It's no secret that I love technology. From the domination of mobile to the latest in recruitment tools and gamification, and how video and live streaming is having an impact on hiring and training--changes are afoot that many of us couldn't have imagined 15 or so years ago. The reason this "tech meets HR" marriage is so exciting is how quickly the technology evolution has disrupted HR and enhanced the way HR professionals get things done. Now there's another big disrupter on the horizon, one that you would be wise to keep your eyes on: Artificial intelligence. In layman's terms, artificial intelligence (or AI) is an area of computer science where computers are "developed" to behave much the way humans do.

Udacity 2,479 views What is machine learning?

Starred articles are candidates for the picture of the week. A comprehensive list of all past resources is found here. We are in the process of automatically categorizing them using indexation and automated tagging algorithms. IBM makes quantum computing available in the cloud 2016 Big Data 100: 20 Coolest Platform And Tools Vendors The fight against antimicrobial resistance across Europe Cool video pie chart Inside Facebook's Biggest Artificial Intelligence Project Ever How to tell two radically different stories from the same dataset Data science, no coding required: DataRobot's automated platform Google launches new machine learning platform TechCrunch Cleaning Big Data: Most Time-Consuming, Least Enjoyable Data Scienc... Forbes Beyond the hype: the hard work behind analytics success MIT Sloan Deep learning will be huge -- and here's who will dominate it Years You Have Left to Live, Probably - Nice interactive chart by FlowingData Alooma gets $11.2 million Series A to solve data science pain points AI program wrote a short novel, and almost won a literary prize How facial recognition can expose your life to strangers Data science, no coding required: DataRobot's automated platform Deep learning will be huge -- and here's who will dominate it Alooma gets $11.2 million Series A to solve data science pain points

When Robert Schapire started studying theoretical machine learning in graduate school three decades ago, the field was so obscure that what is today a major international conference was just a tiny workshop, so small that even graduate students were routinely excluded. But it has become one of the hottest fields in computer science, turning once-obscure academic gatherings like the upcoming Annual Conference on Neural Information Processing Systems in Barcelona, Spain, into a sold-out affair attended by thousands of computer scientists from top corporations and academic institutions. "It's been really something to see this field develop, and to see things that seemed impossible become possible in my lifetime," said Schapire, a principal researcher in Microsoft's New York City research lab whose machine learning research is widely used in the field. The NIPS conference, which starts Monday, is so popular because machine learning has quickly become an indispensable tool for developing technology that consumers and businesses want, need and love. Machine learning is the basis for technology that can translate speech in real time, help doctors read radiology scans and even recognize emotions on people's faces.

My academic background is in computer science and philosophy. My work has been about the relationship between those two fields. What do we learn about being human by thinking about the quest to create artificial intelligence? What do we learn about human decision making by thinking of human problems in computational terms? The questions that have interested me over the years have been, on the one hand, what defines human intelligence at a species level? And secondly, at an individual level, how do we approach decision making in our own lives, and what are the problems that the world throws at us? I find myself interested at the group level, the society level, and the civic level in a couple of different ways. I've been encouraged by what I've seen over the last few years in terms of the norms of the sciences changing. It used to be that people were scared to publish their models because that was the secret sauce; that was their advantage over other research groups.

Using algorithms partially modeled on the human brain, researchers from the Massachusetts Institute of Technology have enabled computers to predict the immediate future by examining a photograph. A program created at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) essentially watched 2 million online videos and observed how different types of scenes typically progress: people walk across golf courses, waves crash on the shore, and so on. Now, when it sees a new still image, it can generate a short video clip (roughly 1.5 seconds long) showing its vision of the immediate future. "It's a system that tries to learn what are plausible videos -- what are plausible motions you might see," says Carl Vondrick, a graduate student at CSAIL and lead author on a related research paper to be presented this month at the Neural Information Processing Systems conference in Barcelona. The team aims to generate longer videos with more complex scenes in the future.

The newest prime number is more than 22 million digits long Watch This Robot Solve a Rubik's Cube in Less Than 2 Seconds Thanks to a $28 million grant, Harvard is researching artificial in... The world has lost one of its greatest minds in science.

I lead the Machine Learning Team at Udacity.

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As a new sub-discipline of Data Science, I notice that SYSTEMS Analytics is starting to get some traction! There are a couple of Analytics graduate level programs with *Systems* in its title (Stevens Institute of Technology and University of North Carolina are the only ones I know). Web search brings up NO books on *Systems* Analytics. With the publication of my book with *Systems* in the title, that gap has been filled now! "SYSTEMS Analytics: Adaptive Machine Learning workbook". My last Analytics startup launched in 2013 explicitly used SYSTEMS Analytics in our Retail Recommendation and Uplift SaaS product; my initial bias for the Systems approach was confirmed by the success of our product.