Shaohui Wang @Quantwang303
Risk Modeling Actuary | PhD (Dr. rer. nat.) in Financial Mathematics | AI & Blockchain | Lifelong Learning | 笃行致远 Munich, Germany Joined October 2021-
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兼听则明
💯
你仔细看随行名单: 贝莱德、Visa、万事达、高盛、花旗、英伟达、马斯克…… 商界团队总共18人,接近一半的人都和Crypto有密切关系.... 如果我们抛开传统“卖飞机、卖芯片”的想法,大胆一点,我只是假设啊,只是假设 这只美国下一代金融基础设施团队,真正想做的,根本不只是 BTC ETF,而是:
@myanTokenGeek 更觉得大家会成为脑力行业的“清洁工”,按需给AI打下手,清理边边角角。都会变成打零工的。包工头儿或者中介会是谁呢?
深有感触,尽管经验和经历不具如何可比性。
喜欢这种有大量明确不确定性结论的文章,不确定可能就是最大的确定性。好奇这个演化过程的走向。
把20年专家经验编码成AI Agent:一个精算师的实践笔记 上周末做了一个实验:把自己20年的精算和金融数学经验,编码进一个AI agent的system prompt。不是fine-tuning,不是暴力RAG检索,而是把专家判断的行为模式提炼成结构化的domain knowledge。 方法 从8个真实工作场景出发(全部anonymized),用”叙述-提取”法:我讲当时做了什么,AI提取meta-pattern。最终产出7个domain YAML,覆盖专业身份、模型可信度评估、假设挑战、ESG建模、监管-商业接口、风险到决策翻译、沟通与判断。 每个domain包含:core patterns(行为规则)、decision heuristics(if-then可执行规则)、anti-patterns(不该做什么)。总共约4000 tokens,注入system prompt。 结果 5轮测试,agent表现出三个vanilla Claude不会自发产生的行为: 1. 质疑问题本身。 当被问”这是校准问题还是市场变动”时,agent先说”这个二选一本身就是错误的framing”。 2. 识别不可妥协边界。 当被问能否用单一死亡率表简化IFRS 17时,agent识别出这不是trade-off而是binary wrong。 3. 在正确的地方停下。 给出管理层选项,但不替管理层做决定——这是senior actuary最难学的一课。 关键洞察 专家知识不是知识,是行为。 面试式提取(“你怎么看X?”)产出的是教科书内容。Case-based提取(“你在那个场景下做了什么?”)产出的是决策模式。后者才是agent的真正value-add。 Claude(或任何frontier LLM)已经知道Hull-White模型、IFRS 17 、martingale test。这些是training data里有的。Agent真正增加的是behavioral layer:先check什么、在哪里停、对谁说什么。 这跟我的professional identity文件里写的一致:答案几乎总是简单的,价值在于到达答案的路径。 下一步:从declarative到procedural 看了Nous Research的Hermes Agent github.com/nousresearch/h…后意识到,我们还缺一层:procedural memory。 现有的domain YAMLs是declarative knowledge——原则和heuristics。但agent完成一个复杂任务后,应该能把successful workflow保存为可复用的procedure。下次遇到类似问题,直接调用,不重新推理。这就是”自己成长”的真正机制。 Architecture上的表达:对外(数据、用例)开放可调试,对内(经验、技能)封闭可演进。每做一个case,agent变强一点。 给同行的建议 如果你也是某个领域的资深专业人士: ∙从case开始,不从系统架构开始。5-8个关键决策场景就够了。 ∙提取行为模式,不是知识点。“我在X情况下总是先做Y”比”Y很重要”有用100倍。 ∙先证明对你自己有用,再考虑产品化。如果你自己要改60%以上的output,agent还不够好。 Agent的价值 = 你的判断力 × AI的scale。判断力是稀缺的那一侧。 Built with Claude (Opus for reasoning, Sonnet for execution). All domain knowledge is company-independent.
@karpathy LLM Wiki说到核心:RAG没有accumulation。我做 digital twin,2500+ PDFs检索没问题但synthesis每次从零来。真正值钱的是编译过的expert judgment。RAG广度+Wiki深度,共存不二选一。苟日新,日日新。Opus aided.
@CSchlauf @Emanuel86781 @Salim767715 Thanks for sharing. Looks not fine,any explanation? Not a criticism, just curious.
整好最近在构建个人知识库,学习一下。
Wow, this tweet went very viral! I wanted share a possibly slightly improved version of the tweet in an "idea file". The idea of the idea file is that in this era of LLM agents, there is less of a point/need of sharing the specific code/app, you just share the idea, then the
Worth reading
感谢精心的整理
一键三连的节奏啊
突发新闻:💥💥 🇺🇸CNBC刚刚宣布,银行和加密公司之间的妥协方案可能会在今天宣布🤯 加密市场组织法案即将最后通过💥💥 一旦该法案签署成为法律,就等待着万億的流动进入加密市场吧🚀
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