The same Microsoft technology currently used by healthcare organizations and yes, robots, is now being tested throughout the summer months by Cricket Australia, making it the first cricketing nation to integrate the company's team and player performance platform into its decision-making processes across fitness, game strategy, player recovery and team selection. As part of the new deal with Microsoft, Cricket Australia is now one of a few sports organizations worldwide experimenting with the new platform. At the recent Microsoft Australia Developer Conference, Chief Executive Officer Satya Nadella said he was "glad" with how Cricket Australia was planning to integrate "machine learning" into its overarching strategy. "Cricket is one of the richest sports when it comes to using machine data. But how could you harness the power of data to even start having a more intelligent informed conversation about performance of teams, performance of players?"

Democratizing Machine Learning has always been BigML's founding mission, so we are continually searching for new opportunities. As such, when a company is interested in our technology and is willing to help us further our cause of "Machine Learning for everyone", we feel the urge to collaborate. This is exactly what happened with our new education partner. Today we are happy to announce our educational collaboration with CICE, the Leading School in New Technologies Training in Madrid, Spain. CICE, the only Official Training Center in Spain for more than 20 multinational companies, is already a community of 70,000 students from 30 different countries.

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?

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.

"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.