How IBM Watson Overpromised and Underdelivered on AI Health Care

Eliza Strickland, How IBM Watson Overpromised and Underdelivered on AI Health Care, IEEE Spectrum, 2 Apr 2019. 

In many attempted applications, Watson’s NLP struggled to make sense of medical text—as have many other AI systems. “We’re doing incredibly better with NLP than we were five years ago, yet we’re still incredibly worse than humans,” says Yoshua Bengio, a professor of computer science at the University of Montreal and a leading AI researcher. In medical text documents, Bengio says, AI systems can’t understand ambiguity and don’t pick up on subtle clues that a human doctor would notice. Bengio says current NLP technology can help the health care system: “It doesn’t have to have full understanding to do something incredibly useful,” he says. But no AI built so far can match a human doctor’s comprehension and insight. “No, we’re not there,” he says....

Reinforcement Learning and Optimal Control by Bertsekas

Dimitri P. Bertsekas,  Reinforcement Learning and Optimal Control, MIT, 2019.

The purpose of the book is to consider large and challenging multistage decision problems, which can be solved in principle by dynamic programming and optimal control, but their exact solution is computationally intractable. We discuss solution methods that rely on approximations to produce suboptimal policies with adequate performance. These methods are collectively referred to as reinforcement learning, and also by alternative names such as approximate dynamic programming, and neuro-dynamic programming.

Two Sigma 的避險基金

紀茗仁、譚偉晟、黃亞琪，光速撈上萬資料 避險基金靠它找標的，今周刊，2019-01-23

一五年十月，《富比世》雜誌曾經報導這家避險基金利用AI的海搜資料本事。當時，公司用於投資決策分析的海搜資料來源就已多達一萬個，動用七萬五千顆CPU（中央處理器）；蒐集面向概略可分為四大層面，基本面、技術面之外，還有像是併購訊息等「特殊事件」類型的資訊；最特別的，則是被稱為「第一手資料」的消息。 

何謂「第一手資料」？就是各種看似與股價沒有直接關聯的消息。舉例來說，Two Sigma的AI系統會從推特等社群媒體的貼文，抓取關於某家零售商的相關抱怨，分析消費者的「怨氣」是否可能影響股價。

當然，還要搭配其他三種面向的分析，例如，即使消費者的怨氣不小，但若發現該零售商股價已從低點突破兩百日均線，且公司主管悄悄買進了更多自家股票，整體分析下來，仍可能做出買進結論。其實，Two Sigma用來分析股價的資料來源族繁不及備載，甚至包括天氣對個股的影響，都被收納在資料蒐集的範圍內。

INSIGHTS at  Two Sigma: Forecasting Factor Returns

12 年國教的 AI 課程

Welcome to the ai4k12 wiki! This interim site is being used to organize the AI for K-12 initiative jointly sponsored by AAAI and CSTA. This page will help us get started on the dialog that will eventually result in (1) national guidelines for AI education for K-12, and (2) an online, curated Resource Directory to facilitate AI instruction. To join the AI for K-12 mailing list, send mail to ai4k12@aaai.org. To read about the initiative, see these slides.

Five Big Ideas in AI (page 37 - 42), Overview of the Resource Library (pages 59 - 77).

The Use of UAVs in Humanitarian Relief (無人機在人道主義救濟中的應用)

Raissa Zurli Bittencourt Bravo, Adriana Leiras, and Fernando Luiz Cyrino Oliveira, The Use of UAVs in Humanitarian Relief: An Application of POMDP-Based Methodology for Finding Victims, Production and Operations Management,  Vol. 28, No. 2, February 2019, pp. 421–440.

Researchers have proposed the use of unmanned aerial vehicles (UAVs) in humanitarian relief to search for victims in disaster-affected areas. Once UAVs must search through the entire affected area to find victims, the path-planning operation becomes equivalent to an area coverage problem. In this study, we propose an innovative method for solving such problem based on a Partially Observable Markov Decision Process (POMDP), which considers the observations made from UAVs. The formulation of the UAV path planning is based on the idea of assigning higher priorities to the areas that are more likely to have victims. We applied the method to three illustrative cases, considering different types of disasters: a tornado in Brazil, a refugee camp in South Sudan, and a nuclear accident in Fukushima, Japan. The results demonstrated that the POMDP solution achieves full coverage of disaster-affected areas within a reasonable time span. We evaluate the traveled distance and the operation duration (which were quite stable), as well as the time required to find groups of victims by a detailed multivariate sensitivity analysis. The comparisons with a Greedy Algorithm showed that the POMDP finds victims more quickly, which is the priority in humanitarian relief, whereas the performance of the Greedy focuses on minimizing the traveled distance. We also discuss the ethical, legal, and social acceptance issues that can influence the application of the proposed methodology in practice.

The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Jonathan Frankle and Michael Carbin, The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks, ICLR 2019 (best paper).

We present an algorithm to identify winning tickets and a series of experiments that support the lottery ticket hypothesis and the importance of these fortuitous initializations. We consistently find winning tickets that are less than 10-20% of the size of several fully-connected and convolutional feed-forward architectures for MNIST and CIFAR10. Above this size, the winning tickets that we find learn faster than the original network and reach higher test accuracy.

利用人工智慧產生的多媒體資訊

James Vincent, This AI-generated Joe Rogan fake has to be heard to be believed, The Verge, May 17, 2019.

英業達的智慧製造

FLOW GLOBAL INC，［落地經驗談］陳維超：追根究底，才能找到AI的應用場景，2019 May 21

今年三月，陳維超到美國NVIDIA 開發者大會上演講，談的就是這一年多來，英業達如何運用Edge AI（終端人工智慧）做工業瑕疵檢測。智慧製造的主要應用有兩層：一，流程自動化，包括自動測檢、生產排程；二，預測性分析，如訂單預估、預防性保養。

前線國際 (Frontier) 的布料搜尋引擎

王志鈞，迎接科技紡織新紀元，自由時報，2019-05-22

Frontier能從供應端數位化布片圖樣，經由四具AI引擎分類轉為有價值的資料，大大簡化成衣品牌商及採購商的決策、採購過程，除可節省80%的布片尋找時間外，還可讓資料庫中長尾化的隱藏性布片，有效被設計師所快速撈出、選用。 

透過數位化系統，紡織業每一季的新樣衣開發流程，可以從約90~60天縮短到最快15天。因此，每個品牌每年可以推出更多季的衣服，創造更高的營業額，並且更精準地行銷生產。 

把廢柴教到全部上大學 她寫鮮師傳奇

郝廣才，把廢柴教到全部上大學 她寫鮮師傳奇，今周刊，2019-05-15 

面對學生消極的高牆，柯林斯用「積極」來打破。她總是在上課的第一天，對學生說：「你們要樹立的是信心。我相信你們能成功，能承擔生活的責任。停止抱怨社會、老師和父母，幸福快樂就在自己身上！」她培養學生的長處，如同爸爸從小那樣激勵她。 

台達電的轉型和智慧製造

黃亦筠，台達電轉型　鄭平：我不會變成天花板，不用都聽我的！，天下，2018-03-23

在台達東莞、吳江廠內，智慧自動化模範生產線從人工插件、測試、包裝、鎖螺絲到點交，全部機台都整合到一氣呵成。「現在model line （模範線）每個廠都有做，都可以達到90%人力的取代，沒有問題，接下來難的是平行展開，」鄭平接受《天下》採訪時自信地說。...

Are adversarial examples inevitable?

Ali Shafahi, W. Ronny Huang, Christoph Studer, Soheil Feizi, and Tom Goldstein, Are adversarial examples inevitable?, ICLR, 2019. (open review)

Theorem 1 (Existence of Adversarial Examples)
Theorem 2 (Adversarial examples on the cube)
Theorem 3 (Sparse adversarial examples)
Theorem 4 (Condition for existence of adversarial examples) 

14 Grand Challenges for Engineering in the 21st Century

National Academy of Engineering, 14 Grand Challenges for Engineering in the 21st Century.

Make Solar Energy Economical
Provide Energy from Fusion
Develop Carbon Sequestration Methods
Manage the Nitrogen Cycle
Provide Access to Clean Water
Restore and Improve Urban Infrastructure
Advance Health Informatics
Engineer Better Medicines
Reverse-Engineer the Brain
Prevent Nuclear Terror
Secure Cyberspace
Enhance Virtual Reality
Advance Personalized Learning
Engineer the Tools of Scientific Discovery

PingWest，事關人類存亡的 14 大工程難題，要靠 AI 來搞定了，TechNews，2019 年 05 月 13 日

Leveraging Comparables for New Product Sales Forecasting

Lennart Baardman, Igor Levin, Georgia Perakis, and Divya Singhvi, Leveraging Comparables for New Product Sales Forecasting, Production and Operations Management, Volume 27, Issue 12, 06 December 2018, Pages: 2340-2343. (First Prize of the 3rd POMS Applied Research Challenge)

This work develops an accurate, scalable and interpretable forecasting tool calibrated with our industry partners’ data. These characteristics are important to our two major industry partners, one being Johnson & Johnson Consumer Companies Inc., a consumer healthcare manufacturer, the other being a large fashion retailer. In building our tool we are motivated by an approach that has been used by industry practitioners: identify a set of products comparable to the new product, average their historical sales, and use this as a forecast. In line with this approach, we devise a model that uses analytics to jointly cluster products while estimating a regularized regression model for each cluster’s sales.... 

The joint cluster-while-regress model is formulated as a non-linear integer optimization problem that is proven to be NP-hard. However, we use the practical interpretation of our problem to devise a fast algorithm whose iterative steps mimic industry practice.... 

Working in collaboration with two large industry partners, we show that our algorithm results in a 20–70% MAPE improvement and 10–60% WMAPE improvement over several benchmarks used in practice.

高中競相找大學教授模擬面試真的有用嗎？

顏聖紘，高中競相找大學教授模擬面試真的有用嗎？，03 Apr, 2018

企業實驗 (Business Experiments)

和一位任職於銀行大數據部門主管聊天，談到企業如何實施行銷的實驗，我建議幾篇論文可以參考

Eric Almquist and Gordon Wyner, Boost Your Marketing ROI with Experimental Design, Harvard Business Review, Oct 01, 2001.

Thomas H. Davenport, How to Design Smart Business Experiments,  Harvard Business Review, Feb 01, 2009.

Eric T. Anderson and Duncan Simester, A Step-By-Step Guide to Smart Business Experiments,  Harvard Business Review, Mar 01, 2011.

杏一醫療的數據應用

王姿琳，150 萬會員資料亂糟糟　杏一如何翻轉它？，商業周刊，2019.04.17

精準預測市場需求
別人下架它備貨，避缺貨潮 

杏一醫療是國內少數可以做到預測市場需求，提高進貨、銷貨與存貨管理效率的業者。舉例來說，今年春天，多數廠商依循過往經驗，在2月就準備將暖暖包下架，避免天氣變暖，導致商品滯銷。但杏一靠著將會員數據、氣象數據與新聞時事結合，預測暖暖包在2月底、3月初將會有需求，提前備貨，成功避免缺貨影響銷售。...