In a bid to integrate machine learning-based solutions on its platform, Snapdeal, has acquired Gurgaon-based predictive marketing technology startup TargetingMantra for an undisclosed amount. It is a boutique technology company that has been working on the field of personalising shopping experience for customers on ecommerce platforms. Founded by Saurabh Nangia and Rahul Singh in March 2013, TargetingMantra, has offices in Palo Alto and Gurgaon. The company provides a unified platform to manage customers life cycle through personalisation, targeting and big data analytics. In 2014, TargetingMantra had raised 1.1 Mn in seed funding led by 500 Startups, Nexus Venture Partners, and One97 Mobility Fund.

Machine learning and artificial intelligence have gained notoriety among the general public through applications such as Siri, Alexa or Google Now. But, beyond consumer applications, these new hot areas of innovation are bringing unbelievable benefits to the different components of IT infrastructure that enable it, said David Meyer, Chairman of the Board at OpenDaylight, a Collaborative Project at The Linux Foundation, in his presentation at the DevOps Networking Forum last month. This is most evident in compute technology, which grew more and more powerful to make machine learning and AI happen. It's less obvious how networking contributes to and benefits from AI, he said. "I've never seen a technology this powerful that's moving as fast or is as cool," Meyer said.

Google is training its deep learning technology to come up with "rather dramatic" short stories based on sentences that researchers give it. Quartz spotted a paper published by Google researchers that includes examples of the work produced by Google Brain. It works using a type of artificial intelligence that examines existing works of fiction and uses that knowledge to come up with new examples. In order to teach its AI how to come up with new sentences, Quartz reports that Google provided it with 11,000 unpublished books. Researchers gave Google Brain a start sentence and an end sentence and left it to fill in the lines in between.

Artificial intelligence can control self-driving cars, beat the best humans at incredibly complex board games, and fight cancer; but one thing it can't do perfectly is communicate. To help solve the problem, Google has been feeding it's AI with more than 11,000 unpublished books, including 3,000 steamy romance titles. "come with me," she said. "talk to me," she said. "don't worry about it," she said.

What do voice search, machine translation, and ATMs have in common? Check out their answers here: https://goo.gl/jpbMy9

But if you're looking for our most hated automated thing in the universe, that's easy. It's the Interactive Voice Response (IVR) system, better known as a phone tree. While bots may one day have the potential to replace customer support and e-commerce, human touch will remain essential to the experience for the foreseeable future. Leverage bots to handle the basics, but also create easy opportunities for customers to connect with a live human (or have one call back) if the interaction gets stalled.

Human beings have a love-hate relationship with automation. Some of the time we love it, and the rest of the time we absolutely hate it -- without much daylight in between. We love DVRs, learning thermostats, and Dollar Shave Club. We hate subscriptions we don't want, memberships we don't use, and meaningless auto-reply emails. Some of the biggest names in tech are coming to TNW Conference in Amsterdam this May.

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

I have started learning tensorflow recently. I am trying to input my custom python code as training data. I have generated random exponential signals and want the network to learn from that. How can I use this graph as input vector in tensorflow?

The rise of free-to-play and other service-based business models in the online gaming market brought to game publishers problems usually associated to markets like mobile telecommunications and credit cards, especially customer churn. Predictive models have long been used to address this issue in these markets, where companies have a considerable amount of demographic, economic, and behavioral data about their customers, while online game publishers often only have behavioral data. Simple time series' feature representation schemes like RFM can provide reasonable predictive models solely based on online game players' login records, but maybe without fully exploring the predictive potential of these data. We propose a frequency analysis approach for feature representation from login records for churn prediction modeling. These entries (from real data) were converted into fixed-length data arrays using four different methods, and then these were used as input for training probabilistic classifiers with the k-nearest neighbors machine learning algorithm.