Is it time to bring text analytics in-house? A director of analytics at Visa, who is slated to speak at the 13th Annual Text Analytics Summit West in San Francisco next week, advises CIOs to consider these three questions before making a move. Text analytics and the CIO don't ordinarily go hand-in-hand. Instead, the emerging field of text analytics, or the mining of text to derive business insights, is typically farmed out to experts. But if the business is serious about investing in tools and bringing the technology in-house, CIOs should be involved, according to Ramkumar Ravichandran, a director of analytics at Visa Inc.

After an up-and-down start to the year, Chinese search giant Baidu (NASDAQ:BIDU) issued earnings last week that outperformed on a host of key indicators. As we've come to expect from Baidu, revenue growth remained brisk, increasing at a healthy 31% year-over-year pace to total 2.5 billion. In keeping with its recent quarters, increased spending crimped Baidu's operating profits, which grew only 2.6% compared with the first quarter of 2015. Either way, Baidu's earnings exceeded expectations on the top and bottom line. What's more, Baidu's guidance for second-quarter sales proved better than analysts anticipated, sending the company's shares up in after-hours trading the day of the announcement.

Donald Trump knows many words. He has the best words. He's going to find the best people to help him run the country. He knows many smart people. His presidency will be classy, huge, even.

People spend most of they're waking lives at work. At Kanjoya, we build products that help companies make work better for employees. We're a nimble, sharp, and passionate team, and we're looking for data scientists who want to have impact on a real product used by real people and who have a love for finding creative and thoughtful solutions to a variety of technical and product challenges. Ideally, you have interest or experience in one or more of a variety of areas: * A passion for language and the skills to computationally model it * Background in machine learning, artificial intelligence, and/ or natural language processing * Solid computer science foundation * Deep understanding of statistics * Bringing algorithmic code to production About Us: Our culture may be the best part of working here. We care about the work we do and the people we do it with.

Every once in a while an innocuous technical term suddenly enters public discourse with a bizarrely negative connotation. I first noticed the phenomenon some years ago, when I saw a Republican politician accusing Hillary Clinton of "parsing." From the disgust with which he said it, he clearly seemed to feel that parsing was morally equivalent to puppy-drowning. It seemed quite odd to me, since I'd only ever heard the word "parse" used to refer to the computer analysis of sentence structures. The most recent word to suddenly find itself stigmatized by Republicans (yes, it does somehow always seem to be Republican politicians who are involved in this particular kind of linguistic bullshittery) is "encryption."

Guest blog post by Xiu-Shen Wei, originally posted here. Deep Neural Networks, especially Convolutional Neural Networks (CNN), allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-arts in visual object recognition, object detection, text recognition and many other domains such as drug discovery and genomics. In addition, many solid papers have been published in this topic, and some high quality open source CNN software packages have been made available. There are also well-written CNN tutorials or CNN software manuals. However, it might lack a recent and comprehensive summary about the details of how to implement an excellent deep convolutional neural networks from scratch. Thus, we collected and concluded many implementation details for DCNNs. Here we will introduce these extensive implementation details, i.e., tricks or tips, for building and training your own deep networks. We assume you already know the basic knowledge of deep learning, and here we will present the implementation details (tricks or tips) in Deep Neural Networks, especially CNN for image-related tasks, mainly in eight aspects: 1) data augmentation; 2) pre-processing on images; 3) initializations of Networks; 4) some tips during training; 5) selections of activation functions; 6) diverse regularizations; 7)some insights found from figures and finally 8) methods of ensemble multiple deep networks. Additionally, the corresponding slides are available at [slide].

Evolution to us is as natural as breathing. To surpass limited natural capabilities has been our endeavor since time immemorial. Earlier it was with sticks and bones, an extension of the human hand; now it is with the help of microchips, transmitters, and processors. Earlier humans used to interact with technology as a separate entity i.e. outside the human body, but now changes are being realized within the individual--changing the meeting point of humans and technology. Technology has already touched almost all spheres of our life.

PRINCETON – Last month, AlphaGo, a computer program specially designed to play the game Go, caused shockwaves among aficionados when it defeated Lee Sidol, one of the world's top-ranked professional players, winning a five-game tournament by a score of 4-1. Why, you may ask, is that news? Twenty years have passed since the IBM computer Deep Blue defeated world chess champion Garry Kasparov, and we all know computers have improved since then. But Deep Blue won through sheer computing power, using its ability to calculate the outcomes of more moves to a deeper level than even a world champion can. Go is played on a far larger board (19 by 19 squares, compared to 8x8 for chess) and has more possible moves than there are atoms in the universe, so raw computing power was unlikely to beat a human with a strong intuitive sense of the best moves.

Register for Hardcore Data Science Day at Strata Hadoop World NYC 2015, which takes place September 29 to October 1. There are many algorithms with implementations that scale to large data sets (this list includes matrix factorization, SVM, logistic regression, LASSO, and many others). In fact, machine learning experts are fond of pointing out: if you can pose your problem as a simple optimization problem then you're almost done. Of course, in practice, most machine learning projects can't be reduced to simple optimization problems. Data scientists have to manage and maintain complex data projects, and the analytic problems they need to tackle usually involve specialized machine learning pipelines.

Steve Jurvetson has been referred to as "the smartest guy in the room," "the smartest person in Silicon Valley" and a "brainiac," among other laudatory monikers attesting to his prodigious intellect. The Internet is chock full of videos of lectures by and interviews with the venture capitalist, a partner at Draper Fisher Jurvetson. They span such topics as rockets and space, Moore's Law, machine learning, synthetic biology, technological innovation, the rich-poor gap and "the democratization of matter." That begins to reflect the breadth of Jurvetson's interests, and also his investments. Over the years, they have included companies that became transformational, from Hotmail (the Web as a platform) to Tesla (automaker as energy company).