Data Science Weekly Newsletter - Issue 156

Issue #156

Nov 17 2016

Editor Picks
 
  • Maths Zeroes In On Perfect Cup Of Coffee
    Mathematicians are a step closer to understanding what makes a perfect cup of coffee. Through some complex calculations, they have shone a light on the processes governing how coffee is extracted from grains in a filter machine...
  • Media in the Age of Algorithms
    Since Tuesday’s election, there’s been a lot of finger pointing, and many of those fingers are pointing at Facebook, arguing that their newsfeed algorithms played a major role in spreading misinformation and magnifying polarization. Some of the articles are thoughtful in their criticism, others thoughtful in their defense of Facebook, while others are full of the very misinformation and polarization that they hope will get them to the top of everyone’s newsfeed. But all of them seem to me to make a fundamental error in how they are thinking about media in the age of algorithms...
 
 

A Message from this week's Sponsor:

 

 
 

Data Science Articles & Videos

 
  • Data Scientists Need More Automation
    Many data scientists aren't lazy enough...Whether we are managing production services or running computations on AWS machines, many data scientists are working on computers besides their laptops... And as we all know, a simple solution that works can be preferable to a fragile solution that requires constant maintenance. That said, I suspect many of us aren't lazy enough. We don't spend enough time automating tasks and processes. Even when we don't save time by doing it, we may save mental overhead...
  • Google Arts & Culture Experiments
    Try out experiments at the crossroads of art and technology, created by artists and creative coders with Google Arts & Culture...
  • Designing with Machine Learning
    WeWork Soho, London. A standard 6-person meeting room (C) is adjacent to the brainstorm room covered with whiteboards (D). A variety of meeting spaces is an essential part of the WeWork experience, but finding the right combination can be challenging. The research team is currently developing ways to ensure our spaces have the right mix of meeting spaces in our locations...
  • Bias in ML, and Teaching AI
    Yesterday I gave a super duper high level 12 minutes presentation about some issues of bias in AI. I should emphasize (if it's not clear) that this is something I am not an expert in; most of what I know is by reading great papers by other people (there is a completely non-academic sample at the end of this post). This blog post is a variant of that presentation...
  • Moving machine learning from practice to production
    With growing interest in neural networks and deep learning, individuals and companies are claiming ever-increasing adoption rates of artificial intelligence into their daily workflows and product offerings...That said, I feel that this field suffers from a gulf between appreciating these developments and subsequently deploying them to solve "real-world" tasks...A number of frameworks, tutorials and guides have popped up to democratize machine learning, but the steps that they prescribe often don't align with the fuzzier problems that need to be solved...This post is a collection of questions (with some (maybe even incorrect) answers) that are worth thinking about when applying machine learning in production...
  • Machine-Learning Algorithm Can Show Whether State Secrets Are Properly Classified
    The U.S. State Department generates some two billion e-mails every year. A significant fraction of these contain sensitive or secret information and so have to be classified, a process that is time-consuming and costly. In 2015 alone, it spent $16 billion to protect classified information. AI might be able to determine why information gets either classified or declassified in error...
  • A tensorflow implementation of French-to-English machine translation using DeepMind's ByteNet
    A tensorflow implementation of French-to-English machine translation using DeepMind's ByteNet from the paper Nal et al's Neural Machine Translation in Linear Time. This paper proposed the fancy method which replaced the traditional RNNs with conv1d dilated and causal conv1d, and they achieved fast training and state-of-the-art performance on character-level translation...
 
 

Jobs

 
  • Machine Learning Engineer - HyperScience - NYC

    Our mission is to help our clients run their businesses more efficiently and effectively by introducing our artificial intelligence solutions into their tech stacks. As a serious expert or practitioner in machine learning, working at HyperScience you’ll have the opportunity to solve a diverse set of problems that to date have simply been unsolvable by humans. Moreover, you’ll work on building artificial intelligences capable of identifying and exploiting information no engineer would think of seeking out...
 
 

Training & Resources

 
  • An HDFS Tutorial for Data Analysts Stuck With Relational Databases
    By now, you have probably heard of the Hadoop Distributed File System (HDFS), especially if you are data analyst or someone who is responsible for moving data from one system to another. One of the questions many people ask when first learning about HDFS is: How do I get my existing data into the HDFS? In this article, we will examine how to import data from a PostgreSQL database into HDFS...
 
 

Books

 

 
 
P.S. Interested in reaching fellow readers of this newsletter? Consider sponsoring! Email us for details :) - All the best, Hannah & Sebastian
 
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