David Cassel, Which Programming Languages Use the Least Electricity?, the New Stack, 20 May 2018.
Last year a team of six researchers in Portugal from three different universities decided to investigate this question, ultimately releasing a paper titled “Energy Efficiency Across Programming Languages.” They ran the solutions to 10 programming problems written in 27 different languages, while carefully monitoring how much electricity each one used — as well as its speed and memory usage.
Akshay Agrawal, Alnur Ali and Stephen Boyd (2021), "Minimum-Distortion Embedding", Foundations and Trends® in Machine Learning: Vol. 14: No. 3, pp 211-378. http://dx.doi.org/10.1561/2200000090.
We consider the vector embedding problem. We are given a finite set of items, with the goal of assigning a representative vector to each one, possibly under some constraints (such as the collection of vectors being standardized, i.e., have zero mean and unit covariance). We are given data indicating that some pairs of items are similar, and optionally, some other pairs are dissimilar. For pairs of similar items, we want the corresponding vectors to be near each other, and for dissimilar pairs, we want the corresponding vectors to not be near each other, measured in Euclidean distance. We formalize this by introducing distortion functions, defined for some pairs of the items. Our goal is to choose an embedding that minimizes the total distortion, subject to the constraints. We call this the minimum-distortion embedding (MDE) problem.
This monograph is accompanied by an open-source Python package, PyMDE, for approximately solving MDE problems. Users can select from a library of distortion functions and constraints or specify custom ones, making it easy to rapidly experiment with different embeddings. Because our algorithm is scalable, and because PyMDE can exploit GPUs, our software scales to data sets with millions of items and tens of millions of distortion functions. Additionally, PyMDE is competitive in runtime with specialized implementations of specific embedding methods. To demonstrate our method, we compute embeddings for several real-world data sets, including images, an academic co-author network, US county demographic data, and single-cell mRNA transcriptomes.
Will Douglas Heaven, DeepMind’s AI predicts almost exactly when and where it’s going to rain, MIT Technology Review, September 29, 2021.
In a blind comparison with existing tools, several dozen experts judged DGMR’s forecasts to be the best across a range of factors—including its predictions of the location, extent, movement, and intensity of the rain—89% of the time. The results were published in a Nature paper today.
張毓思，1700人搶48，錄取率不到10％ 台大現象級通識課，是在教什麼？，天下Web only，2021-02-26
因為要修這門由台大領導學程開設的課程可不容易，學生修課名額48人，今年申請修課與旁聽的總和已達到1737件，學生不但要通過第二階段的面試，還要參加週末整日的工作坊才能搶到修課名額。以申請修課（非旁聽）的學生來算，48個名額有501人申請，錄取率不到10％。
Thomas M. Siebel, Digital Transformation: Survive and Thrive in an Era of Mass Extinction, RosettaBooks, 2019.
From visionary Silicon Valley entrepreneur Tom Siebel comes a penetrating examination of the new technologies that are disrupting business and government--and how organizations can harness them to transform into digital enterprises.
Tava Lennon Olsen, Brian Tomlin (2020) Industry 4.0: Opportunities and Challenges for Operations Management. Manufacturing & Service Operations Management, 22(1):113-122. (pdf)
Industry 4.0 connotes a new industrial revolution centered around cyber-physical systems. It posits that the real-time connection of physical and digital systems, along with new enabling technologies, will change the way that work is done and therefore, how work should be managed. It has the potential to break, or at least change, the traditional operations trade-offs among the competitive priorities of cost, flexibility, speed, and quality. This article describes the technologies inherent in Industry 4.0 and the opportunities and challenges for research in this area. The focus is on goods-producing industries, which include both the manufacturing and agricultural sectors. Specific technologies discussed include additive manufacturing, the internet of things, blockchain, advanced robotics, and artificial intelligence.