1. The Lines of Code That Changed Everything by Clive Thompson for Future Tense / Slate. 33 code examples that changed the way we interact with the world. 
  2. Time Series Prediction – A Short Introduction for Pragmatists by Thomas Ebermann. A comparison of common time series approaches, from simple mean/median models to deep learning.
  3. All Carrots and No Sticks: A Case Study on Social Credit Scores in Xiamen and Fuzhou by Dev Lewis for The Berkmann Klein Center. Updated on two social credit scoring systems, Moli score and Bailu score.
  4. AI in 2019: A Year in Review by AI Now Institute. The midlife crisis of the digital revolution – pushback against AI products and their originating companies. 
  5. Hilda Geiringer – The Woman Who Reshaped Maths by Leila McNeill for The BBC. Geiringer’s contribution to mathematics and materials science lad her to become the first female lecturer at the Humboldt University of Berlin. After she fled from the Nazis, her research was halted as universities would not accept female researchers.
  6. We Have the Tools and Technology to Work Less and Live Better by Toby Phillips for Aeon. Why we could work less but don’t. 
  7. What Does it Mean for a Machine to “Understand”? By Thomas G. Dietterich. Dietterich suggests translating vague notions of “understanding” and “intelligence” into concrete, measurable capabilities for a test-driven development of AI. (Levels of understanding perfectly described by Scott Alexander)
  8. Motor Cortical Representation and Decoding of Attempted Handwriting in a Person with Tetraplegia by Willett et al. (2019), abstract at Abstractsonline. Based on the brain activity of a person paralyzed from the neck down, a brain-to-computer interface based on neural nets translated imagined handwritten letters into digital writing. 
  9. Principles for the Application of Human Intelligence by Jason Collins for The Behavioral Scientist. An absurdist piece about the risks of using humans for decision making: Biases, black boxes, and a lack of consistency – “[…] we need to consider the risks and ensure implementation of human decision-making systems does not cause widespread harm.”
  10. New Complexity Economics by David Krakauer for the Santa Fe Institute. Krakauer introducing the New Complexity Economics symposium: “[…]in the last couple of decades the exponentiation of data and computer power, progress in algorithms, statistical physics, adaptive dynamics, and in neural, behavioral and cognitive science, suggests that a new complexity revolution is on the horizon.”