Web Picks (week of 19 September 2016)

Every two weeks, we find the most interesting data science links from around the web and collect them in Data Science Briefings, the DataMiningApps newsletter. Subscribe now for free if you want to be the first to get up to speed on interesting resources.

  • WaveNet: A Generative Model for Raw Audio
    All the rave this week: another breakthrough from Deepmind: “This post presents WaveNet, a deep generative model of raw audio waveforms. We show that WaveNets are able to generate speech which mimics any human voice and which sounds more natural than the best existing Text-to-Speech systems, reducing the gap with human performance by over 50%.”
  • AI Can Recognize Your Face Even If You’re Pixelated
    Researchers at the University of Texas at Austin and Cornell Tech say that they’ve trained a piece of software that can undermine the privacy benefits of standard content-masking techniques like blurring and pixelation by learning to read or see what’s meant to be hidden in images.
  • The reward engineering problem in reinforcement learning
    Today we usually train reinforcement learning agents to perform narrow tasks with simple goals. We may eventually want to train RL agents to behave “well” in open-ended environments where there is no simple goal. In order to evaluate these agents, the question is: how do we carry out the evaluation, so that the optimal strategy for A is to also make “good” decisions?
  • Need Some AI? Yeah, There’s a Marketplace for That
    Diego Oppenheimer is worried that the Googles and the Facebooks will dominate the world of artificial intelligence. Oppenheimer and his startup, Algorithmia, are doing their part in the battle against AI hegemony. Algorithmia is what Oppenheimer calls an open marketplace for algorithms—code that companies and developers can use to beef up their websites and apps.
  • When your boss is an algorithm
    In the gig economy, companies such as Uber and Deliveroo manage workers via their phones. But is this liberating or exploitative?
  • War-Algorithm Accountability (paper)
    “In this briefing report, we introduce a new concept — war algorithms — that elevates algorithmically-derived “choices” and “decisions” to a, and perhaps the, central concern regarding technical autonomy in war. We thereby aim to shed light on and recast the discussion regarding “autonomous weapon systems.””
  • R for Data Science
    The draft of the new book by R legends Garrett Grolemund and Hadley Wickham is finished and can be read online. Great resource for how modern R in data science should be done!
  • How to train your #NeuralNetwork for Wine tasting?
    When it comes to classification of wine, the practice is quite varied based on region of origin and time. It is one of the most tasteful traditions which is also protected by law of its own in certain regions. Is it possible to teach something about the classification of different variety of wines to Neural Networks?
  • The Neural Network Zoo
    With new neural network architectures popping up every now and then, it’s hard to keep track of them all. Knowing all the abbreviations being thrown around (DCIGN, BiLSTM, DCGAN, anyone?) can be a bit overwhelming at first. This site presents a cheat sheet containing many of those architectures. Fantastic resource!
  • Beware of the gaps in Big Data
    As we entrust ever more of our lives to ‘big data’, how can we protect against the gaps and mistaken assumptions used to handle the information?
  • Visdown
    Make visualisations using only markdown using a simple declarative markup like you would write code. Interesting idea.