A/B testing is widely used for online marketing, management of Internet ads or any other usual analytics. In general, people use it in order to look for "golden features (metrics)" that are vital points for growth hacking. To validate A/B testing, statistical hypothesis tests such as t-test are used and people are trying to find any metric with a significant effect across conditions. If you successfully find a metric with a significant difference between design A and B of a click button, you'll get happy. Such a metric can provide a rule-based predictor for KGI / KPI: for example, a landing page with a button A increases conversion rate by 2%.

In my new book BOLD, one of the interviews that I'm most excited about is with my good friend Ray Kurzweil. Bill Gates calls Ray, "the best person I know at predicting the future of artificial intelligence." Ray is also amazing at predicting a lot more beyond just AI. This post looks at his very incredible predictions for the next 20 years. He has received 20 honorary doctorates, has been awarded honors from three U.S. presidents, and has authored 7 books (5 of which have been national bestsellers). He is the principal inventor of many technologies ranging from the first CCD flatbed scanner to the first print-to-speech reading machine for the blind. He is also the chancellor and co-founder of Singularity University, and the guy tagged by Larry Page to direct artificial intelligence development at Google. In short, Ray's pretty smart… and his predictions are amazing, mind-boggling, and important reminders that we are living in the most exciting time in human history. But, first let's look back at some of the predictions Ray got right. Then in 1997, IBM's Deep Blue defeated Garry Kasparov. He was right, to say the least.

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

This is an introductory tutorial on using Theano, the Python library. I'm going to start from scratch and assume no previous knowledge of Theano. However, understanding how neural networks work will be useful when getting to the code examples towards the end. I recently gave this tutorial as a talk in University of Cambridge and it turned out to be way more popular than expected. In order to give more people access to the material, I'm now writing it up as a blog post. I do not claim to know everything about Theano, and I constantly learn new things myself.

Matrix completion under interval uncertainty can be cast as matrix completion with element-wise box constraints. We present an efficient alternating-direction parallel coordinate-descent method for the problem. We show that the method outperforms any other known method on a benchmark in image in-painting in terms of signal-to-noise ratio, and that it provides high-quality solutions for an instance of collaborative filtering with 100,198,805 recommendations within 5 minutes.

Traditional topic models do not account for semantic regularities in language. Recent distributional representations of words exhibit semantic consistency over directional metrics such as cosine similarity. However, neither categorical nor Gaussian observational distributions used in existing topic models are appropriate to leverage such correlations. In this paper, we propose to use the von Mises-Fisher distribution to model the density of words over a unit sphere. Such a representation is well-suited for directional data. We use a Hierarchical Dirichlet Process for our base topic model and propose an efficient inference algorithm based on Stochastic Variational Inference. This model enables us to naturally exploit the semantic structures of word embeddings while flexibly discovering the number of topics. Experiments demonstrate that our method outperforms competitive approaches in terms of topic coherence on two different text corpora while offering efficient inference.

Information diffusion in online social networks is affected by the underlying network topology, but it also has the power to change it. Online users are constantly creating new links when exposed to new information sources, and in turn these links are alternating the way information spreads. However, these two highly intertwined stochastic processes, information diffusion and network evolution, have been predominantly studied separately, ignoring their co-evolutionary dynamics. We propose a temporal point process model, COEVOLVE, for such joint dynamics, allowing the intensity of one process to be modulated by that of the other. This model allows us to efficiently simulate interleaved diffusion and network events, and generate traces obeying common diffusion and network patterns observed in real-world networks. Furthermore, we also develop a convex optimization framework to learn the parameters of the model from historical diffusion and network evolution traces. We experimented with both synthetic data and data gathered from Twitter, and show that our model provides a good fit to the data as well as more accurate predictions than alternatives.

Microsoft Build, the company's annual conference with software developers, gets underway Wednesday. Microsoft will be talking about its plans for the year, and encouraging developers to think up new applications for its products. Two of the most important things Microsoft is highlighting are artificial intelligence and machine learning. If you use Microsoft products, chances are you already experience machine learning, said spokesman Frank Shaw, who gave the example of Microsoft virtual assistant Cortana. "Cortana will say, currently, 'Hey, you sent email and promised a reply by tomorrow. One of the key elements of the Microsoft Build developer conference is thinking about how to incorporate that kind of intelligence throughout the Microsoft ecosystem. "Microsoft is very good, and they're getting better.

Sunrise co-host Edwina Bartholemew playing an April Fool's Day prank on Kochie. IT'S April Fool's Day today, and Australian companies have gotten into the spirit by having a lend of their customers in some very creative ways. Sunrise co-host Edwina Bartholemew had Kochie fooled when she announced she was engaged to her long time partner Neil Varcoe today. To carry out the joke she used Natalie Barr's ring and put it on her hand. Australia's online dating site eHarmony, in partnership with WILD LIFE Sydney Zoo in Darling Harbour, announced the launch of aHarmony, a revolutionary new dating platform specifically designed for animals, where pet owners can find fluffy, scaly or even prickly partners using eHarmony's famous Compatibility Matching System.

Welcome to the first lesson in our Practicing Deep Learning Series. Thoughtly is writing a multi-part tutorial series focused on understanding the foundations of Deep Learning, specifically as they apply to Natural Language Processing. This series, like our previous series, is targeted towards practitioners of machine learning. Now we are looking to provide information for developers who wish to cultivate a working familiarity with neural networks (NN) and deep learning (DL). Our goal is to help ML students, amateurs and professionals move from an awareness of neural networks to a working familiarity with the tools and workflows necessary to accomplish real-world tasks with a neural network.