徐立妍譯，創業之國以色列：教育思維X兵役制度X移民政策X創投計畫，打造建國七十年成長50倍的經濟奇蹟，木馬文化，2017
Dan Senor and Saul Singer, Start-up Nation: the story of Israel’s economic miracle, Twelve, 2011.
以色列是全世界創投最興盛的國家之一，即使鄰國猛烈轟炸、網路經濟泡沫破滅等危機，以色列的創投資金依然不受影響，且持續成長。新加坡和杜拜亦試圖複製以色列模式，為什麼不成功？到底，以色列有什麼祕訣，在這麼多不利的條件下，仍然表現亮眼？
David Donoho, 50 Years of Data Science, Journal of Computational and Graphical Statistics, Volume 26, Issue 4, 2017, Pages 745-766.
More than 50 years ago, John Tukey called for a reformation of academic statistics. In “The Future of Data Analysis,” he pointed to the existence of an as-yet unrecognized science, whose subject of interest was learning from data, or “data analysis.” Ten to 20 years ago, John Chambers, Jeff Wu, Bill Cleveland, and Leo Breiman independently once again urged academic statistics to expand its boundaries beyond the classical domain of theoretical statistics; Chambers called for more emphasis on data preparation and presentation rather than statistical modeling; and Breiman called for emphasis on prediction rather than inference. Cleveland and Wu even suggested the catchy name “data science” for this envisioned field. A recent and growing phenomenon has been the emergence of “data science” programs at major universities, including UC Berkeley, NYU, MIT, and most prominently, the University of Michigan, which in September 2015 announced a $100M “Data Science Initiative” that aims to hire 35 new faculty. Teaching in these new programs has significant overlap in curricular subject matter with traditional statistics courses; yet many academic statisticians perceive the new programs as “cultural appropriation.” This article reviews some ingredients of the current “data science moment,” including recent commentary about data science in the popular media, and about how/whether data science is really different from statistics. The now-contemplated field of data science amounts to a superset of the fields of statistics and machine learning, which adds some technology for “scaling up” to “big data.” This chosen superset is motivated by commercial rather than intellectual developments. Choosing in this way is likely to miss out on the really important intellectual event of the next 50 years. Because all of science itself will soon become data that can be mined, the imminent revolution in data science is not about mere “scaling up,” but instead the emergence of scientific studies of data analysis science-wide. In the future, we will be able to predict how a proposal to change data analysis workflows would impact the validity of data analysis across all of science, even predicting the impacts field-by-field. Drawing on work by Tukey, Cleveland, Chambers, and Breiman, I present a vision of data science based on the activities of people who are “learning from data,” and I describe an academic field dedicated to improving that activity in an evidence-based manner. This new field is a better academic enlargement of statistics and machine learning than today’s data science initiatives, while being able to accommodate the same short-term goals.
侯良儒，中小企業也能部署！川湖回收水、防空污連閃兩危機，商業周刊第1709期，2020-08-13
不接受砍價決心轉型
從代工轉做品牌，先投入無毒電鍍
處理廢水選最難的路
花二千萬建回收廠， 遇旱保住訂單
當時 ，川湖只要停工一天 ，就是一千二百萬元的損失 ；同時，假如無法出貨 ，客戶便會轉向中國的工廠下單。
成本和風險的抉擇，還發生在五年前
警覺空汙法規將趨嚴
精算長期成本後，換成天然氣鍋爐
Ian Osband, Yotam Doron, Matteo Hessel, John Aslanides, Eren Sezener, Andre Saraiva, Katrina McKinney, Tor Lattimore, Csaba Szepesvari, Satinder Singh, Benjamin Van Roy, Richard Sutton, David Silver, Hado Van Hasselt, Behaviour Suite for Reinforcement Learning, ICLR 2020. (code)
This paper introduces the Behaviour Suite for Reinforcement Learning, or bsuite for short. bsuite is a collection of carefully-designed experiments that investigate core capabilities of reinforcement learning (RL) agents with two objectives. First, to collect clear, informative and scalable problems that capture key issues in the design of general and efficient learning algorithms. Second, to study agent behaviour through their performance on these shared benchmarks. To complement this effort, we open source this http URL, which automates evaluation and analysis of any agent on bsuite. This library facilitates reproducible and accessible research on the core issues in RL, and ultimately the design of superior learning algorithms. Our code is Python, and easy to use within existing projects. We include examples with OpenAI Baselines, Dopamine as well as new reference implementations. Going forward, we hope to incorporate more excellent experiments from the research community, and commit to a periodic review of bsuite from a committee of prominent researchers.
林洧楨 ，亞洲切彎管機王揭5年前關鍵布局：我一猶豫就死了！ 41歲黑手廠挖台積電人才 和和怎麼躲過工具機衰退？，商業周刊，2020-10-15
這家成軍41年的老工具機廠，專攻金屬管材的切割、打洞、折彎的加工設備，在業界最出名的就是，從美國波音飛機、俄羅斯米格戰鬥機航太管材，到義大利法拉利跑車的排氣管加工都要找它。根據同業推估，它過去5年營收從12億元成長到去年近18億元，專攻高利潤的客製化專用機，讓它營業毛利率超過3成以上。
但其實，5年前它還面臨汽車業訂單驟減的衝擊。...
FIONA MACDONALD, 8 Scientific Papers That Were Rejected Before Going on to Win a Nobel Prize, Science Alert, 19 AUGUST 2016.
Peer-review involves having a group of independent researchers read every paper submitted to a journal to make sure that the methods and conclusions are solid. They will often suggest revisions to be made, and can reject a paper if they think more work needs to be done, or if it's not the right fit for the journal.
Following rejection, the end product is usually better than it would have been originally - or it at least, ends up in a more appropriate journal.
Joshua S. Gans and George B. Shepherd, How Are the Mighty Fallen: Rejected Classic Articles by Leading Economists, The Journal of Economic Perspectives, Vol. 8, No. 1 (Winter, 1994), pp. 165-179.
This paper presents a selection of dispatches from the publication battlefront. We begin by discussing rejections that winners of the Nobel Prize and John Bates Clark Medal have endured, and some other notable cases. We then turn to the record of John Maynard Keynes' quirky refusals, when he was the Economics Journal's editor, of several important articles and authors. Finally, we offer some thoughts about the implications of these findings.
陳怡如 ，全球最大數位紡織雲來了！當亞馬遜遇見Frontier，時尚新裝從300天縮短到7天上架！，遠見，2020-10-05
以前一件衣服從設計到打樣，需要花45天，300天後上架，最後有40％到80％的衣服賣不出去；轉為數位設計後，只要1天打樣、7天上架，甚至達到零庫存、零浪費的完美生產境界。...
事實上布片數位化並非新鮮事，只是拍照建檔時得考慮大小、顏色、光影、紋路、拍照設備等一連串問題，目前業界提供布片數位化的公司，光是每塊布片要調校機器拍攝，一塊布就得花上50分鐘，一張還要收費30美元。
為了解決棘手的數位化問題，趙均埔花了六年時間，開發四代系統才成功。Frontier的方式很簡單，紡織廠不用添購昂貴設備，只要用公司裡最常見的事務機掃描布片，三分鐘就能上傳完成。接著再透過雲端上多達12個AI引擎，自動辨識布種、克重範圍、紋路、規格、色號，省去手動建檔的功夫。
原先系統辨識一塊布料的時間需要15分鐘，現在透過AWS的雲端資源助攻，辨識時間大幅縮短為40秒。2019年趙均埔登上有著「服飾業最創新論壇」之稱的PI Apparel舞台，底下坐著UA、Nike、Target等一線品牌大咖，從此讓Frontier一炮而紅。
Pedro Domingos, A Few Useful Things to Know About Machine Learning, Communications of the ACM, 2012, Vol. 55, No. 10, Pages 78-87.
This article summarizes 12 key lessons that machine learning researchers and practitioners have learned. These include pitfalls to avoid, important issues to focus on, and answers to common questions.
Table 1 The three components of learning algorithms.
Learning = Representation + Evaluation + Optimization
This is a nice overview article which could be assigned as an entry-level course reading. For a teacher, you could cover these topics or provide enough background material in your course so that the students could explore the content by themselves.