Web Picks (week of 28 December 2016)

Every two weeks, we find the most interesting data science links from around the web and collect them in Data Science Briefings, the DataMiningApps newsletter. Subscribe now for free if you want to be the first to get up to speed on interesting resources.

  • The great AI awakening
    How Google used artificial intelligence to transform Google Translate, one of its more popular services — and how machine learning is poised to reinvent computing itself.
  • The Most Boring/Valuable Data Science Advice
    “I’m going to make this quick. You do a carefully thought through analysis. You present it to all the movers and shakers at your company. Everyone loves it. Six months later someone asks you a question you didn’t cover so you need to reproduce your analysis…”
  • The major advancements in Deep Learning in 2016
    “In this article, we will go through the advancements we think have contributed the most (or have the potential) to move the field forward and how organizations and the community are making sure that these powerful technologies are going to be used in a way that is beneficial for all.”
  • Tool AI’s want to be Agent AI’s
    “Tool AIs limited purely to inferential tasks will be less intelligent, efficient, and economically valuable than independent reinforcement-learning AIs learning actions over computation / data / training / architecture / hyperparameters / external-resource use.”
  • Building Jarvis
    Wondering how Zuckerberg creates an AI? “My personal challenge for 2016 was to build a simple AI to run my home — like Jarvis in Iron Man.”
  • Finding MLB Anomalies with CADE
    “Over the Summer, while an intern at Elder Research, I learned about a very intuitive anomaly detection algorithm called CADE, or Classifier-Adjusted Density Estimation. The algorithm seemed very simple, so I wanted to try and implement it myself and try to find anomalous players in the MLB.”
  • A Guide to Solving Social Problems with Machine Learning
    “We have learned that some of the most important challenges fall within the cracks between the discipline that builds algorithms (computer science) and the disciplines that typically work on solving policy problems (such as economics and statistics). As a result, few of these key challenges are even on anyone’s radar screen.”
  • Hamiltonian Monte Carlo explained
    MCMC (Markov chain Monte Carlo) is a family of methods that are applied in computational physics and chemistry and also widely used in bayesian machine learning.
  • Speed up your code with multidplyr
    “There’s nothing more frustrating than waiting for long-running R scripts to iteratively run. I’ve recently come across a new-ish package for parallel processing that plays nicely with the tidyverse: multidplyr.”
  • How we learn how you learn
    “In this post, we’ll take a look at the science behind the Duolingo skill strength meter, which we published in an Association of Computational Linguistics article earlier this year….”