Four short links: 9 October 2018

Lost Lessons, Metaphors to Monads, Future of Work, and Awesome Starts at The Top

By Nat Torkington
October 9, 2018
  1. Neither Paper Nor Digital Does Reading WellDevelop a familiarity with, for example, Alan Kay’s or Douglas Engelbart’s visions for the future of computing and you are guaranteed to become thoroughly dissatisfied with the limitations of every modern OS. Reading up hypertext theory and research, especially on hypertext as a medium, is a recipe for becoming annoyed at The Web. Catching up on usability research throughout the years makes you want to smash your laptop agains the wall in anger. And trying to fill out forms online makes you scream “it doesn’t have to be this way!” at the top of your lungs. That software development doesn’t deal with research or attempts to get at hard facts is endemic to the industry. (via Daniel Siegel)
  2. The Unreasonable Effectiveness of Metaphor (YouTube) — Julia Moronuki, author of Haskell from First Principles, sneaks up on the idea of monads by starting with how linguists and cognitive scientists understand metaphors. (via @somegoob)
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  4. World Development Report 2019: The Changing Nature of Work In countries with the lowest human capital investments today, our analysis suggests that the workforce of the future will only be one-third to one-half as productive as it could be if people enjoyed full health and received a high-quality education.
  5. Chairman of Nokia Learned Deep LearningI realized that as a long-time CEO and chairman, I had fallen into the trap of being defined by my role: I had grown accustomed to having things explained to me. Instead of trying to figure out the nuts and bolts of a seemingly complicated technology, I had gotten used to someone else doing the heavy lifting. […] After a quick internet search, I found Andrew Ng’s courses on Coursera, an online learning platform. Andrew turned out to be a great teacher who genuinely wants people to learn. I had a lot of fun getting reacquainted with programming after a break of nearly 20 years. Once I completed the first course on machine learning, I continued with two specialized follow-up courses on deep learning and another course focusing on convolutional neural networks, which are most commonly applied to analyzing visual imagery. Yow.
Post topics: Four Short Links