Weekly Round-Up: Machine Learning, DIY Data Scientists, Games, and Helping Couples Conceive

Welcome back to the round-up, an overview of the most interesting data science, statistics, and analytics articles of the past week. This week, we have 4 fascinating articles ranging in topics from machine learning to helping couple's conceive. In this week's round-up:

  • Jeff Hawkins: Where Open Source and Machine Learning Meet Big Data
  • The Rise Of The DIY Data Scientist
  • Why Games Matter to Artificial Intelligence
  • Three Questions for Max Levchin About His New Startup

Jeff Hawkins: Where Open Source and Machine Learning meet Big Data

Our first piece this week is an InfoWorld article about Jeff Hawkins, the machine learning work that him and his company have been doing, and the open source project they've recently released on Github. The project's name is the Numenta Platform for Intelligent Computing (NuPIC) and it's goal is to allow others to be able to embed machine intelligence into their own systems. The article has a short interview with Jeff and a link to the Github page where the project resides.

The Rise Of The DIY Data Scientist

This is an interesting Fast Company article about how Kaggle competition winners tend to be self-taught. The author of the article interview's Kaggle's chief scientist Jeremy Howard about this phenomenon and other interesting findings derived from Kaggle's competitions about data scientists. Some of the questions inquire about where the winners are from, how they learned data science, and what machine learning algorithms they use.

Why Games Matter to Artificial Intelligence

This blog post on the IBM Research blog is an interview with Dr. Gerald Tesauro about the significance of games in the Artificial Intelligence field. Dr. Tesauro was the IBM research scientists who taught Watson how to play Jeopardy. In the interview, he explains how games tend to be an ideal training ground for machines because they tend to simplify real life. He goes on to answer questions about how that prepares the machines for transitioning to other real-world problems, what he's currently working on, what Watson is doing these days, and where else machine learning can be used.

Three Questions for Max Levchin About His New Startup

Our final piece this week is an MIT Technology Review article about PayPal co-founder Max Levchin's new startup called Glow. A lot of people are having children later in life these days and one downside of this is that many couples have trouble trying to conceive. Levchin has developed an iPhone app that uses data to help couples identify the optimal time for conception. In this brief interview, Levchin talks about what they are doing, why, and the degree of accuracy they hope to achieve.

That's it for this week. Make sure to come back next week when we’ll have some more interesting articles! If there's something we missed, feel free to let us know in the comments below.

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