AI Practical Use #3#: Let AI help you with Excel data analysis. AI 实用玩法第 3 个： 让 AI 帮你做 Excel 数据分析。 Here is a very common office situation: You have an Excel file with sales data, costs, profit, regions, products, and dates. Normally, you may spend 2 hours writing formulas, checking data, making summaries, and building charts. But with AI, you can finish the first draft in about 10 minutes. 一个很常见的办公场景： 你手里有一份 Excel 数据， 里面有销售额、成本、利润、区域、产品、日期。 以前你可能要花 2 小时： 写公式、查数据、做汇总、看趋势、做图表。 现在可以先交给 AI， 10 分钟生成初步分析结果。 You don’t need to manually type every complex formula. Let AI help you: Build formulas Summarize key findings Find abnormal data Compare trends Suggest chart formats Create a report structure 你不需要自己一个个输入复杂函数。 可以让 AI 帮你： 生成公式 总结关键结论 找出异常数据 对比趋势变化 建议图表形式 生成汇报框架 Here is a simple prompt: 这里有一个简单提示词： Please analyze this Excel data. Help me build the right formulas, summarize the key findings, find possible errors or abnormal values, and suggest the best chart or report format. I will review and verify the final results. 中文版本： 请分析这份 Excel 数据。 帮我生成合适的公式，总结关键结论，找出可能的错误或异常值，并建议最适合的图表或汇报格式。 最终结果由我来审核确认。 The key idea is simple: AI does the heavy first draft. You review the logic and final result. 核心思路很简单： AI 负责先把复杂工作做出来， 你负责审核逻辑和最终结果。 Before: 2 hours manually writing formulas. After: 10 minutes with AI assistance. 以前： 手动写公式、做分析，可能要 2 小时。 现在： 借助 AI，10 分钟先完成初稿。 AI is not here to replace your judgment. It helps you save time on repetitive work, so you can focus on checking, thinking, and making better decisions. AI 不是替代你的判断力。 它是帮你节省重复劳动的时间， 让你把精力放在审核、思考和决策上。 Let AI write the formulas. You review the results. 让 AI 写公式， 你负责审核结果。 That is a smarter way to work. 这才是更聪明的办公方式。 #ChatGPT# #AI# #AITools# #Excel# #ExcelTips# #DataAnalysis# #Productivity# #WorkSmarter# #OfficeWork# #BusinessTools# #Automation# #DigitalTools# #TechTips# #FutureOfWork# #PromptEngineering#

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Jackson hole会议有关ai的摘录 1，AI的发展速度与经济增长潜力 Capital and labor have combined to create the large language models at the heart of AI. Users buy tokens to gain access to the models. Reports put annualized token sales for the two leading labs alone at more than $100 billion—an increase of 500-plus percent from a year ago. 资本和劳动力共同创造了处于AI核心地位的大语言模型。用户通过购买Token来使用这些模型。据报道，仅两家领先实验室的Token年化销售额就已超过1,000亿美元，同比增长逾500%。 2. AI可能成为新的生产要素 The Fed watches all of this attentively. We recognize that AI is a new variable—potentially a new factor of production—that will have consequences for both the economy and the conduct of monetary policy. It opens some major lines of inquiry:美联储正在密切关注这一切。我们认识到，AI是一个新的变量——甚至可能成为一种新的生产要素——它将同时影响经济运行和货币政策的实施。这带来了一些重要的研究问题： Will the application of AI cause a significant, sustained rise in productivity across the economy? And if so, when?AI的应用是否会推动整个经济体的生产率显著且持续提高？如果会，这种变化将在何时出现？ Will token usage be complementary or competitive to labor? Will the next generation of AI models demand even greater capital intensity, or will the models themselves help devise a capital-light solution? Token的使用与劳动力之间究竟是互补关系，还是竞争关系？下一代AI模型是否需要投入更多资本，抑或这些模型本身能够帮助开发出资本需求较低的解决方案？ 3. AI产业的收益将由谁获得 Among the other yet unknowns is the resulting market structure. It's not obvious where the returns on capital will land or on what timescale. Early on, how much of the surplus goes to owners of scarce assets—AI labs, chipmakers, energy producers, and cloud providers? Over time, how much of that value accrues to businesses and consumers? What are the broad implications for workers and for the employment side of the Fed's mandate?另一个尚未确定的问题，是AI最终会形成怎样的市场结构。资本收益最终将流向哪里、又将在多长时间内实现，目前并不明确。在发展初期，经济剩余中有多少会流向稀缺资产的所有者——包括AI实验室、芯片制造商、能源生产商和云服务提供商？随着时间推移，又有多少价值会由一般企业和消费者获得？AI将对劳动者以及美联储就业使命产生哪些广泛影响？ 4. Token价格与模型分层 Likewise, we don't yet know the equilibrium price of the tokens. Might there be a heterogeneity of tokens, such that growing sums will be paid for access to the best models at the frontier? Will token prices for older models fall to the level of their marginal cost?同样，我们尚不知道Token的均衡价格。未来是否会出现明显的Token分层，使用户愿意支付越来越高的费用来使用最先进的前沿模型？较旧模型的Token价格是否会下降至其边际成本水平？ 5 美联储将成立专项工作组 We will be thinking through these matters with the help of a task force on productivity and jobs. My early check-ins with the leaders of that task force, and the four others, have been encouraging.我们将借助一个专门研究生产率与就业问题的工作组，深入分析这些事项。我与该工作组以及另外四个工作组负责人的初步交流令人鼓舞。To be clear, though, their recommendations will come later and have no bearing on decisions we make in the current policy conjuncture. But I believe that for future policy challenges, this intellectual investment today will leave us far better prepared.不过需要明确的是，这些工作组的建议将在以后提出，不会影响我们在当前政策环境下所作的决定。但我相信，今天投入的这些研究工作，将使我们在应对未来政策挑战时准备得更加充分。 6. AI已经成为资本开支增长的重要动力 Business capital expenditures—the seed corn of future economic growth—are rising rapidly. The four-quarter change in investment in equipment and intangibles has been around 9 percent, its highest growth rate since 2021. More than half of the cap-ex growth this year can likely be ascribed to the buildout related to Expectations for growth in both cap-ex and corporate earnings are running quite high. I will continue to watch the change in their growth rates, the second derivative. The follow-on effects on asset prices, business confidence, consumer income, and spending are equally important to gauge.市场对资本支出和企业盈利增长的预期都相当高。我将继续观察其增长速度的变化，也就是所谓的“二阶导数”。AI投资对资产价格、企业信心、消费者收入和消费支出所产生的后续影响，同样值得密切评估。 我的看法：Warsh这篇演讲把AI视为一种可能出现的新生产要素，这是一个偏积极的看法。美联储最关心的并非单纯的技术进步，而是AI对生产率、就业、产业收益分配、Token定价以及货币政策的长期影响。Kevin同时认为，AI投资已经对现实经济产生显著作用——今年美国资本支出增长的一半以上可能来自AI建设。

3 hours into @longbow_gg and we have 223 launches with a few coins cracking $1mn already. For a few of them, people have found a way to tie to @Lighter_xyz for pre-IPO tokens and we are seeing some backed by Anthropic and OpenAI. Neat...

3/ available now in Meta Model API and Muse Code. we are cooking! bigger and more capable muse models are close behind 🍉, along with Muse Spark open weights release, and exciting new products. more here →