打造高效AI推薦系統

 蔡銘仁，打造高效AI推薦系統　林永隆率創鑫智慧挺進世界，EE Times Taiwan，2022-10-27

新創公司創鑫智慧僅成軍第三年，首款人人工智慧(AI)加速晶片就採用成本高昂的台積電7nm製程，吸引業界關注；董事長暨執行長林永隆在半導體業界累積近40年的專業資歷，更讓外界對公司的前景抱有高度期待。他們擘劃的宏大願景，是立志成為世界級的AI加速器供應商。...

思維的製程

彭建文，思維的製程：台積電教我的思維進階法，練成全局經營腦和先進工作術，商業周刊，2023

職場不怕碰上難題，怕的是不會聰明解決。

學習台積電多年淬鍊的系統性問題解決策略，

你也能優化思維的製程，扎穩職涯腳步，累積世界第一競爭力。

What makes us happy in life?

Marc Schulz and Robert Waldinger, An 85-year Harvard study found the No. 1 thing that makes us happy in life: It helps us ‘live longer’,  CNBC, Feb 10, 2023.

Contrary to what you might think, it’s not career achievement, money, exercise, or a healthy diet. The most consistent finding we’ve learned through 85 years of study is: Positive relationships keep us happier, healthier, and help us live longer. Period. 

Multimodal artificial intelligence

Jessica Leung, Omega Rho Keynote: Artificial Intelligence and the Future of Universities, ORMS Today, 2022.

Léonard Boussioux, Cynthia Zeng, Théo Guénais, and Dimitris Bertsimas, Hurricane Forecasting: A Novel Multimodal Machine Learning Framework, Weather and Forecasting, March 2022, 37(6), pp. 817–831.

Soenksen, L.R., Ma, Y., Zeng, C. et al. Integrated multimodal artificial intelligence framework for healthcare applications. Nature Machine Intelligence 5, 149 (2022). https://doi.org/10.1038/s41746-022-00689-4. (Data and Code)

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. 

Generalized Synthetic Control for TestOps at ABI

Luis Costa, Vivek F. Farias, Patricio Foncea, Jingyuan (Donna) Gan, Ayush Garg, Ivo Rosa Montenegro, Kumarjit Pathak, Tianyi Peng, and Dusan Popovic, Generalized Synthetic Control for TestOps at ABI: Models, Algorithms, and Infrastructure, To appear in INFORMS Journal on Applied Analytics (Winner, Daniel H. Wagner Prize 2022)

We describe a novel optimization-based approach– Generalized Synthetic Control (GSC)– to learning from experiments conducted in the world of physical retail. GSC solves a long-standing problem of learning from physical retail experiments when treatment effects are small, the environment is highly noisy and non-stationary, and interference and adherence problems are commonplace. The use of GSC has been shown to yield an approximately 100x increase in power relative to typical inferential methods and forms the basis of a new large-scale testing platform: ‘TestOps’. TestOps was developed and has been broadly implemented as part of a collaboration between Anheuser Busch Inbev (ABI) and an MIT team of operations researchers and data engineers. TestOps currently runs physical experiments impacting approximately 135M USD in revenue every month and routinely identifies innovations that result in a 1-2% increase in sales volume. The vast majority of these innovations would have remained unidentified absent our novel approach to inference: prior to our implementation, statistically significant conclusions could be drawn on only ∼ 6% of all experiments; a fraction that has now increased by over an order of magnitude.