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Data Science Weekly Newsletter
March 14, 2019

Editor's Picks

  • Why Data Science Teams Need Generalists, Not Specialists
    Division of labor by function is so ingrained in us even today that we are quick to organize our teams accordingly. Data science is no exception... Alas, we should not be optimizing our data science teams for productivity gains; that is what you do when you know what it is you’re producing—pins or otherwise—and are merely seeking incremental efficiencies. But the goal of data science is not to execute...
  • 10 things R can do that might surprise you
    Over the last few weeks I’ve had a couple of interactions with folks from the computer science world who were pretty disparaging of the R programming language. A lot of the critism focused on perceived limitations of R to statistical analysis. So this post is about some of the ridiculously awesome features of R that may or may not be as widely known...

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Data Science Articles & Videos

  • The AI Roles Some Companies Forget to Fill
    AI talent goes far beyond machine learning Ph.D’s. Equally important and less understood are the set of talent issues emerging around AI product development and engineering. Most firms have not filled these roles, and their AI projects are suffering as a result...
  • The Bitter Lesson
    The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin. The ultimate reason for this is Moore's law, or rather its generalization of continued exponentially falling cost per unit of computation...
  • Designing agent incentives to avoid side effects
    Impact penalties help us train agents to avoid unwanted side effects, but these penalties can still produce undesired behaviour. We compare different penalty design choices and show how to avoid this...
  • The Promise of Hierarchical Reinforcement Learning
    We will start by reviewing the fundamentals of RL before elaborating on its current limitations. We will then see how HRL can be an attractive way to counter the limits of RL, including its motivations, main frameworks and own limitations. Finally, we will discuss active and future research in this area...
  • Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem
    Classifiers used in the wild, in particular for safety-critical systems, should not only have good generalization properties but also should know when they don't know, in particular make low confidence predictions far away from the training data. We show that ReLU type neural networks which yield a piecewise linear classifier function fail in this regard as they produce almost always high confidence predictions far away from the training data. For bounded domains like images we propose a new robust optimization technique similar to adversarial training which enforces low confidence predictions far away from the training data...


  • Senior Data Scientist - PepsiCo eCommerce - NYC

    PepsiCo has assembled a dedicated eCommerce team – tasked with optimizing eCommerce operations and developing innovations that will give PepsiCo a sustainable competitive advantage. While tied closely to broader PepsiCo, the eCommerce group more closely resembles a start-up environment.
    PepsiCo’s Data Science and Analytics group is a team of data scientists, technology specialists, and business innovators who operate within eCommerce to build industry-leading systems and solutions. By focusing on machine learning and automation, the Data Science & Analytics group is pushing the bounds of possibility for PepsiCo and its strategic partners...
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