Use LLM (e.g., ChatGPT) to learn *** and its impact

Research

• Dimitris Bertsimas and Georgios Margaritis, Robust and Adaptive Optimization under a Large Language Model Lens, arXiv:2501.00568. 

一些常聽的 Podcast 節目和培養英文聽力的方法

一些常聽的 Podcast 節目 (*)，適合坐車、(讓眼睛) 休息、睡前、或運動時聽 (Some frequently listened-to podcasts (*) suitable for listening while traveling, giving your eyes a rest, before bed, or during exercise.) 

• Acquired (You could look at its transcript using iPhone.)
• Google Part III: The AI Company 
• I am listening to it the third time to fill in some missed details.
• In a rapidly changing world, learning how technology, corporations, and uncertainty management interact is essential.
• Hinton did split the money fairly with his students, so the message in the podcast is incorrect.
• Google search: Stories about its technology and business model
• How ARM Became The World’s Default Chip Architecture (with ARM CEO Rene Haas)
• The Complete History & Strategy of Microsoft: Vol. I, Vol. II 
• Renaissance Technologies: The best-performing investment firm of all time. A book. The hosts even talk about the hidden Markov process!
• TSMC Founder Morris Chang
• American Public Media: Marketplace
• Asianometry: Global Semiconductor Issues, Semiconductor "Course", TSMC Analysis
  

Guides for graduate students and my Laboratory members

Knowledge to master for a better foundation (and future)

Tools and general: 

•  Adi Ignatius, “We Want to Make Ourselves Better”: The HBR Interview with Bob Sternfels, Harvard Business Review, January–February 2026.  (new)

作業研究和機器學習

課程

• 作業研究 (上): 3.8 迴歸，11.2 迴歸，12.3.1 類神經
• Google Scholar: generative ai optimization
• 作業研究 (下): 2 Robust Optimization (穩健最佳化)，3 適應穩健最佳化，5 資料驅動的報童模型
• Applications in generative AI: diffusion probabilistic models 
• Dmytro Kuzmenko, Denoising diffusion probabilistic models
• Lilian Weng, What are Diffusion Models?
• Dimitris Bertsimas and Georgios Margaritis, Robust and Adaptive Optimization under a Large Language Model Lens, arXiv:2501.00568.
• 最佳化和機器學習：

  機器學習和作業研究的奇妙結合

中國無人計程車便宜效率高

施慧中 / 編譯，中國無人計程車便宜效率高 衝擊傳統駕駛就業市場，公視，2024-08-23 

人工智慧時代，無人計程車成為時代趨勢。中國百度旗下的「蘿蔔快跑」是先驅者，在武漢有500多輛規模，超便宜價格戰，讓傳統計程車駕駛面臨飯碗不保的壓力。專家指出AI全面取代人類駕駛，還有至少5到10年，但科技與就業之間的衝突仍須及早應對。

學習數學的四個層次：(3) 在許多行業的應用

學習數學的四個層次：(0) 如何學數學，(1) 代表具備基礎的知識與能力，(2) 邏輯推理和抽象思考的能力，(3) 在許多行業的應用，(4) 純粹滿足好奇心或求知慾。

2015/12/1 初稿，持續更新中。

一般性說明

• 數學是科學之母，科學則是工業的基礎，所以大學工學院的數理化課程總學分超過 1/3。可以參考如何選填大學志願。
• 應用在不同的領域 (理工商醫農、教育)，如財務工程、設計電腦、貨物產銷、工程師、使用統計學分析學習成效等等。
• 抽象的模式與思考的方式，適用於現在與未來的應用，以微分為例，物理學的距離微分是速度，經濟學中成本的微分是邊際成本，電子學的電荷微分是電流。也就是說，可以使用函數表示任何待解的問題，函數的微分便可以研究其變化和極值的情況，例如機器學習中，超參數 (hyperparameter) 的學習 。
• 基本的原則變動不大，微積分、機率和統計學、和線性代數已經有 200 年以上的歷史，可幫助未來的自我學習。許多人說學校學的東西，畢業後立即過時或沒用，我覺得很疑惑。大學只是基礎教育，必須不斷地學習新的東西，以因應產業和職務的變化；最近熱門的大數據 (big data) 和人工智慧 (artificial intelligence)，其數學基礎正是這些課程。
  

Applications of Operations Research (作業研究) (including Optimization)

為了提高同學們的學習動機，提供以下相關的資訊，以幫助同學們找到方向。也和暑期實習和未來就業中，決策支援系統中的演算法有密切關聯。以下許多的內容屬於碩博士階段的課程，也可以增加同學們就讀研究所的動機：

• Journals: 
• INFORMS Journal on Applied Analytics
• INFORMS is the leading international association for Operations Research & Analytics professionals.
• The mission of INFORMS Journal on Applied Analytics is to publish manuscripts focusing on the practice of operations research and management science and the impact this practice has on organizations throughout the world. 
• Good topics to be explored for the final project
• Ramayya Krishnan and Pascal Van Hentenryck, editors, Advances in Integrating AI & O.R., INFORMS EC2021, Volume 16, April 19, 2021.

Machine-Guided Discovery of a Real-World Rogue Wave (瘋狗浪) Model

台北天文館，AI 找到如何預測巨浪的公式，2023 年 12 月 02 日 

Dion Häfner, Johannes Gemmrich, Markus Jochum, Machine-Guided Discovery of a Real-World Rogue Wave Model,  Proceedings of the National Academy of Sciences (2023), 120(48). (arXiv, data, code)

智慧農業的成功因素

林一平，智慧農業的成功因素，電子時報，2023-08-21

在台灣，農業物聯網感測設備供應商眾多，然而通訊技術和資料傳輸格式卻千差萬別，導致資料在不同系統間的流通和加值應用面臨著困難。

為了解決這一重要問題，農業部於2023年4月27日推出「智慧農業感測資料格式標準與測試規範」。透過推動資料格式的標準化，提高農業物聯網應用領域中資料串接的效率，同時也降低開發成本，推動農業物聯網的深入應用。

友達 AI 數位化打造高效供應鏈

吳珍儀，友達AI數位化打造高效供應鏈 蟬聯美國製造領導獎，Yahoo 財經，2023年6月29日

友達在智慧研發環節開發色彩飽和度模擬系統，引入大數據分析演算法，進行材料選用的相關模擬，提前預測客戶喜好的顏色和材料穿透率，使設計階段效率提升83%。在面板前段製程中，建立元件標準化資料庫並結合佈局設計演算法，成功縮短50%的光罩設計時程。

Generative AI

S. Huang, P. Grady, and GTP-3, Generative AI: A Creative New World, Sequoia Capital (紅杉資本), September 19, 2022.

A powerful new class of large language models is making it possible for machines to write, code, draw and create with credible and sometimes superhuman results.

Using AI to Accelerate Scientific Discovery

Demis Hassabis, Using AI to Accelerate Scientific Discovery, Institute for Ethics in AI Oxford, 2022.

Corporate spending on AI and 2 nice reports

Inside AI  (Could subscribe to free newsletters)

學習大數據 (big data) 的技能

一些工具或念個學位

可以參考 DS Examiner, Data Scientist Foundations: The Hard and Human Skills You Need, November 8, 2013

或者  Insight Data Science Fellows Program 說明了可能使用的工具

1. Software Engineering Best Practices: Learn how to contribute to a large code-base and instrument a web application to collect data. Tools you may learn: Python, Git, LAMP web stack, Javascript, Flask.
2. Storing and Retrieving Data: How to clean data, store it in the appropriate database or distributed data storage system and then run queries to retrieve the information needed for analysis. Tools you may will learn: MySQL, Hadoop, Hive.
3. Statistical Analysis & Machine Learning: Learn industry best practices for doing basic and advanced statistical analysis on large data sets. Tools you may learn: R, NumPy & SciPy, Mahout.
4. Visualizing and Communicating Results: Learn how to effectively communicate your findings visually and verbally. Tools you may learn: D3 Javascript library, visualization and presentation best practices. 

Training One Million Machine Learning Models in Record Time with Ray

Eric Liang and Robert Nishihara, Training One Million Machine Learning Models in Record Time with Ray, Anyscale, December 17, 2022.

Ray and Anyscale are used by companies like Instacart to speed up machine learning training workloads (often demand forecasting) by 10x compared with tools like Celery, AWS Batch, SageMaker, Vertex AI, Dask, and more.

In this blog, we’ll cover:

• Why companies are doing many model training
• How to use Ray to train multiple models
• The properties of Ray that enable efficient many model training 

半導體產業的挑戰

魏哲家，半導體產業的挑戰，玉山科技論壇，2022-12-17 

NeurIPS 2022

 Some information about NeurIPS 2022: 

• Outstanding Paper, Datasets And Benchmarks
• Nice summary by Jim (Linxi) Fan
• Google search "NeurIPS 2022 summary"
• 李宏毅 (Hung-yi Lee)，Chat GPT (可能)是怎麼煉成的 - GPT 社會化的過程

Data-driven research in retail operations

M. Qi, H.Y. Mak, and Z.J.M. Shen, Data‐driven research in retail operations—A review, Naval Research Logistics, 2020, 67 (8), 595-616. (Open access)

We review the operations research/management science literature on data-driven methods in retail operations. This line of work has grown rapidly in recent years, thanks to the availability of high-quality data, improvements in computing hardware, and parallel developments in machine learning methodologies. We survey state-of-the-art studies in three core aspects of retail operations—assortment optimization, order fulfillment, and inventory management. We then conclude the paper by pointing out some interesting future research possibilities for our community.

Garrett van Ryzin talks about optimization

以前教營收管理的時候，讀了不少哥倫比亞商學院 van Ryzin 教授的文章，其中一篇說，他們的研究是在解決10年後的問題。各位注意喔，是商學院！

最近在讀一本書，某教授寫的序，開頭四個字，就是學用落差。

INFORMS Analytics Collections Vol. 16: Advances in Integrating AI & O.R.

Ramayya Krishnan and Pascal Van Hentenryck, editors, Advances in Integrating AI & O.R., EC2021, Volume 16, April 19, 2021.

The INFORMS strategic initiative in AI resulted in a white paper that summarized the findings and provided a number of recommendations for the INFORMS community. This volume of Editor’s Cut complements the white paper and assembles a collection of papers from the INFORMS community that bridge the AI and O.R. communities. The papers are grouped into five categories:

1. Blending Predictive and Prescriptive Methods
2. AI/ML for Optimization Problems
3. Integrating Predictive and Causal Inference
4. Games, Control, Data-intensive Preference Estimation
5. Unstructured Data Analytics, AI and OR/MS – Innovative Applications