Jurnal Singkat

http://www.pnas.org/content/113/52/14873.full.pdf

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http://www.darpa.mil/news-events/2016-06-17 outreach@darpa.mil 6/17/2016 Popular search engines are great at finding answers for point-of-fact questions like the elevation of Mount Everest or current movies running at local theaters. They are not, however, very good at answering what-if or predictive questions—questions that depend on multiple variables, such as “What influences the stock market?” or “What are the major drivers of environmental stability?” In many cases that shortcoming is not for lack of relevant data. Rather, what’s missing are empirical models of complex processes that influence the behavior and impact of those data elements. In a world in which scientists, policymakers and others are awash in data, the inability to construct reliable models that can deliver insights from that raw information has become an acute limitation for planners. To free researchers from the tedium and limits of having to design their own empirical mod

http://www.bbc.com/capital/story/20170105-open-offices-are-damaging-our-memories By Bryan Borzykowski 11 January 2017 Four years ago, Chris Nagele did what many other technology executives have done before — he moved his team into an open concept office. His staff had been exclusively working from home, but he wanted everyone to be together, to bond and collaborate more easily. It quickly became clear, though, that Nagele had made a huge mistake. Everyone was distracted, productivity suffered and the nine employees were unhappy, not to mention Nagele himself. Whether it's noisy personal phone calls or constant interruptions, most of us have been victims of the open office. Share your stories with us on Facebook. In April 2015, about three years after moving into the open office, Nagele moved the company into a 10,000-square foot office where everyone now has their own space —

http://www.computervisionblog.com/2016/12/nuts-and-bolts-of-building-deep.html Nuts and Bolts of Building Deep Learning Applications: Ng @ NIPS2016 You might go to a cutting-edge machine learning research conference like NIPS hoping to find some mathematical insight that will help you take your deep learning system's performance to the next level. Unfortunately, as Andrew Ng reiterated to a live crowd of 1,000+ attendees this past Monday, there is no secret AI equation that will let you escape your machine learning woes. All you need is some rigor , and much of what Ng covered is his remarkable NIPS 2016 presentation titled " The Nuts and Bolts of Building Applications using Deep Learning " is not rocket science. Today we'll dissect the lecture and Ng's key takeaways. Let's begin. Figure 1. Andrew Ng delivers a powerful message at NIPS 2016. Andrew Ng and the Lecture Andrew Ng's lecture at NIPS 2016 in Barcelona was phenomenal --

https://kevinzakka.github.io/2016/09/26/applying-deep-learning/ Nuts and Bolts of Applying Deep Learning Sep 26, 2016 This weekend was very hectic (catching up on courses and studying for a statistics quiz), but I managed to squeeze in some time to watch the Bay Area Deep Learning School livestream on YouTube. For those of you wondering what that is, BADLS is a 2-day conference hosted at Stanford University, and consisting of back-to-back presentations on a variety of topics ranging from NLP, Computer Vision, Unsupervised Learning and Reinforcement Learning. Additionally, top DL software libraries were presented such as Torch, Theano and Tensorflow. There were some super interesting talks from leading experts in the field: Hugo Larochelle from Twitter, Andrej Karpathy from OpenAI, Yoshua Bengio from the Université de Montreal, and Andrew Ng from Baidu to name a few. Of the plethora of presentations, there was one somewhat non-technical one given by A