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Data Science Weekly Newsletter
November 7, 2019

Editor's Picks

  • History’s message about regulating AI
    As we consider artificial intelligence, we would be wise to remember the lessons of earlier technology revolutions—to focus on the technology’s effects rather than chase broad-based fears about the technology itself...
  • Highlights from the 2019 Google AI Residency Program
    The program’s latest installment was our most successful yet, as residents advanced progress in a broad range of research fields, such as machine perception, algorithms and optimization, language understanding, healthcare and many more. Below are a handful of innovative projects from some of this year’s alumni...

A Message From This Week's Sponsor

Introducing Helix: the first dynamic data engine for data science teams

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Data Science Articles & Videos

  • AI and Compute
    We’re releasing an analysis showing that since 2012, the amount of compute used in the largest AI training runs has been increasing exponentially with a 3.4-month doubling time (by comparison, Moore’s Law had a 2-year doubling period)...
  • Key challenges for delivering clinical impact with artificial intelligence
    Artificial intelligence (AI) research in healthcare is accelerating rapidly, with potential applications being demonstrated across various domains of medicine. However, there are currently limited examples of such techniques being successfully deployed into clinical practice. This article explores the main challenges and limitations of AI in healthcare, and considers the steps required to translate these potentially transformative technologies from research to clinical practice...
  • The Measure of Intelligence
    I've just released a fairly lengthy paper on defining & measuring intelligence, as well as a new AI evaluation dataset, the "Abstraction and Reasoning Corpus". I've been working on this for the past 2 years, on & off...
  • Fast Transformer Decoding: One Write-Head is All You Need
    Multi-head attention layers, as used in the Transformer neural sequence model, are a powerful alternative to RNNs for moving information across and between sequences. While training these layers is generally fast and simple, due to parallelizability across the length of the sequence, incremental inference (where such paralleization is impossible) is often slow, due to the memory-bandwidth cost of repeatedly loading the large "keys" and "values" tensors. We propose a variant called multi-query attention, where the keys and values are shared across all of the different attention "heads", greatly reducing the size of these tensors and hence the memory bandwidth requirements of incremental decoding...
  • How should a Data Scientist's resume differ from an Academic CV?
    Your academic cv is very coursework and research focused. You've heard business resumes need to be more action and results oriented, but you're not sure what that means for you. You're looking for advice on how to re-work your academic cv and not finding much advice out here. To help get you started, here are some thoughts on what you'll need to do...


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  • Data Scientist - Datadog - NYC

    At Datadog, we’re on a mission to build the best monitoring platform in the world. We operate at high scale—trillions of data points per day—and high availability, providing always-on alerting, visualization, and tracing for our customers' infrastructure and applications around the globe.
    Our engineering culture values pragmatism, honesty, and simplicity to solve hard problems the right way. We need you to design and build machine learning-powered products that help our customers learn from their data and make better decisions in real-time....
        Want to post a job here? Email us for details >>

Training & Resources

  • Knowledge Graphs & NLP @ EMNLP 2019 Part I
    The review post of the papers from ACL 2019 on knowledge graphs (KGs) in NLP was well-received so I thought maybe it would be beneficial for the community to look through the proceedings of EMNLP 2019 for the latest state of the art in applying knowledge graphs in NLP. Let’s start!...
  • 2019 LookML Open-Source State of the Union
    With this growth in open-source projects, and little in the way to organize and discover them, we saw a need to put together a comprehensive survey. We presented this overview at JOIN, and now bring it to you in the first (of hopefully many such) LookML Open-Source State of the Union reports...


  • The Lady Tasting Tea:
    How Statistics Revolutionized Science in the Twentieth Century
    An insightful, revealing history of how mathematics transformed our world...
    "I have taken courses in statistics, taught it many times and solved several statistical problems that have appeared in journals. But until I read this book, I never really thought about it in so deep and philosophical a manner..."...
    For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page


    P.S., Enjoy the newsletter? Please forward it to your friends and colleagues - we'd love to have them onboard :) All the best, Hannah & Sebastian

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