2020 year in review by McKinsey

McKinsey, 2020 year in review, 2020/12/28.

動態規劃 (dynamic programming)

考慮多階段決策的問題，例如下方的最短路徑問題，需要從起點 A、經過中間站的 BCDE、走到終點 F；A 到 B 的距離為 1，D 到 F 的距離為 100 等等。

現在的問題很小，可以使用窮舉法 (method of enumeration) 找出所有的可能。走 ABDF 總距離 102、ABEF 總距離 8、ACDF 總距離 106、ACEF 總距離 9，所以最短路徑為 ABEF。

如何解決一般化的問題？首先考慮短視的策略  (myopic policy)，站在 A 點時，在 B 和  C 兩條路中選擇 B (AB = 1 < 4 = AC)；站在 B 點時，選擇 D；站在 D 點時，只能選擇 F。總成本為 102，路徑為 ABDF。

You Can’t Escape Hyperparameters and Latent Variables

Charles Isbell, You Can’t Escape Hyperparameters and Latent Variables: Machine Learning as a Software Engineering Enterprise, keynote speech at NeuIPS, Dec 8th, 2020. (slide 70: What have we learned?)

Successful technological fields have a moment when they become pervasive, important, and noticed. They are deployed into the world and, inevitably, something goes wrong. A badly designed interface leads to an aircraft disaster. A buggy controller delivers a lethal dose of radiation to a cancer patient. The field must then choose to mature and take responsibility for avoiding the harms associated with what it is producing. Machine learning has reached this moment. In this talk, I will argue that the community needs to adopt systematic approaches for creating robust artifacts that contribute to larger systems that impact the real human world. I will share perspectives from multiple researchers in machine learning, theory, computer perception, and education; discuss with them approaches that might help us to develop more robust machine-learning systems; and explore scientifically interesting problems that result from moving beyond narrow machine-learning algorithms to complete machine-learning systems.

這樣的傅斯年

沈珮君，這樣的傅斯年（上），聯合報，2020-08-06

最近，重回傅鐘下，發現一塊小碑，是陳維昭校長在民國91年立的，寫了傅鐘21響意義，「一天只有二十一小時，剩下三小時是用來沉思的」。...

他在民國38年1月20日接掌台大，39年12月20日去世，還差一個月才滿兩年。他太累了，台大千頭萬緒，上自典章制度、攬才招生，下至親筆給學生、工人回信，細大不捐，曾有一個外文系校友去信給他，抱怨畢業一年卻仍無畢業證書，他親自洽問，才知教部因要查核每人是否修滿學分，一人不符，全體不發，光復以後即未發過畢業證書，在他要求下才解決。他去世前幾天，跟朱家驊說：「你把我害了，台大的事真是多，吃不消，我的命要斷送在台大了。」他死在省參議會，報告完台大校務，緩緩回到座位，臉發白，手奇冷，血壓一度高到230，五小時後即逝。

《中央日報》刊出他在省參議會的最後發言，傅斯年強調台大獎學金制度不應廢止，「對於那些資質好、肯用功的，僅只為了沒錢而不能升學的青年，我是萬分同情的，我不能讓他們被摒棄於校門之外」，這成了他的遺言。當初受惠的台大青年，應都九十幾歲了。...

Mathematics for Machine Learning

Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong, Mathematics for Machine Learning, Cambridge University Press, 2020. (pdf)

The book assumes the reader to have mathematical knowledge commonly covered in high school mathematics and physics. For example, the reader should have seen derivatives and integrals before, and geometric vectors in two or three dimensions. Starting from there, we generalize these concepts. Therefore, the target audience of the book includes undergraduate university students, evening learners and learners participating in online machine learning courses.

台灣軟體業的發展

 科技島讀，Ep.126 難產的台灣獨角獸｜特別來賓 XREX 創辦人黃耀文，2020/12/20

台灣獨角獸遲遲難產，讓人擔憂輝煌的半導體與電子製造業後繼無人。相較於以色列、芬蘭、烏克蘭、新加坡等在軟體領域各據地盤，台灣軟體業至今無法破繭而出，更啟人疑竇。島讀邀請成功創業家黃耀文，一起從產品定位、資本、人才等面向，分析台灣新創的錯過與值得。

慈善捐款

 Nicholas Kulish, Giving Billions Fast, MacKenzie Scott Upends Philanthropy, NYT, Dec. 20, 2020.

By disbursing her money quickly and without much hoopla, Ms. Scott has pushed the focus away from the giver and onto the nonprofits she is trying to help. They are the types of organizations — historically Black colleges and universities, community colleges and groups that hand out food and pay off medical debts — that often fly beneath the radar of major foundations.

“If you look at the motivations for the way women engage in philanthropy versus the ways that men engage in philanthropy, there’s much more ego involved in the man, it’s much more transactional, it’s much more status driven,” said Debra Mesch, a professor at the Women’s Philanthropy Institute at Indiana University. “Women don’t like to splash their names on buildings, in general.”

How To Speak by Patrick Winston

Patrick Winston, How To Speak, MIT OpenCourseWare, 2019/12/20.

Feedback Control Perspectives on Learning

Jeff Shamma, Feedback Control Perspectives on Learning, keynote speech at NeuIPS, Dec 8th, 2020.

The impact of feedback control is extensive. It is deployed in a wide array of engineering domains, including aerospace, robotics, automotive, communications, manufacturing, and energy applications, with super-human performance having been achieved for decades. Many settings in learning involve feedback interconnections, e.g., reinforcement learning has an agent in feedback with its environment, and multi-agent learning has agents in feedback with each other. By explicitly recognizing the presence of a feedback interconnection, one can exploit feedback control perspectives for the analysis and synthesis of such systems, as well as investigate trade-offs in fundamental limitations of achievable performance inherent in all feedback control systems. This talk highlights selected feedback control concepts—in particular robustness, passivity, tracking, and stabilization—as they relate to specific questions in evolutionary game theory, no-regret learning, and multi-agent learning.

Listening to Prof. Shamma's talk is always enjoyable and a great learning experience.  

The information on slide 3 by 2 prominent researchers in control:

K. Astrom, "Automatic Control – a Perspective," UON FEBE, 2019/9/5.

G. Stein, “Respect the unstable,” IEEE Control System Magazine, vol. 23, no. 4, pp. 12–25, Aug. 2003. (Cartoons for slides 97 - 98)

 

An AI development platform for industrial systems

Kyle Wiggers, Microsoft launches Project Bonsai, an AI development platform for industrial systems, Venture Beat, May 19, 2020.

Microsoft announced the public preview of Project Bonsai, a platform for building autonomous industrial control systems, during its Build 2020 online conference. The company also debuted an experimental platform called Project Moab that’s designed to familiarize engineers and developers with Bonsai’s functionality.

Project Bonsai is a “machine teaching” service that combines machine learning, calibration, and optimization to bring autonomy to the control systems at the heart of robotic arms, bulldozer blades, forklifts, underground drills, rescue vehicles, wind and solar farms, and more. Control systems form a core component of machinery across sectors like manufacturing, chemical processing, construction, energy, and mining, helping manage everything from electrical substations and HVAC installations to fleets of factory floor robots. But developing AI and machine learning algorithms atop them — algorithms that could tackle processes previously too challenging to automate — requires expertise....

耶魯最受歡迎的金融通識課

 陳志武，耶魯最受歡迎的金融通識課，今周刊，2019

全書文字通俗易懂，沒有公式和金融模型，卻能從歷史角度和量化分析視角來幫助讀者建立金融思維，以經濟眼光看待這個世界的運轉。是一本幫助普通大眾認清財富本源，學會用金融思維看懂世界的金融通識書。

25 頁

金融的核心任務，是要解決人與人之間的跨期價值交換的問題。…… 這些跨期價值交換涉及人與人之間的跨期承諾 (intertemporal commitment)，而跨期承諾是人類社會最難解決的挑戰。

在金融市場出現之前，人類多次的文化和社會組織創新，目的都是為了解決跨期承諾的挑戰，進而提升人與人之間跨期交換的安全度。

在書裡你會學到，傳統習俗、迷信宗教愛情婚姻家庭禮尚往來，以及儒家、基督教等文化的背後，其實含有豐富的金融邏輯。也就是說，許多文化的內涵實際上是因為金融市場的缺點而產生的，是為了解決本來應該由金融出面的問題而來的。

Beat the Slots in Pokémon Using Reinforcement Learning

 Daniel Saunders, How I Beat the Slots in Pokémon Using Reinforcement Learning, towardsdatascience, 2020/12/10. (Python code)

Given a set of possible actions (“arms” of a multi-armed bandit — in this case different machines to try), Thompson sampling optimally trades off exploration vs exploitation to find the best action, by trying the promising actions more often, and so getting a more detailed estimate of their reward probabilities. At the same time, it’s still randomly suggesting the others from time to time, in case it turns out one of them is the best after all. At each step, the knowledge of the system, in the form of posterior probability distributions, is updated using Bayesian logic. The simplest version of the one-armed bandit problem involves Bernoulli trials, where there are only two possible outcomes, reward or no reward, and we are trying to determine which action has the highest probability of reward.

This is a nice article and it should be interesting to teach this example to motivate the students. 

聰明學統計的 13 又 1/2 堂課

愛荷譯，聰明學統計的 13 又 1/2 堂課 : 每個數據背後都有戲, 搞懂才能做出正確判斷，先覺，2013

C.J. Wheelan, Naked Statistics:  Stripping the Dread from the Data, W.W. Norton, 2013. 

 《聰明學經濟的12堂課》作者查爾斯．惠倫再次以他豐富的學養、幽默的風格，以及化繁為簡的無上功力，拋開統計學枯燥生硬的理論，讓你從生活中的大小事，輕鬆搞懂最關鍵實用的統計概念，做出正確決策。他說，本書是要讓最重要的統計觀念變得更合乎直覺，也更容易上手。他說，用統計說謊很容易，沒有統計要找出真相卻很困難。你將從這本書中，得到超乎你想像的重要訊息！而且，是用一種充滿趣味的方式！

適合當課程的補充教材。 

為什麼是 13 又 1/2 堂呢？大概和美國的大學學制有關，學期制一學期 15 週，扣掉考試差不多。

246 頁，作者舉例說明高爾夫課課程對平均桿數的影響，線性迴歸分析當然沒辦法描述。我們需要的是非線性迴歸 (第七章)。

秋野芒劇團

 連翊君，為偏鄉孩子而演！許子漢的「許伯大夢」，大人社團，2018-07-19 

秋野芒劇團創辦人、花蓮東華大學華文系（原中文系）副教授許子漢今年54歲，朋友暱稱他「大伯」，他有個「許伯大夢」，要做6個故事相連的兒童劇，一年演出120場，包括花蓮等地偏鄉，讓國小學童每年看一部戲，直到畢業。...

Artificial Intelligence — The Revolution Hasn’t Happened Yet (人工智慧 -- 革命尚未發生)

Michael Jordan, Artificial Intelligence — The Revolution Hasn’t Happened Yet, Harvard Data Science Review, Apr 19, 2018.

When my spouse was pregnant 14 years ago, we had an ultrasound. There was a geneticist in the room, and she pointed out some white spots around the heart of the fetus. “Those are markers for Down syndrome,” she noted, “and your risk has now gone up to one in 20.” She let us know that we could learn whether the fetus in fact had the genetic modification underlying Down syndrome via an amniocentesis, but amniocentesis was risky—the chance of killing the fetus during the procedure was roughly one in 300. Being a statistician, I was determined to find out where these numbers were coming from. In my research, I discovered that a statistical analysis had been done a decade previously in the UK in which these white spots, which reflect calcium buildup, were indeed established as a predictor of Down syndrome. I also noticed that the imaging machine used in our test had a few hundred more pixels per square inch than the machine used in the UK study. I returned to tell the geneticist that I believed that the white spots were likely false positives, literal white noise.

She said, “Ah, that explains why we started seeing an uptick in Down syndrome diagnoses a few years ago. That’s when the new machine arrived.”

We didn’t do the amniocentesis, and my wife delivered a healthy girl a few months later, but the episode troubled me, particularly after a back-of-the-envelope calculation convinced me that many thousands of people had gotten that diagnosis that same day worldwide, that many of them had opted for amniocentesis, and that a number of babies had died needlessly. The problem that this episode revealed wasn’t about my individual medical care; it was about a medical system that measured variables and outcomes in various places and times, conducted statistical analyses, and made use of the results in other situations. The problem had to do not just with data analysis per se, but with what database researchers call provenance—broadly, where did data arise, what inferences were drawn from the data, and how relevant are those inferences to the present situation? While a trained human might be able to work all of this out on a case-by-case basis, the issue was that of designing a planetary-scale medical system that could do this without the need for such detailed human oversight....

SIAM Conference on Mathematics of Data Science (MDS20)

 SIAM Conference on Mathematics of Data Science (MDS20)  Topological Data Analysis of Complex High-Dim. Layout Configurations for IC Physical Designs