歲月靜好

參加了數百次的系務會議，針對各種大大小小的提案和投票，每位老師都會拿到紙本說明，由主持人或系助理先朗讀條文內容，讓每位老師表達不同的看法和意見，再投票。有時候，遇到爭論不休的條文，一時之間老師們找不到共識，也不會強行表決；會議主席會裁決，再找時間討論。這就是民主的程序。

也曾擔任兩年的校預算委員會委員，每學期開會十多次，都會邀請提案的單位前來說明。因為年度預算有限，頂多是刪減預算，從來沒有將預算刪成負數。

台灣為什麼重要？

Shelley Rigger, Why Taiwan Matters: Small Island, Global Powerhouse

姚睿譯，台灣為什麼重要？美國兩岸研究權威寫給全美國人的台灣觀察報告

台灣漢光演習五大看點

蔣宜婷，台灣「漢光演習」五大看點：動員創新高、超市首度納入「持久戰」部署，BBC中文記者，2025年7月9日

Statistical Modeling: The Two Cultures

Cynthia Rudin, Leo Breiman, the Rashomon Effect, and the Occam Dilemma, arXiv:2507.03884, 2025.

In the famous “Two Cultures” paper, Leo Breiman provided a visionary perspective on the cultures of “data models” (modeling with consideration of data generation) versus “algorithmic models” (vanilla machine learning models). I provide a modern perspective on these two approaches. One of Breiman’s key arguments against data models is what he called the “Rashomon Effect,” which is the existence of many different-but-equally-good models. The Rashomon Effect implies that data modelers would not be able to determine which model generated the data. Conversely, one of his core advantages in favor of data models is simplicity, as he claimed there exists an “Occam Dilemma,” i.e., an accuracy-simplicity tradeoff, where algorithmic models must be complex in order to be accurate. After 25 years of more powerful computers, it has become clear that this claim is not generally true, in that algorithmic models do not need to be complex to be accurate; however, there are nuances that help explain Breiman’s logic, specifically, that by “simple,” he appears to consider only linear models or unoptimized decision trees. Interestingly, the Rashomon Effect is a key tool in proving the nullification of the Occam Dilemma. To his credit though, Breiman did not have the benefit of modern computers, with which my observations are much easier to make.

The Batch by A. Ng

weekly summary of AI and more by a. Ng. Free subscription and enjoy reading/Listening like I  do.

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• issue 309: Inside Walmart’s AI App Factory (Walmart is no. 1 company in revenue.
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• Large scale system: The system aggregates data generated by 240 million customers and 2 million store personnel, feeding applications that streamline operations among 100,000 suppliers, 150 distributors, and 10,000 retail venues in 19 countries.

雷鋒網，年終收藏，吳恩達盤點 2020 年度 AI 熱門事件，2020 年 12 月 31 日