Here's an analogy using a concept that we can all relate to: a supermarket. Picture the scene: Shopping list in one hand, shopping basket in the other, you're ready to tackle your weekly shopping in your local supermarket. Your items range from fruit and vegetables to washing detergent, perhaps with some free-range eggs thrown in for good measure. Quite the eclectic mix, but you know that you'll be able to find all you need under one roof. The fact that this is possible is in itself quite remarkable.

In this article, I present a few modern techniques that have been used in various business contexts, comparing performance with traditional methods. The advanced techniques in question are math-free, innovative, efficiently process large amounts of unstructured data, and are robust and scalable. Implementations in Python, R, Julia and Perl are provided, but here we focus on an Excel version that does not even require any Excel macros, coding, plug-ins, or anything other than the most basic version of Excel. It is actually easily implemented in standard, basic SQL too, and we invite readers to work on an SQL version. In short, we offer here an Excel template for machine learning and statistical computing, and it is quite powerful for an Excel spreadsheet.

According to one of Maurice Conti's TED presentations, we arrived to a new, augmented age, in which Artificial Intelligence gains a new role. The future holds a generated era in which artificial intelligence comes up with its own designs, its own ideas, its own products instead of all our tools being passive, us telling them what to do and them doing it. From now on, tasks will be executed by robots, AI and humans cooperating, robots providing perfect execution, AI the design and humans making the necessary decisions. But in order to get to this point it is crucial to build a "nervous system" for robots through sensors. Industry 4.0 has introduced the concept of data exchange withing manufacturing, opening countless business opportunities, some that were previously unimaginable. By implementing sensors experts will be able to make use of data of all type which were not at hand before, making products become even services as well.

The famous cartoon series "The Jetsons" predicted 53 years ago how our lives would look like in 2063 when all our "daily devices" such as home appliances or cars would be connected to the internet. In 2016, the Internet of Things (IoT) is still evolving to a convergence of multiple technologies, including wireless communication, real-time analytics, machine learning, commodity sensors, embedded systems, automation, and more. On October 21, a series of Distributed Denial of Service (DDoS) attacks caused widespread disruption of internet activity across Europe and the US. This attack was also unique because it targeted IoT devices due to the fact they have soft security profiles. As the awareness of what impact connected objects would have on our lives and on our security.

Has a digital coup begun? Is big data being used, in the US and the UK, to create personalised political advertising, to bypass our rational minds and alter the way we vote? The short answer is probably not. A series of terrifying articles suggests that a company called Cambridge Analytica helped to swing both the US election and the EU referendum by mining data from Facebook and using it to predict people's personalities, then tailoring advertising to their psychological profiles. These reports, originating with the Swiss publication Das Magazin (published in translation by Vice), were clearly written in good faith, but apparently with insufficient diligence.

First of all, I will explain the reason for the post title. For those who have not seen the films, I summarize: "A group of four illusionists win year after year to the public with their incredible magic shows and even mocking the FBI. GSMA is a great illusionist and MWC is their principal magic show. We are invited year after year to visit an event with unique keynote speakers, an enormous list of exhibitors, amazing performances and a great LinkedInplace where we can meet in person some of our social media contacts. What else can we ask for? I know that it is very ruthless to compare the GSMA with illusionists and the MWC as their greatest magic show, but at least I see quite a few reasonable resemblances, you don t. If in 2015 I wrote " MWC 2015: Everything Connected, Tapas and Jamon", and I argued as one the reasons to attend MWC was the fact it was celebrated in Barcelona. In 2016, in my post "GSMA need to think how to reinvent MWC" I justify the reasons why the MWC needed to reinvent itself. One thing has become clear to me after many years attending MWCs, this is the world's biggest phone and mobile networks show, with manufacturers set to unveil a raft of new phone handsets and new technology. However, the GSMA had insisted on introducing more and more distractions like Internet of Things (IoT), Connected Living, Connected Car, AR/ VR, Robots. Maybe the reason is because Telecom operators do not have the DNA to change. Still, many telecom operators take a dim view of some of the aggressive moves being made by these peers, especially when it comes to business models based on commercializing customer data. "I expected to see less hype and a dose of common sense" Starting by the announcement of Spain's Telefonica to introduce a broad plan "4th Platform" to help both consumer and business customers keep greater control over their data rather than giving it away to web giants Google, Facebook and Amazon. "I expected to see more applications where IoT will become a lot less exciting, but more useful and profitable.

At its Google Cloud Next conference in San Francisco today, Google announced the launch of a machine learning startup competition, the winner of which will receive cloud credits and $1 million from well-regarded Silicon Valley venture capital firms. The program is open to startups in the U.S. that have raised less than $5 million and use machine learning software, in any industry vertical. Entrants' use of the Google Cloud Platform public cloud infrastructure is "encouraged but not required," according to the application form. The winning startup gets $1 million in Google Cloud Platform credits, which can cover the cost of computing and storage services as well as fully managed machine learning tools. Employees will get G Suite subscriptions, which includes Gmail, Google Drive, Google Calendar, and other applications -- and Google engineers will help startup employees improve their machine learning models.

The media often likes to proclaim "The Year of This" or "The Year of That." With the greater attention given to advancing capabilities in artificial intelligence and machine learning, it seemed like a no-brainer to declare 2017 "The Year of AI." However, in the interest of truth and the pursuit of all things real, it appears we may have been all wrong. According to a new report from big data management company Talend, 2017 will not be the year of AI, but will actually be the year of (wait for it) real-time analytics. This finding, which is based on a survey of 189 people who attended a Talend event in Paris last fall, may run counter to the prevailing wisdom that AI and ML are taking off in big ways, and that they are doing so right now.

What impact will artificial intelligence (AI) have on the workforce? Will smart machines really replace a large number of people in a variety of jobs? These questions have been on the minds of a lot of people of late -- especially as AI becomes even more advanced. Clearly the technology will take away the need for some functions that are now performed by humans. But there's good reason to believe that AI will actually create a lot of new jobs as well -- at least in some areas of the economy.

The weight of the automotive and tech industries is fully behind the move toward self-driving cars. Cars with "limited autonomy"--i.e., the ability to drive themselves under certain conditions (level 3) or within certain geofenced locations (level 4)--should be on our roads within the next five years. But a completely autonomous vehicle--capable of driving anywhere, any time, with human input limited to telling it just a destination--remains a more distant goal. To make that happen, cars are going to need to know exactly where they are in the world with far greater precision than currently possible with technology like GPS. And that means new maps that are far more accurate than anything you could buy at the next gas station--not that a human would be able to read them anyway.