This summer, the picturesque Birchcliffe Centre, a converted baptist church in the West Yorkshire village of Hebden Bridge, hosted an unusual festival. Guests danced across the sunlit floor to the music of Johann Sebastian Bach; they drank mugs of tea and watched talks. Outside, there was an "art walk" where attendees trudged up and down the muddy slopes, breathing in the scent of early summer flowers. There were nice places to eat, the village was welcoming. Everyone felt safe and included.

We are living in a digital era where Customer is the king. Many businesses have capitulated to this new realm and have started interacting with customers dynamically. Today the customers are free to navigate a merchant (eCommerce) website any way they fancy. Also the merchant can display content and place offers dynamically based on how a given customer interacts with his website. To add to the complexity purchase decisions are not necessarily made on the first visit itself.

Machine learning is a technical discipline that provides computers with the ability to learn from data (observations) without being explicitly programmed. Machine learning excels in solving complex, data-rich business problems where traditional approaches, such as human judgment and software engineering, increasingly fail. "Ten years ago, we struggled to find 10 machine learning based business applications. Now we struggle to find 10 that don't use it." While the basic concepts of machine learning have been around for decades, interest is at an all-time high.

Apple needs to change its attitude and approach to customer data, back away from the big data corner it has painted itself into, and use its upcoming World Wide Developer Conference (WWDC) to lay out some sort of artificial intelligence vision. Amazon has Alexa and its Echo. Google has Home, Assistant and a bevy of other services. Meanwhile, Apple has its long-in-the-tooth Siri that reportedly will be opened up to third party developers. Over the last two years, Apple has dug its heels in on privacy, vilified ad models to some degree and knocked Silicon Valley rivals (read Facebook and Google) for using customers as the products and collecting too much information.

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

In March this year, AlphaGo, a machine created by Google's artificial intelligence (AI) arm, DeepMind, trounced Mr Lee Sedol, a grandmaster at Go, the ancient Chinese game. AlphaGo used cutting-edge AI to beat a player acknowledged to be one of the greatest ever. For Go aficionados, the game will never be the same again, just as chess was changed when IBM's Deep Blue beat then world champion Garry Kasparov in 1997. That year, it was widely thought that while machines could master chess, beating the world's best at Go - a far more complex game with near-infinite variations of play - was still several decades away. Deep Blue used brute-force calculation and sheer computing power to beat the reigning world champion.

We introduce Bayesian Poisson Tucker decomposition (BPTD) for modeling country--country interaction event data. These data consist of interaction events of the form "country $i$ took action $a$ toward country $j$ at time $t$." BPTD discovers overlapping country--community memberships, including the number of latent communities. In addition, it discovers directed community--community interaction networks that are specific to "topics" of action types and temporal "regimes." We show that BPTD yields an efficient MCMC inference algorithm and achieves better predictive performance than related models. We also demonstrate that it discovers interpretable latent structure that agrees with our knowledge of international relations.

While the world has been fixated on following the soap opera of financial markets, a more profound and ubiquitous development has been taking place worldwide - the rapid development in artificial intelligence and the fourth industrial revolution, which we think will mark an endless wave of disruptions. We believe artificial intelligence (AI) is almost ready for wider adoption by businesses, in turn providing opportunities, but also risks for investors. With corporate longevity already on the decline - according to McKinsey, one in five listed companies in the US may not last beyond the next five years - the integration of AI into business applications will have significant investment implications in the years to come. Similar to how companies with no core assets could become leaders in their industries today, AI companies have the potential to become tomorrow's industry leaders. As noted by Mr Tom Goodwin of the French media group Havas, who would have imagined just a few years back that the world's largest taxi firm (Uber) would own no vehicles, the world's largest accommodation provider (Airbnb) would operate no rooms, while the world's most valuable media company (Facebook) would create no content?

Most of you know that I'm a learning consultant by trade and I apply the science of learning to real-world learning and performance improvement projects for my clients. You may also have noticed that one of my side interests is artificial and augmented intelligence. At least, I used to think that this was a side interest, only tenuously connected to my "day job," until several different threads converged in my brain and got me thinking: What if the Singularity – meaning the emergence of a true "artificial" intelligence (AI) -- has already happened and most of us just haven't noticed? Here's a short chronology of how my perception started to shift from "this is kind of cool" to "this could change everything." First I need to give you a bit of a disclaimer here: This story is not intended to be a detailed chronology of scientific developments in the fields discussed.

FUTURE PREDICTIONS: In 1998, Kodak had 170,000 employees and sold 85% of all photo paper worldwide. Within just a few years, their business model disappeared and they went bankrupt. What happened to Kodak will happen in a lot of industries in the next 10 years - and most people don't see it coming. Did you think in 1998 that 3 years later you would never take pictures on paper film again? Yet digital cameras were invented in 1975.