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Data Science Weekly Newsletter
March 15, 2018

Editor's Picks

  • Making music using new sounds generated with machine learning
    Building upon past research in the field of machine learning and music, last year Magenta released NSynth (Neural Synthesizer). It’s a machine learning algorithm that uses deep neural networks to learn the characteristics of sounds, and then create a completely new sound based on these characteristics. Rather than combining or blending the sounds, NSynth synthesizes an entirely new sound using the acoustic qualities of the original sounds—so you could get a sound that’s part flute and part sitar all at once...
  • AI Has A Halluncination Problem That's Proving Tough To Fix
    Tech commpanies are rushing to infuse everything with artificial intelligence, driven by big leaps in the power of machine learning software. But the deep-neural-network software fueling the excitement has a troubling weakness: Making subtle changes to images, text, or audio can fool these systems into perceiving things that aren’t there...

A Message From This Week's Sponsor

Gartner’s 2018 Magic Quadrant for Data Science and Machine-Learning Platforms

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  • How Gartner defines the Data Science Platform category, and their perspective on the evolution of the data science platform market in 2018.

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Data Science Articles & Videos

  • How to Make A.I. That’s Good for People
    I worry, however, that enthusiasm for A.I. is preventing us from reckoning with its looming effects on society. Despite its name, there is nothing “artificial” about this technology — it is made by humans, intended to behave like humans and affects humans. So if we want it to play a positive role in tomorrow’s world, it must be guided by human concerns. I call this approach “human-centered A.I.” It consists of three goals that can help responsibly guide the development of intelligent machines...
  • What is AI, really?: A cultural and practical introduction for designers
    Welcome to the first chapter in our AI-First Design Foundations Series, in which we aim to nail down the language of artificial intelligence and discuss its many definitions. In doing so we hope to land on an idea of what artificial intelligence is today, from which we can build towards answering: What is AI-First Design?...
  • Achieving Human Parity on Automatic Chinese to English News Translation
    In this paper, we first address the problem of how to define and accurately measure human parity in translation. We then describe Microsoft’s machine translation system and measure the quality of its translations on the widely used WMT 2017 news translation task from Chinese to English. We find that our latest neural machine translation system has reached a new state-of-the-art, and that the translation quality is at human parity when compared to professional human translations. We also find that it significantly exceeds the quality of crowd-sourced non-professional translations...
  • Semantic Image Segmentation with DeepLab in Tensorflow
    Today, we are excited to announce the open source release of our latest and best performing semantic image segmentation model, DeepLab-v3+, implemented in Tensorflow. This release includes DeepLab-v3+ models built on top of a powerful convolutional neural network (CNN) backbone architecture...


  • Data Scientist, Growth Insights - Spotify - NYC
    We are looking for a Data Scientist to join the band and help drive a data-first culture with focus on growth. As a Data Scientist, our mission is to turn our 200 petabytes of data into insights and gain a deep understanding of music and listeners to impact the strategy and direction of Spotify. You will study user behavior, strategic initiatives, markets, content, and new features and bring data and insights into every decision we make. Above all, your work will impact how we think about user growth and how we can make Spotify available and accessible for more people in the world...

Training & Resources

  • Kipoi: Model zoo for genomics
    Kipoi is an API and a repository of ready-to-use trained models for regulatory genomics. It currently contains 1709 different models, covering canonical predictive tasks in transcriptional and post-transcriptional gene regulation. Kipoi's API is implemented as a python package and it is also accessible from the command line or R...
  • Reptile: A Scalable Meta-Learning Algorithm
    We’ve developed a simple meta-learning algorithm called Reptile which works by repeatedly sampling a task, performing stochastic gradient descent on it, and updating the initial parameters towards the final parameters learned on that task...


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