夜雨聆风
1.英译汉（部分原文，如需全文可点击下方链接）
来源：《经济学人》2024年4月4日
https://www.economist.com/finance-and-economics/2024/04/04/the-federal-reserve-cleans-up-its-money-printing-mess
Apr 4th 2024|Washington, DC|4 min read
At this point, almost everyone in global markets is familiar with the notion of higher-for-longer interest rates.
Soon, they are likely to meet another concept as important for understanding central-bank policy: less-for-longer quantitative tightening (QT).
This phrase describes how the Federal Reserve intends to continue reducing its assets to undo its huge bond purchases during the covid-19 pandemic.
It hopes that a less-for-longer approach will ultimately leave it with a smaller balance-sheet than would otherwise be the case.
This may all seem quite technical.
Indeed, in one metaphor much liked by Fed officials, tracking QT should be as interesting as watching paint dry.
But the very dullness—if it remains that way—has crucial implications, because it would help to make balance-sheet expansion and contraction a staple in central banks’ tool kits for staving off financial crises.
Although other monetary authorities are also in the midst of QT, the Fed plays a dominant role in this experiment as the central bank for the world’s biggest economy.
In any case, the big picture is just how few ripples the central bank’s balance-sheet reduction has caused so far—a contrast with both the turbulence of 2019 and the “taper tantrum” of 2013, when the Fed first discussed plans for trimming asset purchases.
“People are getting more used to thinking about balance-sheet tools, and the Fed is more used to communicating them,” says William English, a former Fed economist.
Watching paint dry is boring.But a well-painted wall can be lovely.
2.汉译英
没有找到原文，大致内容如下：
ST:
我国近期经济指标表现良好, 外贸逆势增长，货物贸易同比增长百分之…高科技产业增加值增长同比增长…7.3%
大模型deepseek，EAST（原文给出了全拼）
高质量发展，根本目的是….“投资人民”，国办中办发布中共中央办公厅国务院办公厅《关于进一步保障和改善民生着力解决群众急难愁盼的意见》
全面闭关封岛海南自贸港…努力打造…我国新时代对外开放的重要窗口
3.校审
（1）来源：标题为Why AI hallucinations can be a good thing（鼠标放在标题上可看到具体网址）
Please use the sharing tools found via the share button at the top or side of articles. Copying articles to share with others is a breach of FT.comT&Csand Copyright Policy. Email licensing@ft.com to buy additional rights. Subscribers may share up to 10 or 20 articles per month using the gift article service. More information can be found on the Help FAQ on gift articles. https://www.ft.com/content/3b88cbd7-e72d-48c7-badc-096006488c36?syn-25a6b1a6=1ST:The writer is founder of Sifted, an FT-backed site about European start-ups The tendency of generative artificial intelligence systems to “hallucinate” — or simply make stuff up — can be zany and sometimes scary, as one New Zealand supermarket chain found to its cost. After Pak’nSave released a chatbot last year offering recipe suggestions to thrifty shoppers using leftover ingredients, its Savey Meal-bot recommended one customer make an “aromatic water mix” that would have produced chlorine gas. Lawyers have also learnt to be wary of the output of generative AI models, given their ability to invent wholly fictitious cases. A recent Stanford University study of the responses generated by three state of the art generative AI models to 200,000 legal queries found hallucinations were “pervasive and disturbing”. When asked specific, verifiable questions about random federal court cases, OpenAI’s ChatGPT 3.5 hallucinated 69 per cent of the time while Meta’s Llama 2 model hit 88 per cent. To many users, generative AI’s hallucinations are an irritating bug that they assume the tech companies will fix one day, just like email spam. To what extent companies can do so is currently a subject of active research and fierce contention. Some researchers argue that hallucinations are inherent to the technology itself. Generative AI models are probabilistic machines trained to give the most statistically likely response. It is hard to code in human traits such as common sense, context, nuance or reasoning. While technologists try to figure that one out, some users find machine-generated fiction to be an exciting feature and have been exploring its creative possibilities. “I love hallucinations,” says Martin Puchner, a Harvard University professor and author. “They are so cool when it comes to artistic creation.” As the author of Culture: A New World History, Puchner has a distinctive view of creativity. In his book, he argues that for millennia humans have been assimilating the inputs of preceding generations and other cultures and mixing them up to generate fresh syncretistic outputs. Consider how much imperial Rome borrowed from ancient Greece, or the Italian renaissance drew from Arabic scholarship, or how Japan adopted Chinese writing and philosophy, or Jamaican Rastafarian culture absorbed Ethiopian traditions. Every civilisation, Puchner writes, tends to overestimate the originality of their own culture to prop up dubious claims of superiority and ownership. “Such claims conveniently forget that everything comes from somewhere, is dug up, borrowed, moved, purchased, stolen, recorded, copied, and often misunderstood,” he writes. “Culture is a huge recycling project.” The parallels with generative AI are striking. To some extent, our machines are doing today what humans have been doing forever: mashing up different cultural inputs to generate slightly varied outputs. In that sense, generative AI can be seen as a giant cultural syncretism machine. Reliant on imperfect, partial, data and overconfident in generalising from the particular, machines may be more like fallible humans than we sometimes imagine. Hallucinations may not be so much an algorithmic aberration as a reflection of human culture. That all sounds abstract. What does it mean in practice? In short, everything depends on the use case. Generative AI models can be a fantastic tool for enhancing human creativity by generating new ideas and content, especially in music, images and video. If prompted in the right way, these models can act as a useful sparring partner and a tireless source of inspiration for creative projects. They can be the algorithmic equivalent of thinking outside the box. Puchner has himself been experimenting with customised chatbots to converse with historical figures, such as Aristotle, Confucius and Jesus, based on their words and ideas. “Prompt engineering should be part of Harvard’s syllabus,” Puchner says. The founder of one generative AI company tells me we are rapidly entering the age of “generative media”. “The internet and social media brought the cost of distribution down to zero. Now, thanks to gen AI, the cost of creation is also going close to zero,” he says. That trend could lead to an explosion of creativity, for both good and bad. It may also deepen concerns over disinformation and intellectual property theft. Impressive as it is as a fiction writer, generative AI still has a long way to go when it comes to reliable non-fiction. That is fine so long as we use it as a tool to enhance human capabilities rather than imagine it as an agent that can replace all human processing power. To hallucinate a phrase: caveat prompter.Martin Puchner's customized GPTs, which allow you to converse withhistorical figures such as Socrates, The Buddha, and Du Bois, were featured in a recent article from the Financial Times. An excerpt is included below: "Generative AI models can be a fantastic tool for enhancing human creativity by generating new ideas and content, especially in music, images and video. If prompted in the right way, these models can act as a useful sparring partner and a tireless source of inspiration for creative projects. They can be the algorithmic equivalent of thinking outside the box. Puchner has himself been experimenting with customised chatbots to converse with historical figures, such as Aristotle, Confucius and Jesus, based on their words and ideas. 'Prompt engineering should be part of Harvard’s syllabus,' Puchner says."
（2）.汉译英：未找到完整原文，参见：
《中共中央国务院关于深化养老服务改革发展的意见》
https://www.gov.cn/zhengce/202501/content_6996776.htm
原文关键词：
养老服务 养老相关法律 养老基础设施建设 养老服务规划 科学化 基层 城乡差异 老龄友好...
