1. The State of Artificial intelligence: Jack Clark and Azeem Azhar in Conversation. Discussion on the state of artificial intelligence development, the geopolitics of technology, and the implications of automation on the society.
  2. The Biggest Lesson I Learned in 2018 — Stop Resulting by Ben Brostoff. “The risk of tying good and bad outcomes to skill through a narrative is rejecting useful strategies and embracing harmful ones.”
  3. A Field Guide for Teaching Evolution in Social Sciences by Legare et al. This should be mandatory reading material.
  4. Ways to Think about Machine Learning by Ben Evans. “I don’t think, though, that we yet have a settled sense of quite what machine learning means – what it will mean for tech companies or for companies in the broader economy, how to think structurally about what new things it could enable, or what machine learning means for all the rest of us, and what important problems it might actually be able to solve.” 
  5. David “DHH” Heinemeier Hansson: The Entrepreneurial and Unstoppable Stoic for The Daily Stoic. A while back I discovered DHH and was amazed by his mindset. So it comes as little surprise that he follows Stoicism.
  6. Data Science and the Art of Persuasion by Scott Berinato for Harvard Business Review. A core issue in getting full value from data science is expecting the data scientists to be unicorns: “They [companies] still expect data scientists to wrangle data, analyze it in the context of knowing the business and its strategy, make charts, and present them to a lay audience. That’s unreasonable.” 
  7. A New Approach to Understanding How Machines Think by Rachel Bujalski for Quanta Magazine. Been Kim on new ways to improve interpretability of machine learning results.
  8. Gradually, Then Suddenly by Tim O’Reilly. A list of things that are expected to have their “gradually, then suddenly” transition.
  9. Is Life A Recursive Video Game? by Alex Zhavoronkov for Forbes. on the history of video games and their potential role in the future. (Interesting especially in combination with Yuval Noah Harari’s Guardian Article)
  10. How Alan Turing Deciphered Shark Skin by Jonathan Lambert for Nautilus. “Turing’s model, called a reaction-diffusion mechanism, is beautifully simple. It requires only two interacting agents, an activator and an inhibitor, that diffuse through tissue like ink dropped in water.”