I won't post every issue in this blog, but here's the inaugural edition to get you started. If you want to sign up, here's the link. No more than twice a month, I promise!
Welcome to Data Citables, an ICYMI roundup of recent articles and blogs having to do with data and data science!
- “The suit, which seeks class-action status, claims the We-Vibe vibrator app chronicles how often and how long consumers use the sex toy and sends that data to the company's Canadian servers.” Sex toys and the Internet of Things collide—what could go wrong? (David Kravets, Ars Technica)
- “And why should private enterprise benefit from public data? A rising tide lifts all ships.” How open government data creates smarter societies (Karen Eng, ideas.ted.com)
- “I call these band[s] "Metal" here for the sake of brevity only, and I apologise in advance.” Heavy Metal and Natural Language Processing - Part 2 (Iain, Degenerate State)
- “Classically they were only capable of categorising linearly separable data; say finding which images are of Garfield and which of Snoopy, with any other outcome not being possible.” The Neural Network Zoo (Fjodor Van Veen, Asimov Institute)
- “ ‘It’s not really that complicated [...] There are many things that are much more complicated than looking at the polls and taking an average... right?’ ” How Hillary's Campaign is (Almost Certainly) Using Big Data (Eric Siegel, Scientific American)
- “You don't even know what's in the box. It could be hamsters or something equally ridiculous. Before we do machine learning, we must know what our observable phenomena is.” Why isn't supervised machine learning more automated? (Alex Clemmer, Quora)
- “This data set is called Word2vec and is hugely powerful. Numerous researchers have begun to use it to better understand everything from machine translation to intelligent Web searching. But today Tolga Bolukbasi at Boston University and a few pals from Microsoft Research say there is a problem with this database: it is blatantly sexist.” How Vector Space Mathematics Reveals the Hidden Sexism in Language (MIT Technology Review)
- “This data-driven solution also hopes to prevent police violence by pinpointing an at-risk officer before an ‘adverse event’ even occurs. There have been 583 people shot and killed by police in 2016 at time of writing.” Police Are Using Machine-Learning To Flag Officers As Potential Risks (Melanie Ehrenkranz, Mic.com)
- “Machine learning is far from being a ‘genie’ that is ready to spring from a bottle and run amok.” Deep learning isn’t a dangerous magic genie. It’s just math. (Oren Etzioni, Wired)
September 22, 2016. E-mail frequency: about every 2-3 weeks.
And yes, I totally stole the format from the Verbatim section of Vox Sentences.