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Data Science Weekly Newsletter
May 30, 2019

Editor's Picks

  • AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence
    Perhaps the most ambitious scientific quest in human history is the creation of general artificial intelligence, which roughly means AI that is as smart or smarter than humans. The dominant approach in the machine learning community is to attempt to discover each of the pieces required for intelligence, with the implicit assumption that some future group will complete the Herculean task of figuring out how to combine all of those pieces into a complex thinking machine. I call this the ``manual AI approach.'' This paper describes another exciting path that ultimately may be more successful at producing general AI. It is based on the clear trend in machine learning that hand-designed solutions eventually are replaced by more effective, learned solutions. The idea is to create an AI-generating algorithm (AI-GA)...
  • GANs And Deepfakes Could Revolutionize The Fashion Industry
    AI will transform online commerce for retailers in an even more major way in the near future — realistic digital models may eventually replace humans. The technology that could make this happen is called GANs, short for generative adversarial networks...
  • Building your own Deep Learning dream machine
    I’ve been geeking out on Deep Learning lately, taking Andrew Ng’s awesome Deep Learning specialization on Coursera and my friend Lukas Biewald’s awesome ML class. I wanted to build my own Deep Learning desktop so I can train models much faster than on my Mac laptop (or even than on an AWS Deep Learning AMI). With Lukas’ help & tutelage, we made it happen. In case you’re interested in doing the same, here’s the box we built...

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Data Science Articles & Videos

  • Training language GANs from Scratch
    We show it is in fact possible to train a language GAN from scratch -- without maximum likelihood pre-training. We combine existing techniques such as large batch sizes, dense rewards and discriminator regularization to stabilize and improve language GANs...
  • When algorithms mess up, the nearest human gets the blame
    Who or what gets blamed when you're harmed by AI? Well, it could be you. Humans act like a “liability sponge,” says Data Society, absorbing legal & moral responsibility in algorithmic accidents no matter how little they are involved...
  • Multi-Sample Dropout for Accelerated Training and Better Generalization
    Dropout is a simple but efficient regularization technique for achieving better generalization of deep neural networks (DNNs); hence it is widely used in tasks based on DNNs. During training, dropout randomly discards a portion of the neurons to avoid overfitting. This paper presents an enhanced dropout technique, which we call multi-sample dropout, for both accelerating training and improving generalization over the original dropout...
  • No cloud required: Why AI’s future is at the edge
    For all the promise and peril of artificial intelligence, there’s one big obstacle to its seemingly relentless march: The algorithms for running AI applications have been so big and complex that they’ve required processing on powerful machines in the cloud and data centers, making a wide swath of applications less useful on smartphones and other “edge” devices. Now, that concern is quickly melting away, thanks to a series of breakthroughs in recent months in software, hardware and energy technologies that are rapidly coming to market...
  • Game of Thrones Twitter Sentiment with Google Cloud Platform and Keras
    The final season of Game of Thrones apparently raised a lot of eyebrows, so I wanted to dig deeper on how people felt before, during and after the final episode of Game of Thrones by turning towards the ever non-soft-spoken Twitter community. In this blogpost, we’ll look at how an end-to-end solution can be built to tackle this problem, using the technology stack available on Google Cloud Platform...
  • Perceptual Straightening of Natural Videos
    Video is an interesting domain for unsupervised, or self-supervised, representation learning. But we still don't know what type of inductive biases will enable us to best exploit the information encoded in the temporal sequence of video frames...


  • Data Enginner / Data Scientist - Validate Health - Chicago

    Interested in being part of a small founding team, so you can see your direct impact on improving the healthcare industry? Want to be one of the rockstars building an innovative product from the ground up?​
    Validate Health is an early stage healthcare analytics company on a mission to improve accessibility to healthcare by enabling medical organizations to operate at stable and sustainable financial models.
    This position is a versatile combination of Data Engineer and Data Scientist roles. You’ll get to play a key role in shaping the delivery of powerful data-driven products that enable sustainable value-based healthcare models...

Training & Resources

  • Create TensorFlow Name Scopes For TensorBoard
    Learn how to use TensorFlow Name Scopes (tf.name_scope) to group graph nodes in the TensorBoard web service so that your graph visualization is legible, via a screencast video and full tutorial transcript...
  • Cold Case: The Lost MNIST Digits
    For all the people dealing/have dealt with MNIST, here's an extra never seen before 50,000 digits that you can test your models on! Check out...


  • Guesstimation: Solving the World's Problems on the Back of a Cocktail Napkin
    "Guesstimation enables anyone with basic math and science skills to estimate virtually anything--quickly--using plausible assumptions and elementary arithmetic"...
    For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page

    P.S., Want to reach our audience / fellow readers? Consider sponsoring - grab a spot now; first come first served! All the best, Hannah & Sebastian

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