It can seem like a Catch-22 is baked into the practice of meditation. It's meant, among other things, to foster patience--but meditation also seems to require considerable patience to work. Or at least "mindfulness meditation" does. When I began to toy with it several years ago--because of the demonstrable health benefits science was showing it could provide--I found that I couldn't stand the "mindfulness" version. In "The neuroscience of mindfulness meditation," a 2015 paper in Nature Reviews Neuroscience, Yi-Yuan Tang and colleagues write that mindfulness meditation is often described as "non-judgmental attention to present-moment experiences."

Technology breakthroughs always seem to come with a warning. The caution on big data from the experts was it's not about the data: i.e., it's pointless to collect big data unless you're going to analyze it -- and get some quantifiable business gain for your efforts. But today, making hay on big data is no longer just about the analytics. Companies chasing the next competitive edge will need to be hyperpersonal, and that will require speed and compute power. "If you're not able to create innovations to market in a timely, quick, effective way, then all of the technology is useless," Alfred Essa said at the recent Spark Summit East in Boston, Mass.

In this special guest feature, Debra Goldfarb from Intel writes that her recent panel discussion at SC16 illustrated just how fast Artificial Intelligence is advancing all around us. Computational science has come a long way with machine learning (ML) and deep learning (DL) in just the last year. Leading centers of high-performance computing are making great strides in developing and running ML/DL workloads on their systems. Users and algorithm scientists are continuing to optimize their codes and techniques that run their algorithms, while system architects work out the challenges they still face on various system architectures. At SC16, I had the honor of hosting three of HPC's thought leaders in a panel to get their ideas about the state of Artificial Intelligence (AI), today's challenges with the technology, and where it's going. My guests were Nick Nystrom from Pittsburgh Supercomputing Center (PSC), Ivan Rodero from Rutgers University, and Prabhat from NERSC at Berkeley National Laboratory. They answered both questions I put to them and from the audience.

Police and prosecutors face a daunting task of identifying, managing and reviewing a veritable tsunami of digital evidence generated daily by officer bodycams, jail house calls, interrogation videos, dispatch calls and closed caption television, which stretches staffing capabilities and resources. Today, Vū Digital for Law Enforcement's A.I. capabilities deliver a robust and dynamic evidence management solution for law enforcement agencies worldwide. The layer recognizes and identifies events, keywords and sequences of words generated by Vū's automated tagging engine to tag and identify instances of significance, including confessions, identification of personal information and references to certain "trigger" words. "Data can exist without A.I., but not vice versa," said Wade Smith, vice president of operations for Vū Digital. "First, we create data where it otherwise didn't exist, then we apply an artificial intelligence layer to identify relevant events from the digital evidence. In the end, artificial intelligence is only as powerful as the data it considers."

With reports in American Banker this week that Amazon is rumored to be exploring the potential acquisition of Capital One, an innovator in financial services marketing, is it too far-fetched to ask whether the financial services industry may be on the cusp of the next wave of disruption? As unheard of as this notion might have seemed a few years ago, financial services firms are facing important questions about their future, driven by disruptive technologies, ranging from big data to artificial intelligence and machine learning to fintech solutions and blockchain. The prospect of disruptive change looms large in the minds of senior financial services industry executives who participated in a recent survey focused on Big Data Business Impact: Achieving Business Results Through Innovation And Disruption, which was conducted by strategy advisors NewVantage Partners and published in January. The survey comprised senior business and technology leaders, and was heavily represented by leading financial services firms, which accounted for 75.8% of the survey respondents. Financial services industry participants included industry giants such as AIG, American Express, Bank of America, Blackrock, Capital One, Charles Schwab, CitiGroup, Fidelity Investments, Freddie Mac, JP Morgan Chase, Lincoln Financial, MetLife, Morgan Stanley, State Street, and Wells Fargo among others. One of the most provocative findings of the survey is that a substantial percentage of the financial services industry executives that were surveyed – 40% -- were concerned that their firm and their industry was at risk of major disruption in the coming decade, with a segment of these executives expressing the viewpoint that "it's transform or die".

Sarasota, Florida-based Revcontent, the fastest growing native ad network, has announced that it has acquired a machine learning company called Rover. Rover is known for developing advanced personalization and recommendation technology that will complement Revcontent's massive ad network. After the acquisition closes, Rover's offices in Sunnyvale, California will be turned into Revcontent's Silicon Valley headquarters. The terms of the acquisition were undisclosed, but the deal was reportedly valued at more than $30 million. Rover was founded by Jonathan Siddharth and Vijay Krishnan while the two of them were attending graduate school at Stanford University.

Apple is known for its secrecy when it comes to ongoing product development, but the company is operating a little different with its efforts around artificial intelligence and machine learning. Apple's director of machine learning, Carlos Guestrin, openly discussed Apple's plans to grow its engineering presence in Seattle in a new GeekWire interview. Guestrin joined Apple through the company's acquisition of AI firm Turi last fall, and now the University of Washington is naming a $1 million professorship after him to help discover and support new talent in the machine learning field: A new $1 million endowed professorship, made possible by Apple's acquisition of Seattle-based machine learning startup Turi last year, will give the University of Washington Computer Science & Engineering department a chance to attract more top talent in the field of machine learning and artificial intelligence. In an interview at Apple's downtown Seattle engineering office, Guestrin said it was important to him and the Turi team to support the University of Washington's computer science and engineering program, allotting the funding as part of the acquisition process. "It's another way to foster the university's development," Guestrin said.

We've traditionally seen machine learning interview questions pop up in several categories. The first really has to do with the algorithms and theory behind machine learning. You'll have to show an understanding of how algorithms compare with one another and how to measure their efficacy and accuracy in the right way. The second category has to do with your programming skills and your ability to execute on top of those algorithms and the theory. The third has to do with your general interest in machine learning: you'll be asked about what's going on in the industry and how you keep up with the latest machine learning trends. Finally, there are company or industry-specific questions that test your ability to take your general machine learning knowledge and turn it into actionable points to drive the bottom line forward. We've divided this guide to machine learning interview questions into the categories we mentioned above so that you can more easily get to the information you need when it comes to machine learning interview questions. These algorithms questions will test your grasp of the theory behind machine learning.

After completing Part 1 of Jeremy Howard's awesome deep learning course, I took a look at my AWS bill and found I was spending nearly $200/month running GPUs. It's not necessary to spend that much to complete his course, but I started working on a few extracurricular datasets in parallel and I was eager to get results. After talking with fellow students and reading a number of blog posts, I decided to try building my own box. Technology and hardware change so rapidly that I'm afraid much of post will become outdated soon, but I hope my general approach will still be useful for at least a little while. I started by reading a bunch of blogs to get the current consensus on which parts to buy.

The robots were Joe McGillivray's idea. The first one arrived at Dynamic Group in Ramsey, Minn., by pickup truck in two cardboard boxes. With a mixture of excitement and trepidation, McGillivray watched as a vendor unpacked two silver tubes, assorted blue-and-gray joints and a touch screen and put them all together. When he was finished 10 minutes later, McGillivray beheld an arm that, had its segments not all been able to swivel 360 degrees, might have belonged to a very large N.B.A. player or a fairly small giant. Its "shoulder" was mounted to a waist-high pedestal on wheels. If it were to hail someone across the room, its "elbow" would reach eye level. Below its "wrist," which was triple-jointed for extra dexterity, there were sockets for various attachments. McGillivray, not sure yet if he wanted to keep the contraption, stuck a piece of clear tape to the wrist and drew a happy face on it, which made the arm look a bit as if it were putting on a puppet show. He hoped that this would help it look nonthreatening.