one of denial. They grasp at the ever diminishing number of places where such models still struggle, rather than noticing the ever-growing range of tasks where they have reached or passed human level. Many will point out that AI systems are not yet writing award-winning books, let alone patenting inventions. But most of us also don’t do these things.
知识工作者对语言模型的普遍反应是否认。他们紧抓着这些模型仍然存在困难的地方，而不是注意到它们在越来越多的任务上已经达到或超过了人类水平。许多人会指出，人工智能系统还没有写出获奖的书籍，更不用说获得专利的发明了。但我们大多数人也不会做这些事情
The economically and politically relevant comparison on most tasks is not whether the language model is better than the best human, it is whether they are better than the human who would otherwise do that task. This makes the objection that AI systems are not yet coding long sequences or doing more than fairly basic math on their own a more relevant one. But these systems will continue to improve at all cognitive tasks. The shared goal of the field of artificial intelligence is to create a system that can do anything. I expect us to soon reach it. If I’m right, how should we think about the coming obsolescence of work?
在大多数任务上，经济和政治相关的比较并不是语言模型是否比最优秀的人类更好，而是它们是否比本来会做这些任务的人类更好。这使得对人工智能系统尚不能编写长序列代码或仅能完成相对基础的数学任务的反对意见更具相关性。但是这些系统将在所有认知任务上继续改进。人工智能领域的共同目标是创建一个能做任何事情的系统。我预计我们很快就会达到这一目标。如果我是对的，我们应该如何看待即将到来的工作的过时？
It is worth noting up front that even today, work is far from the only way to participate in society. Nevertheless, it has proven to be the best way to transfer wealth and resources; it provides personal goods like social connection, status, and meaning; and it offers social goods like political stability.
值得首先指出的是，即使在今天，工作也远不是参与社会的唯一方式。然而，它被证明是转移财富和资源的最佳方式；它提供了社会联系、地位和意义等个人利益；并提供了政治稳定等社会利益
Given this, should we meet the possibility of its loss with sadness, fear, joy, or hope? The overall economic effects of Artificial General Intelligence (AGI) are difficult to forecast, and here I will focus on the question of how people will feel without work—whether they will, or can, be happy. There are obviously other vital questions, like how people will be able to meet their material needs. Many have examined this question, with no final answer yet adopted as official policy for this contingency by any government. I am instead going to do something that may feel like cheating. I will go ahead and assume that people can meet their financial needs through universal basic income or other transfers and will solely concentrate on the question of whether people can and will be happy—or at least as happy as they are now—without work.
鉴于此，我们应该以悲伤、恐惧、喜悦还是希望来面对其可能的消失？通用人工智能（AGI）的整体经济影响难以预测，这里我将重点关注人们在没有工作时的感受——他们是否会，或者能否，感到快乐。显然还有其他重要问题，比如人们如何满足他们的物质需求。许多人已经研究了这个问题，但迄今为止，还没有任何政府将最终答案作为应对这一突发情况的官方政策。我将做一些可能感觉像作弊的事情。我将假设人们可以通过普遍基本收入或其他转移支付来满足他们的财务需求，并将仅关注人们是否能以及是否会感到快乐——或者至少像现在一样快乐——而没有工作
The Obsolescence of Knowledge Work I expect AI to get much better than it is today. Research on AI systems has shown that they predictably improve given better algorithms, more and better quality data, and more computational power. Labs are in the process of further scaling up their clusters—the groupings of computers that the algorithms run on. Machine learning is a young field, with an enormous amount of “low hanging fruit” in terms of discoveries, meaning that researchers continuously find improvements to the algorithms of these AI systems. While an enormous amount of data has already been fed through them, there is still more to be found and it can also be generated by the systems themselves. So, given the “scaling laws”, we can reasonably foresee that these systems will keep getting better—at least until these inputs run out.
知识工作的过时我预计人工智能会变得比现在好很多。关于人工智能系统的研究表明，给出更好的算法，更多和更高质量的数据，以及更多的计算能力，它们会有可预测的改进。实验室正在进一步扩大它们的计算集群——这些算法运行的计算机群组。机器学习是一个年轻的领域，有大量的“低垂果实”可以发现，这意味着研究人员不断找到改进这些人工智能系统算法的方法。尽管已经有大量数据被输入其中，但仍有更多的数据可以被发现，并且也可以由系统本身生成。因此，考虑到“扩展规律”，我们可以合理地预见这些系统将继续变得更好——至少在这些输入耗尽之前。
And at what rate will they get better? Language models are not, for the most part, continuously improving. They get better in discontinuous jumps. A rough analogy to the current LLM process is that making a new model is like baking a cake. You figure out your data and algorithms—like mixing the batter—and then you pretrain the model, that is, run it on a large number of computers for several months—like putting it in the oven—and then at the end you do some “post training”—like frosting and decorating the cake. Post training can adjust the model in certain ways, often to make it more harmless or honest, or to make it particularly good at some specific skill or use case—but most of what matters for the model’s capabilities, at least right now, is the underlying “cake,” and this can’t be easily adjusted without starting over and baking something new. So when it comes to the rate of progress, when models seem to plateau, you should actually assume that that just means that the next model is in the oven but hasn’t come out yet.
那么它们会以什么速度变得更好呢？语言模型大部分时间并不是在持续改进，而是以不连续的跳跃方式变得更好。一个粗略的比喻是，当前的大语言模型（LLM）过程就像烤蛋糕。你要弄清楚数据和算法——就像混合面糊——然后你预训练模型，也就是在大量计算机上运行几个月——就像把蛋糕放进烤箱——最后你做一些“后期训练”——就像给蛋糕涂上糖霜和装饰。后期训练可以在某些方面调整模型，通常是为了使它更无害或诚实，或者使它在某些特定技能或用例上特别优秀——但至少现在，决定模型能力的大部分因素是基础的“蛋糕”，这不能轻易调整，除非从头开始重新烤一个新的。因此，当谈到进展速度时，当模型似乎达到瓶颈时，你实际上应该认为这只是意味着下一个模型还在“烤箱”里，还没有出来。
Many expect AI to eventually be able to do every economically useful task. I think it’s reasonable to say this will happen sooner rather than later, and it’s not obvious how the current economic system or a replacement would function in a world where this happens. I have no special insight into this matter, though, and many people have written on this. Instead, I will focus on the question of what we should hope for when work becomes unnecessary and whether people will be happy under such conditions.
许多人预计人工智能最终能够完成每一项经济上有用的任务。我认为，可以合理地说，这将更早而不是更晚发生，并且在这种情况下，目前的经济系统或替代系统如何运作尚不明显。不过，我对此没有特别的见解，许多人已经对此写了很多。相反，我将专注于当工作变得不必要时我们应该希望什么，以及在这种情况下人们是否会感到快乐
A starting point is that many people without jobs are not particularly happy. The rise of what we call “deaths of despair”—suicides, drug overdoses, and deaths from alcoholism—are notably prominent among working-age men who find themselves unemployed. In many parts of the United States, such deaths are the leading cause of death among men under the age of 50. The impacts of job loss or not finding work also correlate with other negative outcomes: more people live alone, more people experience long-term health issues, and more people report depression and anxiety.
一个出发点是，许多没有工作的人并不特别快乐。所谓的“绝望之死”——自杀、药物过量和酗酒导致的死亡——在发现自己失业的工作年龄男性中尤其突出。在美国的许多地方，这类死亡是50岁以下男性的主要死亡原因。失业或找不到工作的影响还与其他负面结果相关：更多的人独自生活，更多的人经历长期健康问题，更多的人报告抑郁和焦虑。
For many people, work is an essential source of meaning and structure. We derive self-esteem, social ties, and purpose from work, and its absence can lead to a loss of these benefits. However, it is important to note that not all work is equally fulfilling or meaningful. Many jobs are mundane, repetitive, and devoid of any deep sense of purpose. The challenge then becomes figuring out how to provide individuals with the benefits of work—such as purpose, social connection, and structure—without the necessity of traditional employment.
对许多人来说，工作是意义和结构的基本来源。我们从工作中获得自尊、社会联系和目标，缺少它们可能导致这些利益的丧失。然而，重要的是要注意，并非所有的工作都同样充实或有意义。许多工作是平凡的、重复的，缺乏任何深刻的目标感。因此，挑战在于如何在不需要传统就业的情况下，为个人提供工作的这些好处——如目标、社会联系和结构。
There are some potential solutions. One possibility is the idea of a “post-work” society where people engage in activities that are intrinsically rewarding rather than economically necessary. This could include volunteering, artistic pursuits, education, and other forms of personal development. Another possibility is the creation of new forms of work that are less tied to economic necessity and more to personal fulfillment. However, transitioning to such a society will likely require significant cultural, social, and policy changes.
有一些潜在的解决方案。一个可能性是“后工作”社会的概念，在这种社会中，人们从事的是内在回报的活动，而不是经济必需的活动。这可能包括志愿服务、艺术追求、教育和其他形式的个人发展。另一个可能性是创造新的工作形式，这些工作形式与经济必要性联系较少，而与个人充实感联系更多。然而，向这样的社会过渡可能需要重大的文化、社会和政策变革。
Moreover, we must consider the importance of providing people with the means to meet their basic needs in the absence of traditional work. Universal basic income (UBI) is one proposed solution that aims to ensure everyone has a baseline level of financial security. There are also proposals for more robust social safety nets and public services that can help mitigate the impacts of job loss.
此外，我们必须考虑在没有传统工作的情况下，为人们提供满足其基本需求的手段的重要性。普遍基本收入（UBI）是一种提议的解决方案，旨在确保每个人都有基本的财务保障。还有一些关于更健全的社会安全网和公共服务的提议，可以帮助减轻失业的影响
In conclusion, the obsolescence of work due to advances in AI presents both challenges and opportunities. While the loss of traditional employment could lead to negative social and psychological outcomes, it also opens the door to rethinking the role of work in our lives and exploring new ways to achieve personal and societal well-being. Addressing these challenges will require a multifaceted approach that includes economic, social, and policy interventions. With thoughtful planning and a willingness to adapt, we can navigate this transition in a way that enhances human happiness and fulfillment.
总之，由于人工智能的进步，工作的过时既带来了挑战，也带来了机遇。虽然失去传统就业可能导致负面的社会和心理结果，但这也为重新思考工作在我们生活中的角色和探索实现个人和社会福祉的新方式打开了大门。应对这些挑战将需要多方面的方法，包括经济、社会和政策干预。通过周密的规划和适应的意愿，我们可以以一种提升人类幸福和充实感的方式，顺利度过这一过渡期
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