Larry W. Bridgesmith, J.D., is an adjunct professor of law and coordinator of the Program on Law and Innovation at Vanderbilt Law School. You've probably seen one of the many commercials featuring the IBM supercomputer Watson, which made waves a few years ago when it easily defeated two "Jeopardy!" Watson even analyzes trends in music now, as seen in a recent advertising spot featuring Bob Dylan. Perhaps you read where a Google software program just beat a world master champion at Go, a game of intelligence, strategy and intuition far more complex than chess. Instead, the strength of artificial intelligence lies in its effect on the ways we do our jobs -- jobs we might have assumed would always be performed by humans.

For two of the datasets we are using a small sample for testing. The OpenStreetMap data is limited to edits in Azerbaijan from 2012 and earlier, and the Git data is just from the Django GitHub repository. The datasets we have selected span a wide range of densities, user and item counts, and types of ratings. Additionally, they provide a wide variety of information about items and users allowing us to explore different methods of extracting content vectors from the datasets.

Microsoft has also confirmed that mods will be coming to the mobile, Windows 10 and console games. It should provide feature parity -- more so than before, at least -- to each platform and open up its advanced tools to a broader group of players. That's important as Microsoft pushes into the classroom. The company is working on an Education Edition, based on MinecraftEdu, that'll be out later this summer. Some schools will use it to teach coding, so it makes sense to offer command blocks in the regular versions of Minecraft, where students can then practice at home.

Erik Brynjolfsson has a dream of the future. A vision of a world where computers entrench the power of a wealthy elite and push the majority into poverty. A world where the rising tide of technology doesn't lift all boats, but sucks under all but the biggest ships. Brynjolfsson is an economist at the Massachusetts Institute of Technology (MIT) and co-author of The Second Machine Age, a book that asks what jobs will be left once software has perfected the art of driving cars, translating speech and other tasks once considered the domain of humans. Dystopia is only one outcome foreseen by Brynjolfsson, but why does he even think it's a possibility? New technology has upended industries for millennia. But the advent of the power loom or steam engine didn't permanently rob men of labour. So what makes today different?

Choong-am Dojang is far from a typical Korean school. Its best pupils will never study history or math, nor will they receive traditional high-school diplomas. The academy, which operates above a bowling alley on a narrow street in northwestern Seoul, teaches only one subject: the game of Go, known in Korean as baduk and in Chinese as wei qi. Each day, Choong-am's students arrive at nine in the morning, find places at desks in a fluorescent-lit room, and play, study, memorize, and review games--with breaks for cafeteria meals or an occasional soccer match--until nine at night. Choong-am, which is the product of a merger between four top Go academies, is currently the biggest of a handful of dojangs in South Korea. Many of the students enrolled in these schools have been training since they were four or five, perhaps playing informally at first but later growing obsessed with the game's beauty and the competitiveness and camaraderie that surround it.

How do you combine historical Big Data with machine learning for real-time analytics? TIBCO outlines an approach, use cases, and tools of the trade. "Big Data" has gained a lot of momentum recently. Vast amounts of operational data are collected and stored in Hadoop and other platforms on which historical analysis is conducted. Business intelligence tools and distributed statistical computing are used to find new patterns in this data and gain new insights and knowledge for a variety of use cases: promotions, up- and cross-sell campaigns, improved customer experience, or fraud detection.

Google's move to give away its latest machine-learning software, key to its speech- and photo-recognition programs, isn't as crazy as it may appear. The unit of Alphabet said Monday it is releasing its TensorFlow system for free under an open-source license. That's one of the company's crown jewels, a machine-learning program that teaches computers to be smarter. But Google retains much of what makes its machine-learning effort special: massive piles of data, a powerful network of computers to run the software and a big team of artificial-intelligence experts to tweak the algorithms. "It's not a suicidal idea to release this," said Nello Cristianini, a professor of artificial intelligence at the U.K.'s University of Bristol.

Many people and companies seem to think of "cognitive computing" as an area separate from analytics. Most large organizations today have significant analytical initiatives underway, but they think of the cognitive space as being an exotic science project. One executive told me, "We have no desire to win Jeopardy," an allusion, of course, to the IBM Watson project from 2011. But cognitive computing is not just about Watson, and it's not an exotic science project. In fact, I'd argue that cognitive computing is a logical extension of analytics work.

The web is full of good explanations of machine learning algorithms. And every second applicant for a data science position has finished the Coursera course on machine learning. Theory will not help you choose good values for the 16 parameters a standard implementation of a random forest takes. The default values are good to get started, but which parameters should you modify depending on your data? Choosing the right features, algorithms and parameters is an art.

Machine learning is not new. SAS has been doing it for over 20 years and some early machine learning papers date back to the 50's. So why is it one of the hottest topics at the Strata Hadoop World conference later this week? Clearly, Hadoop is playing a major role in the increased focus on machine learning. Powerful, low-cost distributive computing environments coupled with Hadoop give data scientists the ability to run iterative models (like neural networks) they may not have been able to in the past.