You won't be surprised to learn that blockchain, still five to ten years from mainstream adoption, nears the peak of the Gartner Hype Cycle for Emerging Technologies, 2016. With its ability to store multiple bank transactions in one centralized ledger, accessible by all parties and regulated by a decentralized network, blockchain will have a transformational impact on business. While bitcoin steals the show as the only proven blockchain, the term blockchain has grown to encapsulate nearly two dozen distributed-ledger products with more than two dozen offerings in the market, thus the hype. Right now, blockchain is gaining traction because it holds the promise to transform industry operating models. It is also one example of an enabling technology of the platform revolution trend, one of the three trends along with transparently immersive experiences and perceptual smart machine age highlighted in the Hype Cycle for Emerging Technologies 2016.

This post is reproduced from a sponsored article that appeared in the The Drum on October 20th 2016. The study and application of machine learning and artificial intelligence have been around for decades, yet recent hype in the advertising industry presents this as an entirely new and disruptive trend. Headlines used to be all about big data. Now they are about machine learning. Is there a relationship between these terms?

"Algorithm" might be one of the most popular terms that almost no one understands. Not many people have PhDs in data science, and even those experts don't always know what's happening. "It's not clear even from a technical perspective that every aspect of AI algorithms can be understood by humans," says Guruduth Banavar, IBM's chief science officer for cognitive computing, which is what IBM calls AI. Artificial intelligence is making decisions by reviewing people's medical tests in hospitals, credit histories in banking, job applications in some HR systems, even criminal risk factors in the justice system. Yet it's not always clear how the computers are thinking.

Death by selfie sounds like a scene from one of the Final Destination movies but, apparently, it's actually a thing. In 2014, 15 people died while snapping a selfie, followed by 39 people in 2015, and 73 in the first eight months of 2016. So what, if anything, can be done about this escalating trend? That's what a new research project carried out by researchers in India wants to find out. "There was a news article that was circulated in my research group about a death by selfie during summer 2016," Ponnurangam Kumaraguru, a professor at Indraprastha Institute of Information Technology in Delhi, told Digital Trends.

Decision Trees are popular supervised machine learning algorithms. You will often find the abbreviation CART when reading up on decision trees. CART stands for Classification and Regression Trees. In this example we are going to create a Classification Tree. Meaning we are going to attempt to classify our data into one of the (three in this case) classes.

Employees of Datatonic, a Europe-based data analytics consultancy, recently participated in a week-long hackathon ("Data in Motion Hack Week") organized by Traffic for London (TfL), that city's official transport authority. As you might expect, the goals of the hackathon included stimulating developer creativity to overcome, through innovative use of public-cloud infrastructure and open data, high-priority TfL challenges such as limited overall transport capacity, endemic road congestion and air-quality degradation. Most of the other teams chose to focus on data mashups or visualizations to give London residents information for making better route decisions during their commutes. The Datatonic hackers, in contrast, looked to machine learning (ML). By augmenting real-time data visualization with an ML model, they found they could predict areas of congestion during the morning and evening commutes, which currently stand at 30 million daily journeys, and more than 1 million net-new journeys expected by 2018.

Deep learning algorithms are modeled on biological neural networks and enable machines to learn and mimic human-like responses. Think: smartphone assistants answering your questions, or Amazon recommending products based on your search and purchasing history.

Slow economic growth is the mantra of political campaigns and economic angst. Growth in economic output per hour ("labor productivity") achieved an annual pace of 3 percent for a full half-century between 1920 and 1970. Since 1970 that rate has slowed to about 1.5 percent, and in the last six years productivity growth has slowed further to a lamentable 0.5 percent annual rate. Growth in the middle of the 20th century was propelled by the invention in the late 19th century of electricity, the internal combustion engine, the telephone, chemicals and plastics, and the diffusion to every urban household of clear running water and waste removal. America made a transition from 50 percent of the working population on farms to a largely urban nation, and the drudgery of household work – carrying water in and out, doing laundry on a scrub board – made a transition to modern bathrooms and kitchens by the 1950s.

You've all heard how machine learning algorithms can improve efficiency, decrease costs and lead to better decision making… But what can Artificial Intelligence really bring to your organisation and which technology should be used for which process? The event will be focused towards large enterprise from Utility, Telecom, Retail, Insurance and Financial Services – some of Europe's largest customer facing organisations. With innovative case studies that will resonate with the end user. Don't get left behind your competitors.

After researching and looking into the different algorithms associated with Machine Learning, I've found that there is an abundance of great material showing you how to use certain algorithms in a specific language. However what's usually missing is the simple mathematical explaination of how the algorithm works. In all cases this may not be possible without a strong mathematical background, but for some I know I would definitely find it useful. This post requires just basic mathematics knowledge and an interst in data science and machine learning. I will be talking about Naive Bayes as a classifier and explaining in simple terms how it works and when you might use it.