Say hello to Nadine, a "receptionist" at Nanyang Technological University (NTU Singapore). She is friendly, and will greet you back. Next time you meet her, she will remember your name and your previous conversation with her. She looks almost like a human being, with soft skin and flowing brunette hair. She smiles when greeting you, looks at you in the eye when talking, and can also shake hands with you. And she is a humanoid.

In 2011, Watson made a dramatic public debut on the Jeopardy! Using IBM Power Systems and analytics software developed with open frameworks like Apache Hadoop and UIMA, Watson showcased a cutting-edge ability to understand complex natural language questions and sift through vast libraries of unstructured human knowledge. And Watson proved to know a thing or two about Beatles songs and Olympic history too. But while the show served as a great platform to introduce amazing new technology to the world, the intelligence behind Watson was designed to solve real business problems. IBM Watson Analytics allows business users to better compete by helping them get more value from their data.

Assistants are all the rage right now. Everyone seems to be working on one. People imagine them becoming the new interface to computers -- why bother with apps and searching the web when you can just ask your assistant to do it for you? Yet, a major challenge stands between the dream assistant and the current reality. It's called the multi-agent problem, and most companies are reluctant to talk about it.

The artificial intelligence and voice recognition space has been growing rapidly. According to a Gartner report, by 2020, 85% of customer interactions will be managed without a human. This is pretty much likely, as we are already teaching our machines to interpret data into logical solutions. Apps running on artificial intelligence should make a user's life easy, but what goes into building such apps? To understand it further, we spoke to some successful founders who have built and scaled apps running on artificial intelligence algorithms.

Mark Zuckerberg professed his drive to create an AI akin to Tony Stark's personal assistant in Iron Man, J.A.R.V.I.S., at the start of the year. Now, in a matter of months, it seems the Facebook founder is well on his way to achieving that goal. In a new interview, Zuckerberg reveals that his AI butler now lets him control his entire home, and also makes him breakfast. "That's actually easy," he states nonchalantly. "It's going well…I have it to the point where I can control everything in the house. It can control the lights, the temperature, the doors. It can make me toast," Zuckerberg tells The Verge.

Continuing its evangelism of machine learning, on Wednesday Google said two of its machine learning APIs introduced in March have advanced into open beta status. The first of these is the Cloud Natural Language API, which lets developers parse the meaning and structure of text. With initial support for English, Spanish, and Japanese, the API provides tools to understand the sentiment expressed in text, the relevant entities discussed (e.g. It is, in short, a way to help software understand. For companies, potential applications might include understanding how people feel about a product based on the sentiment expressed in online reviews, or how customers feel about support interaction based on analysis of transcribed calls.

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages. For eons they have carried out a huge variety of tasks, from manufacturing goods, to transporting people around, to helping us decipher the natural world, to simply entertaining us. Machines can fight, protect, heal, and even teach us. But what they have not been able to do until quite recently is to learn, make decisions, and act on their own. Today, intelligent machines are everywhere. From the Netflix recommendation en- gine to Google Translate to Appleâ s Siri voice-recognition system, artificial intelligence has become sufficiently accurate, reliable, and useful to find its way into numerous devices and applications. These technologies have taken off in parallel with a dramatic expan- sion of the amount and complexity of data, which provides fertile teaching ground from which machines can learn to make intelligent decisions on their own.

The popular dating app generally doesn't verify most user accounts, but that hasn't stopped spammers from pretending to offer the service. In recent weeks, automated bots masquerading as Tinder profiles have been telling real users to get "verified," as part of a clever scam to sell them porn, security firm Symantec said on Thursday. The spam bots first send off flirty messages, like "Wanna eat cookie dough together some time?" It's a free service, the spam bot will claim, and done "to verify the person you wanna meet isn't a serial killer lol." The spam bot will then send a link to a site called "Tinder Safe Dating."

Men are far more aggressive on online dating sites - but often'mass mail' women that are out of their league in the hope of a response, researchers have found. They say that by comparison, women tend to be more conscious of their own attractiveness to other users and approach fewer people. The study found major behavioural differences between male and female users when it comes to contacting potential partners. The researchers say men are far more aggressive on online dating sites - but often'mass mail' women that are out of their league in the hope of a response, researchers have found. Using data collected from Baihe, one of the largest dating websites in China, researchers from Binghamton University, University of Massachusetts Lowell and Northeastern University analysed the messages and how suitable each person actually was.

"Who cares if we find out we lost a customer after she left?" The objective of predictive analytics is not just understand why you lost a customer but how to prevent you from losing one before it happens. Insight, not hindsight is the essence of predictive analytics. How organizations instrument, capture, create and use data is fundamentally changing the dynamics of work, life and leisure. Analytics is the discovery and communication of meaningful patterns in data (corporate, product, channel, and customer). All of these need data. Data is the new oil. Cloud is the new pipeline. Digital use cases is the new experience frontier.