Virtual assistants like Siri, Cortana and Google Now have become a fixture in many people's daily lives, helping them get driving directions, find phone numbers and search the Web using their voice. Now a crop of savvy users have found ways to use those tools at work, too. Virtual assistants can tackle a range of workplace tasks, such as scour emails for important information, send reminders about future appointments and set up meetings automatically. Thanks to big data and artificial intelligence, assistants are better able to understand the way people really communicate and are beginning to anticipate their needs. According to Gallup, nearly two-thirds of working adults say they don't have enough time in the workday to complete what they set out to do.

Occasionally, I manage to be clever when I am not even trying to be clever, which isn't often. In a recent conversation about the new class of doomsday scenarios inspired by AlphaGo beating the Korean trash-talker Lee Sedol, I came up with the phrase human complete (HC) to characterize certain kinds of problems: the hardest problems of being human. An example of (what I hypothesize is) an HC problem is earning a living. I think human complete is a very clever phrase that people should use widely, and credit me for, since I can't find other references to it. I suspect there may be money in it. Here is a picture of the phrase that I will explain in a moment. In this post, I want to explore a particular bunny trail: the relationship between being human and the ability to solve infinite game problems in the sense of James Carse. I think this leads to an interesting perspective on the meaning and purpose of AI. The phrase human complete is constructed via analogy to the term AI complete, an ambiguously defined class of problems, including machine vision and natural language processing, that is supposed to contain the hardest problems in AI. That term itself is a reference to a much more precise one used in computer science: NP complete, which is a class of the hardest problems in computer science in a certain technical sense. NP complete is a subset of a larger class known as NP, which is the set of all problems for a certain class of non-God-level computers.

First the machines came for the menial jobs. Since then, they've climbed the corporate ladder and now sit on the board of directors of at least one venture capital firm in Japan. But that's Japan, you say. Well, if you thought your job was safe, you might want to think again. In the cover story of this week's MoneyWeek magazine, Matthew Partridge gets to grips with the nuts and bolts of what's driving the rise of artificial intelligence, or AI for those in the know.

DUBLIN--(BUSINESS WIRE)--Research and Markets has announced the addition of the "Global Artificial Intelligence Market Analysis & Trends - Industry Forecast to 2025" report to their offering. The Global Artificial Intelligence Market is poised to grow at a CAGR of around 44.3% over the next decade to reach approximately 23.4 billion by 2025. This industry report analyzes the global markets for Artificial Intelligence across all the given segments on global as well as regional levels presented in the research scope. It presents historical market data for 2013, 2014 revenue estimations are presented for 2015 and forecasts from 2016 till 2025. The study focuses on market trends, leading players, supply chain trends, technological innovations, key developments, and future strategies.

It doesn't look like a place to make groundbreaking discoveries that change the trajectory of society. But in these simulated, claustrophobic corridors, Demis Hassabis thinks he can lay the foundations for software that's smart enough to solve humanity's biggest problems. 'Our goal's very big,' says Hassabis, whose level-headed manner can mask the audacity of his ideas. He leads a team of roughly 200 computer scientists and neuroscientists at Google's DeepMind, the London-based group behind the AlphaGo software that defeated the world champion at Go in a five-game series earlier this month, setting a milestone in computing."

You don't have to go very far in the marketing tech space these days to bump into predictive technology that scores future customers, system intelligence that puts unnoticed pieces of data together or machine learning that recognizes useful patterns in piles of data. More and more, smart marketing tools are generating insights, assisting with or making decisions, and even taking actions. But this calls into question what the field is about. Since the first time someone stimulated interest in a new product, marketers have assembled information and made choices about ways to get the word out about their products, increase the number of customers, generate customer loyalty and boost sales. So it's not out of bounds to ask: what is the marketer's role when increasingly self-reliant and autonomous intelligent software does those things?

Google executives on Tuesday took turns extolling the capabilities and potential of the company's cloud computing infrastructure, with an eye toward dethroning cloud computing leader Amazon. "In the future, almost everything will be done in the cloud because it simply is a better way of doing computing," said Google CEO Sundar Pichai in opening remarks at the Google Cloud Platform Next 2016 conference. Such sentiment is expected at a cloud evangelism event. But Google wrapped its hard sell in an alluring package, the promise of machine learning. "This platform is not the end, it's the bottom," said Eric Schmidt, chairman of Google parent company Alphabet.

This classifier is based on deep learning algorithms applied to music. These techniques can find useful descriptions entirely on their own. This can have three reasons. The first one is that the class or label you are looking for was not part of the training set. The second one is that we only analyze 1 minute of the track for this demo.

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.

Microsoft is offering new tools to help developers build interactive bots that understand natural language, the company announced at its Build conference today. There are two key components, which are available in preview and are both part of the larger Cortana Intelligence Suite. "The first, Microsoft Cognitive Services, is a collection of intelligence APIs that allows systems to see, hear, speak, understand and interpret our needs using natural methods of communication," Microsoft said. "The second, the Microsoft Bot Framework, can be used by developers--programming in any language--to build intelligent bots that enable customers to chat using natural language on a wide variety of platforms including text/SMS, Office 365, Skype, Slack, the Web and more." Though Microsoft's own "Tay" bot became a public relations nightmare, the company demonstrated how artificial intelligence applications built with Microsoft technology can be useful in the real world.