Covariant Uses Simple Robot and Gigantic Neural Net to Automate Warehouse Picking

Evan Ackerman, Covariant Uses Simple Robot and Gigantic Neural Net to Automate Warehouse, IEEE Spectrum, 29 Jan 2020.

There’s already a huge amount of automation in logistics, but as Abbeel explains, in warehouses there are two separate categories that need automation: “The things that people do with their legs and the things that people do with their hands.” The leg automation has largely been taken care of over the last five or 10 years through a mixture of conveyor systems, mobile retrieval systems, Kiva-like mobile shelving, and other mobile robots. “The pressure now is on the hand part,” Abbeel says. “It’s about how to be more efficient with things that are done in warehouses with human hands.”

極端政治的誕生

陳重亨譯，極端政治的誕生：政客如何透過選舉操縱左右派世界觀的嚴重對立，有方文化，2019
Marc Hetherington and Jonathan Weiler, Prius or Pickup?: How the Answers to Four Simple Questions Explain America’s Great Divide, Houghton Mifflin Harcourt, 2018.

《極端政治的誕生》
為政治兩極對立的現況，提供真正關鍵而精準的解析 

你的車庫停著什麼車？你喝的是哪裡買的咖啡？
你是貓星人還是狗星人？你更喜歡住在城市還是鄉下？ 

喜歡宣稱「政治歸政治，____歸____」？
很遺憾的，你的投票傾向，無一不在你的生活選擇之中被揭露。
我們在日常生活中的選擇，從教養觀、上班地點、飲食品酒，
聽的音樂、看的電影、喜歡的運動，都透露著我們的政治偏好。
正因如此，政治立場與己相左的人似乎更是方方面面不能入眼，
從他的投的票到衣著美學都令人嫌惡。

學習數學的四個層次：(0) 如何學數學

學習數學的四個層次：(0) 如何學數學，(1) 代表具備基礎的知識與能力，(2) 邏輯推理和抽象思考的能力，(3) 在許多行業的應用，(4) 純粹滿足好奇心或求知慾。

YouTube 影片

2015/12/1 初稿，持續更新中。

數學研究數 (number) 與形 (shape)，被稱為科學之母。

我們從小學數學，到了大學還是要學數學。許多學生不知道為什麼要學數學，甚至深受其苦。接下來的四篇文章期待能夠提供一點點的幫忙，也希望能讓國高中生知道，數學和大學科系的連結和生活中的應用。歡迎指教。

Online Vehicle Routing: The Edge of Optimization in Large-Scale Applications

Dimitris Bertsimas, Patrick Jaillet, and Sébastien Martin, Online Vehicle Routing: The Edge of Optimization in Large-Scale Applications, Operations Research, Vol. 67, No. 1, 2019, Pages:143–162.

With the emergence of ride-sharing companies that offer transportation on demand at a large scale and the increasing availability of corresponding demand data sets, new challenges arise to develop routing optimization algorithms that can solve massive problems in real time. In this paper, we develop an optimization framework, coupled with a novel and generalizable backbone algorithm, that allows us to dispatch in real time thousands of taxis serving more than 25,000 customers per hour. We provide evidence from historical simulations using New York City routing network and yellow cab data to show that our algorithms improve upon the performance of existing heuristics in such real-world settings.

Travel Time Estimation in the Age of Big Data

Dimitris Bertsimas, Arthur Delarue, Patrick Jaillet, and Sébastien Martin, Travel Time Estimation in the Age of Big Data, Operations Research, Vol. 67, No. 2, 2019.

Twenty-first century urban planners have identified the understanding of complex city traffic patterns as a major priority, leading to a sharp increase in the amount and the diversity of traffic data being collected. For instance, taxi companies in an increasing number of major cities have started recording metadata for every individual car ride, such as its origin, destination, and travel time. In this paper, we show that we can leverage network optimization insights to extract accurate travel time estimations from such origin–destination data, using information from a large number of taxi trips to reconstruct the traffic patterns in an entire city. We develop a method that tractably exploits origin–destination data, which, because of its optimization framework, could also take advantage of other sources of traffic information. Using synthetic data, we establish the robustness of our algorithm to high variance data, and the interpretability of its results. We then use hundreds of thousands of taxi travel time observations in Manhattan to show that our algorithm can provide insights about urban traffic patterns on different scales and accurate travel time estimations throughout the network.