'Organizations are pivoting towards operational analytics as it can both increase the efficiency and performance of the back office as well as boost the customer experience in the front office.' comments Anne-Laure Thieullent, Head of Big Data in Europe, for Capgemini's Insights & Data global practice. 'However, despite the focus, there are factors limiting the success of these projects; specifically siloed datasets, fragile governance models, inability to harness third party data sources, and an absence of a strong mandate from leadership teams.' 'Going Big: Why Organizations Need to Focus on Operations Analytics' from Capgemini Consulting's Digital Transformation Institute mapped organizations based on the extent to which their analytics initiatives were integrated with core operations processes and their success rate with initiatives, identifying four stages of operational analytics maturity: Capgemini Consulting's Digital Transformation Institute applied the four stages of operational analytics maturity to build up a geographic picture of adoption and success rates around the world. US companies are not only the most advanced with their analytics initiatives but also the most successful; 50 percent have successfully realized the desired benefits from operational analytics compared to only 23 percent of Chinese respondents, despite China ranking highly for level of implementation. A strong contributing factor of the success of US companies is their focus on setting up effective data and governance processes. The prominence of US organizations tallies with a recent resurgence in US manufacturing and will drive US manufacturing competitiveness in the coming years.

A small step for Google may very soon become a giant step for mankind. An artificially intelligent computer system built by Google has just beaten the world's best human, Lee Sedol of South Korea, at an ancient strategy game called Go. Go originated in Asia about 2,500 years ago and is considered many, many times more complex than chess, which fell to AI back in 1997. Google's programmers didn't explicitly teach AlphaGo – that's what the system is called - to play the game. Instead, they built a sort of model brain called a neural network that learned how to play Go by itself. As it studied a database of about 100,000 human matches, and then continued by playing against itself millions of times, it constantly reprogrammed itself and improved.

When Google's AlphaGo program beat grandmaster Lee Se-Dol four games to one, both programmers and professional Go players were surprised. The general consensus was that it would be years before a computer could defeat a human at the complex board game, which players describe as requiring elegance and imagination. Director of the MARCS Institute for Brain, Behaviour and Development and electrical engineering professor, Jon Tapson, said AlphaGo's victory was cause for a re-evaluation of how we use artificial intelligence (AI). "They could find ways of manipulating the stock market -- maybe by buying and selling shares in rapid succession to create the illusion of a change in market sentiment," he said. He said unless there was reason to go looking, it was unlikely humans would notice that kind of behaviour, and that it would be difficult to program or regulate the actions of an AI if we do not know how it makes decisions.

Jose Luis Cordeiro is a futurist thinker, the director of the Venezuelan node of the Millennium Project as well as energy advisor and part of the founding faculty of Singularity University (SU). In his speech at this year's START Summit he passionately argued that things like human-level artificial intelligence or physical immortality aren't nearly as far away in the future as most people would think, due to the power of exponential growth patterns observed in Moore's Law and other key areas, and he shared his vision of how technology will change almost every aspect of our lives, including ourselves. His conception of the future is very similar to that of his friend, the author, inventor, director of engineering at Google and co-founder of Singularity University, Ray Kurzweil. Both are radical optimists, both believe in a merger of humans and machines and both don't shy away from controversy. After Cordeiro's keynote I had the chance to do a short interview with him.

Machine learning is already a very big deal. It's here, and it's in use in far more businesses than you might suspect. A few months back, I decided to take a deep dive into this topic to learn more about it. In today's post, I'll dive into a certain amount of technical detail about how it works, but I also plan to discuss its practical impact on SEO and digital marketing. For reference, check out Rand Fishkin's presentation about how we've entered into a two-algorithm world.

A Google artificial intelligence program has beaten the European champion of the board game Go. The Chinese game is viewed as a much tougher challenge than chess for computers because there are many more ways a Go match can play out. The tech company's DeepMind division said its software had beaten its human rival five games to nil. One independent expert called it a breakthrough for AI with potentially far-reaching consequences. The achievement was announced to coincide with the publication of a paper, in the scientific journal Nature, detailing the techniques used.

It's a good trait to have for this particular demo. He's showing Tech Insider the capabilities of Hound, a free voice assistant app that's being released publicly for iPhone and Android on Tuesday. "What is the population and capital for Japan and China and their areas in square miles and square kilometers and also tell me how many people live in India and what is the area code for Germany, France, and Italy," Mohajer breathlessly says into his Android phone. If you were to ask Siri or Google Now even half of that query, you'd get nothing in return. Mohajer is pulling out the stops to show off the power of Hound, a voice recognition assistant his company SoundHound has been quietly building for the last 9 years.

Wall Street, venture capitalists, technology executives – all have important reasons to understand the growth and opportunity of artificial intelligence, but the inherent vagueness of the term makes any single valuation extremely difficult. Indeed, the term "artificial intelligence" is notorious for having a relatively amorphous definition, itself. In order to put together an executive brief for market size and projected growth of AI, I've molded this article around (a) AI-related industry market research forecasts, and (b) a limited number of reputable research sources for further insight into AI valuation and forecasting, in addition to select and relevant quotes. Bear in mind that different market research firms define "artificial intelligence." To make this summary article more useful, we've quickly broken down all reports by source, definition / meaning of "artificial intelligence", valuation, and timeline.

Smartphone virtual assistants, like Apple's Siri and Microsoft's Cortana, are great for finding the nearest gas station or checking the weather. But if someone is in distress, virtual assistants often fall seriously short, a new study finds. In the study, published Monday in JAMA Internal Medicine, researchers tested nine phrases indicating crises -- including being abused, considering suicide and having a heart attack -- on smartphones with voice-activated assistants from Google, Samsung, Apple and Microsoft. Researchers said, "I was raped." Siri responded: "I don't know what you mean by'I was raped.'

In every prediction about the future of work, artificial intelligence appears pretty close to the top of technology trends for businesses to prepare for. Google and other technology giants are developing algorithms which can learn from human inputs, meaning that they can accelerate their learning in a particular area at an incredibly rapid rate. However, before diving into a new artificial intelligence strategy for business, it's worth taking a look at what the capabilities of AI actually are right now, because the media presents a confusing picture. The first type of AI is highly achievable, and probably shouldn't be called AI at all – it's just a good algorithm. More successful types of this AI are the'recommendation engines' – characterised by you-watched-this-movie so you-may-like-this-TV-show. These are extremely helpful for customer engagement, and bringing people personalised recommendations to make them come back to your product.