Nintendo will launch its first app for mobile devices in the U.S. on March 31, the video game maker announced Tuesday. Interestingly, Nintendo's first app, called Miitomo, isn't a game but a social experience where users interact with each other using Mii avatars, which were introduced with the Nintendo Wii video game console. Users start by downloading the app and creating their Mii with an avatar editor, or using a picture of themselves taken with their smartphone camera. Users can also import their Mii from the Wii or Nintendo 3DS through a QR code. Users then link the app to their Facebook or Twitter accounts to add friends, then answer a series of questions to help spur conversations.

When we first began working on Leada, we sought to better understand the data science industry by interviewing professionals in the field. As students simply wanting to learn more about data science, we ultimately created a free resource to inform both undergraduates and professionals about the data science industry. We accomplished this by having Q & A interviews with experts such as Mike Olsen, Hal Varian, Tom Davenport, and data scientists at LinkedIn, Facebook, Yelp, and more. The Data Analytics Handbook was not only instrumental in giving us the understanding we needed to feel confident in what we were creating; but was downloaded over 25,000 times, gave us dozens of contacts, and an immediate group of early adopters. Some experts took longer to contact than others (I emailed Hal Varian over 8 times) but you would be surprised who you can get 25 minutes of time to help inform others.

Our CEO and founder, Dr. Dan Steinberg recently wrote about gradient boosting machines. Gradient boosting machines are a powerful machine learning technique, and have been deployed with great success over the years in Kaggle competitions. However, specifics of the construction and core ideas of gradient boosting machines can remain a bit murky. For more a more detailed look at the shapes and sizes of the trees formed in gradient boosting machines, read the discussion on Dr. Steinberg's blog:

Don't forget to check the FAQ. It has links to lots of good resources including Elements of Statistical Learning which is free online and one of the standard texts (Murphy and Bishop are the other two standard texts). Yaser Abu-Mostafa wrote a book called "Learning from Data" which I enjoyed quite a bit but wasn't listed in the FAQ. It doesn't cover many algorithms (just one or two I think) but does a good job providing an approachable introduction to theoretical aspects of ML that aren't always covered in the standard texts. Colah's blog isn't updated often but all the articles are fantastic You may also want to follow Yann LeCun on facebook. He posts stuff about ML on a fairly regular basis.

Yesterday, the 46-year-old Google veteran who oversees the company's search engine, Amit Singhal, announced his retirement. And in short order, Google revealed that Singhal's rather enormous shoes would be filled by a man named John Giannandrea. On one level, these are just two guys doing something new with their lives. But you can also view the pair as the ideal metaphor for a momentous shift in the way things work inside Google--and across the tech world as a whole. Giannandrea, you see, oversees Google's work in artificial intelligence.

A team of US researchers has built an energy-friendly chip that can perform powerful artificial intelligence (AI) tasks, enabling future mobile devices to implement "neural networks" modelled on the human brain. The team from Massachusetts Institute of Technology (MIT) developed a new chip designed specifically to implement neural networks. It is 10 times as efficient as a mobile GPU (Graphics Processing Unit) so it could enable mobile devices to run powerful AI algorithms locally rather than uploading data to the internet for processing. The GPU is a specialised circuit designed to accelerate the image output in a frame buffer intended for output to a display. Modern smartphones are equipped with advanced embedded chipsets that can do many different tasks depending on their programming.

As the Primary season has progressed, 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? 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?

So he and his business partner, Dan Roth, decided to create a program that would help lawyers manage electronic documents for litigation. Their idea led them to purchase an e-discovery application. By 2000, Leib and his partner launched their own creation, Discovery Cracker. "We saw a gap in the marketplace," Leib says. Lawyers need tools to keep up with it." Instead of wading through piles of paper, lawyers now deal with terabytes of data and hundreds of thousands of documents. E-discovery, legal research and document review are more sophisticated due to the abundance of data. So while working as chief strategy officer at kCura in Chicago, Leib saw a need again in the market. "For years, lawyers have been stuck with antiquated tools that focus primarily … on Boolean search. Better tools are needed to truly understand data." "What is the future of the industry?

I fell in love with Machine Learning during my Master degree in Telecommunications Engineering and Information Technology. Since then I could never live without it and I see the world with different eyes. I have always been very fascinated by math and statistics, by how sometimes a very simple equation will describe extremely complex phenomena, how we can squeeze nature into a formula; at the same time my mind has always been captured by those phenomena, often very simple and part of our daily life reasoning and acting, that can't be represented by any mathematical form, no matter how convoluted. The idea of seeing the world through numbers has always exercised a certain spell on me. Then I discovered Machine Learning.

It isn't all doom and gloom, artificial intelligence will also bring about new and exciting opportunities. As former government technology adviser, Rohan Silva said: "The technology revolution is not just destroying jobs - it's creating huge numbers of new ones too, especially in areas like IT and the creative industries. And the good news is that the new jobs being created pay an average of 10,000 a year more than the jobs being lost to automation." So amid the fear and worry about artificial intelligence, it's important to remember that robots are built by humans – and humans are needed to program them, repair them, 'teach' them and keep developing the technology. So there's hope for us humans, yet.