Today whole economy is changing into Digital economy and disrupting all the respective markets and industries. Businesses are shifting from selling physical things to digital things. Just to take an example of Music industry – in old days we had to go to store to buy LP records, then came cassettes and CDs which were then disrupted due to introduction of MP3 format in 1990s. Later came IPod/IPhone and people could carry their music with them. Spotify further changed this to streaming music, so now you don't have to download music at all.

Computer Science is a remarkable field that allows people to build incredible tools – but at the end of the day, those tools we build are still just tools. Personally, I'm not interested in building mechanisms unless they are used to reduce human suffering. As a PhD student in Computer Science, I have the flexibility to set up my own projects and work on efforts that I find both intellectually stimulating and personally meaningful. My research focuses on cyber-security, machine learning and natural language processing – all fields that have produced amazing tools and insights. My goal and desire is to both add to these fields, and use their instruments to analyze and root out the dark corners where traffickers and child abusers spend their time on the Internet.

As the Primary season progresses there's been no end of political pundits backpedaling and mea-culpa-ing over their previous inability to predict the rise of Donald Trump to become the frontrunner in the GOP. From Charles Krauthammer admitting that it was wrong to laugh at The Donald to innumerable others, both liberal and conservative, wishing they'd take Trump seriously, it seems like just about everyone in the Predictive Class will be dining on roast crow this Easter. But why did they get things so wrong? Was it because they assumed that he'd "crash and burn" like John Podhoretz did? Was it because they assumed that he couldn't win because Republican voters hated him, as implied by Patrick Murray of Monmouth University when releasing early poll results in June of 2015?

Dave and Matt discuss the recent Hot Topic of A.I. Should we create an A.I? Is A.I a form of life?

Top Machine Learning & Data Mining Books - for this post, we have scraped various signals (e.g. We have combined all signals to compute a Quality Score for each book and publish the list of top Machine Learning and Data Mining books. The readers will love the list because it is data-driven & objective. This book is very well rated on Amazon website and is written by three professors from USC, Stanford and University of Washington. The three authors: Gareth James, Daniela Witten, & Trevor Hastie all have backgrounds in statistics.

No longer the exclusive domain of data-reliant businesses like Google, Microsoft, and Amazon, Machine learning has been making its way to the masses as an essential approach to data. Today, machine learning is understood and accepted by a more mainstream audience, and has become a measurable driver for big business both on and offline. There are three key reasons why machine learning has become one ofthe top 10 strategic technology trendsthat will shape digital business opportunities through 2020. First, the volume of data companies now collect is so massive that many struggle to make sense of it. Machine learning allows companies to take advantage of the information they already have.

Here are choice articles on hot leadership topics culled from the business schools, the business press and major consulting firms, to start off your work week. Highlights include leading in the digital age, changing the game in industrial goods through digital services, the rise of machine learning, how women and men internalise the glass ceiling, and the explosion of wearing work on our wrists. Note: Some links require you to register or are to publications that have some form of limited paywall. "Servant leadership is not a new concept. Robert Greenleaf introduced the idea back in 1977. In recent years, however, concrete evidence has emerged that the approach delivers more than warm, fuzzy feelings. Last month, the first quantitative study that begins to explain a connection between servant leadership and improved individual performance was published by researchers in Canada. This new evidence may help move servant leadership from a niche practice to one adopted by more executives."

One of the most hotly contested bits of real estate today is one you can't see. As we move into an increasingly wireless-connected world, staking out a piece of the crowded electromagnetic spectrum becomes more important. DARPA is hoping to help solve this issue with its latest Grand Challenge, which calls for the use of machine-learning technologies to enable devices to share bandwidth. The Spectrum Collaboration Challenge (SC2) is aimed at alleviating an ongoing technological headache. Ever since the invention of radio, it's been recognized that there is only so much of the electromagnetic spectrum to go around, so government regulations were imposed to allocate bandwidth.

It can be hard to know where to start when you want to learn about Bayesian statistics. I am frequently asked to share my favorite introductory resources to Bayesian statistics, and my go-to answer has been to share a dropbox folder with a bunch of PDFs that aren't really sorted or cohesive. In some sense I was acting as little more than a glorified Google Scholar search bar. It seems like there is some tension out there with regard to Bayes, in that many people want to know more about it, but when they pick up, say, Andrew Gelman and colleagues' Bayesian Data Analysis they get totally overwhelmed. And then they just think, "Screw this esoteric B.S." and give up because it doesn't seem like it is worth their time or effort.

Artificial Intelligence is just around the corner. Of course, it's been just around the corner for decades, but in part that's our own tendency to move the goalposts about what'intelligence' is. Once, playing chess was one of the smartest things you could do. Now that a computer can easily beat a Grand Master, we've reclassified it as just standard computation, not requiring proper thinking skills. With the rise of deep learning and the proliferation of machine learning analytics, we edge ever closer to the moment where a computer system will be able to accomplish anything and everything better than a human can. So should we start worrying about SkyNet?