In an effort to bolster its hold in artificial intelligence in developing Siri and alikes, Apple has acquired a Hyderabad (India)-based machine learning startup Tuplejump–that helps companies to store, process and visualize Big Data– for an undisclosed amount. "Apple buys smaller technology companies from time to time, and we generally do not discuss our purpose or plans," While Rai and Buddhavarapu, according to their LinkedIn pages, have moved to Apple–working as Software Engineer and Engineering Manager respectively– post-acquisition, Alur has joined Anaplan and is heading the engineering team at the cloud-based analytics platform for sales, operations and finance sector. Techcrunch also cited that, Apple is particularly interested in Tumplejump built "FiloDB", an open-source project that aims at applying machine learning concepts and analytics to massive amounts of complex data sets. While Tuplejump, as a standalone company, claimed to be an early adopter of Big Data technology and help Fortune 500 companies manage the same. According to Venturebeat, the Tumplejump team was well acquainted with open source big data tools such as the Apache Spark processing engine, the Apache Cassandra NoSQL database, and the Apache Kafka via publish-subscribe messaging system.

Recent advancements in technology have resulted in the rise of artificial intelligence. Since it was started in 1950, great strides have been made by incorporating cognitive ability while building these machines. These machines are built with the ability to independently make decisions and operate in a way that enables them to attain certain programmed goals. This development is characterized by a number of open source intelligence solutions companies to cater for different fields, such as for example Expert System. The number one users of this technology are in the health care sector.

Google has achieved something major in artificial intelligence (AI) research. A computer system it has built to play the ancient Chinese board game Go has managed to win a match against a professional Go player: the European champion Fan Hui. The research is documented in a paper in this week's issue of the journal Nature. The Google system, named AlphaGo, swept France's Hui, who is ranked a 2-dan, in a five-game match at the Google DeepMind office in London in October. AlphaGo played against Hui on a full 19-by-19 Go board and received no handicap.

Customer attrition, also known as customer churn, customer turnover, or customer defection, is the loss of clients or customers. Banks, telephone service companies, Internet service providers, pay TV companies, insurance firms, and alarm monitoring services, often use customer attrition analysis and customer attrition rates as one of their key business metrics (along with cash flow, EBITDA, etc.) because the cost of retaining an existing customer is far less than acquiring a new one. Companies from these sectors often have customer service branches which attempt to win back defecting clients, because recovered long-term customers can be worth much more to a company than newly recruited clients. Churn prediction is one of the most popular Big Data use cases in business. It consists of detecting customers who are likely to cancel a subscription to a service.

Recently we shared an introduction to machine learning. While making machines learn from data is fun, the data from real-world scenarios often gets out of hand if you try to implement traditional machine-learning techniques on your computer. To actually use machine learning with big data, it's crucial to learn how to deal with data that is too big to store or compute on a single computing machine. Today we will discuss fundamental concepts for working with big data using distributed computing, then introduce the tools you need to build machine learning models. We'll start with some naive methods of solving problems, which are meant only as an example.

Watch TV Show Online respects the intellectual property of others. Watch TV Show Online takes matters of Intellectual Property very seriously and is committed to meeting the needs of content owners while helping them manage publication of their content online. It should be noted that Watch TV Show Online is a simple search engine of videos available at a wide variety websites. Any videos shown on websites are the responsibility of those sites and not movietvstream.com. We have no knowledge of whether content shown on websites is or is not authorized by the content owner as that is a matter between the host site and the content owner.

Yes Bank, India's 5th largest private sector bank, has announced that YES TAG - India's 1st Chatbot-based banking app, has crossed the mark of 10,000 transactions. YES TAG was launched recently by Yes Bank and offers a "conversational smart banking" experience. Customers can download YES TAG and enjoy seamless Chat Banking on five messaging platforms: Facebook Messenger, Twitter, Skype, Telegram and WeChat. YES TAG app enables the chat bot to perform easy, anywhere and anytime banking. For instance, users can simply launch Facebook Messenger app from YES TAG and send'BAL' to the Yes Bank bot to know their account balance.

Google has released the latest iteration of its machine learning system that figures out what's in an image and captions it, and it's better than ever. The company also made it open-source. Google has been working on the program since 2014, and now says the algorithm can describe a picture with 93.9 percent accuracy. The big question for the Google team, as they were working on this newest iteration that uses an Inception architecture, was whether the algorithm could do more than simply identify objects within images set before it. To really interpret and caption a photo, AI needs to understand not only what's in the picture but also how certain objects in the image interact with one another.

A subset of those scientists, engineers and programmers have a different goal: They're trying to use computer science to solve one of the most complex and deadly challenges humans face: Cancer. And, for the most part, they are doing so with algorithms and computers instead of test tubes and beakers. "We are trying to change the way research is done on a daily basis in biology," said Jasmin Fisher, a biologist by training who works in the programming principles and tools group in Microsoft's Cambridge, U.K., lab. One team of researchers is using machine learning and natural language processing to help the world's leading oncologists figure out the most effective, individualized cancer treatment for their patients, by providing an intuitive way to sort through all the research data available. Another is pairing machine learning with computer vision to give radiologists a more detailed understanding of how their patients' tumors are progressing.

According to technologists, the future of Fintech looks like this: we are all going to be paying for goods and services with our thumbs, robots will make sure our pensions don't decrease, AIs will manage hedge funds, online only banks will be available, we'll be able to get cash from our favorite merchant with our cell phones, credit and debit cards will become extinct, global money transfers will be automatic, market place lending will offer a peer-to-peer option, and it will be impossible to launder money. The incumbents and infrastructure that drive future finance will eventually be replaced by financial technology. However, I believe only those Fintech firms doing it for the right reasons will be successful. Is the new technology cheaper than the current norm? Does it provide a seamless, easy to use solution?