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.