Four short links: 18 August 2017
Neural Style Transfer, Hype Cycles, Automation and Jobs, and Become a Bayesian
- Neural Style Transfer — overview of the state of the art in recasting an image to have the style of another.
- Eight Lessons from 20 Years of Hype Cycles — Out of the more than 200 unique technologies that have ever appeared on a Gartner Hype Cycle for Emerging Technology, just a handful of technologies—Cloud Computing, 3D Printing, Natural Language Search, Electronic Ink—have been identified early and traveled even somewhat predictably through a Hype Cycle from start to finish. […] [J]ust over 50 individual technologies appear for just a single year on the Hype Cycle—never to reappear again. […] 20% of all technologies that were tracked for multiple years on the Hype Cycle became obsolete before reaching any kind of mainstream success. […] I was often struck by how many times the Hype Cycle had an insight that was essentially correct, but the technology or the market just wasn’t ready yet. […] There are a number of core technologies that appear again and again in different guises over the years in Hype Cycles, sometimes under multiple aliases. Each reincarnation makes progress and leaves lessons for its successors without really breaking through. […] It’s remarkable the number of major technologies from the last 20 years that were either identified late or simply never appeared on a Hype Cycle.
- Robopocalypse Not (James Surowiecki) — A rigorous study of the impact of robots in manufacturing, agriculture, and utilities across 17 countries, for instance, found that robots did reduce the hours of lower-skilled workers—but they didn’t decrease the total hours worked by humans, and they actually boosted wages. In other words, automation may affect the kind of work humans do, but at the moment, it’s hard to see that it’s leading to a world without work. McAfee, in fact, says of his earlier public statements, “If I had to do it over again, I would put more emphasis on the way technology leads to structural changes in the economy, and less on jobs, jobs, jobs. The central phenomenon is not net job loss. It’s the shift in the kinds of jobs that are available.”
- Become a Bayesian in Eight Easy Steps: An Annotated Reading List — The resources are presented in an incremental order, starting with theoretical foundations and moving on to applied issues. […] Our goal is to offer researchers a starting point for understanding the core tenets of Bayesian analysis, while requiring a low level of time commitment. After consulting our guide and the outlined articles, the reader should understand how and why Bayesian methods work, and feel able to evaluate their use in the behavioral and social sciences.