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.

Several years ago, I attended a Buddhist retreat in which I was introduced to the idea of the "retinue," a constellation of influential and supportive people whom one imagines in an enveloping cloud as one meditates. I took the concept one step further and decided to create an actual photo montage that I could hang on the wall above my desk: my childhood piano teacher, my high school English teacher, my rabbi, mentors in science, writers who encouraged me--in all, 20 people who had profoundly influenced me. Some members of my retinue were still living, some not. In some cases I could find the photographs myself. In others, I had to contact the mentors. When I finally tracked down William Gerace, who introduced me to physics nearly 50 years ago, he was puzzled as to why I should desire such a montage. We had not spoken for decades. Reluctantly, he sent me an old, out-of-focus photo of himself, dating back to the days when I knew him. Now, Gerace is a professor of science education at the University of North Carolina at Greensboro, after a 30-year career as a professor of physics at the University of Massachusetts Amherst, during which time he made the transition from theoretical nuclear physicist to leader in science education and co-founder of the Scientific Reasoning Research Institute at the University of Massachusetts, Amherst. When I knew him, in the late 1960s, he was a lowly instructor in physics at Princeton, where he had recently received his Ph.D. I was an undergraduate. The photo shows a man in his late 20s, about 5 feet 6, slight in build, dark hair beginning to thin, dressed in a button-down shirt and blue sweater, and a Mona Lisa smile. Each new mathematical technique Bill taught us was offered with the enthusiasm of a 12-year-old boy showing his friend a strange new butterfly. I first met Bill Gerace during a physics lab my sophomore year.

It had always been Katherine Goble's great talent to be in the right place at the right time. In August 1952, 12 years after leaving graduate school to have her first child, that right place was in Marion, Virginia, at the wedding of her husband, Jimmy Goble's, little sister Patricia. Pat, a vivacious college beauty queen just two months graduated from Virginia State College, was marrying her college sweetheart, a young army corporal named Walter Kane. Jimmy's other sister and brother-in-law, Margaret and Eric Epps, had journeyed from Newport News, and the newlyweds planned to accompany the Eppses back to the coast, hitching a ride to their honeymoon at Hampton's segregated Bay Shore Beach resort. "Why don't y'all come home with us too?" Eric asked Katherine. "I can get Snook a job at the shipyard," he said, using Jimmy's family nickname. "In fact, I can get both of you jobs." There's a government facility in Hampton that's hiring black women, Eric told Katherine, and they're looking for mathematicians. It's a civilian job, he told her, but attached to Langley Memorial Aeronautical Laboratory--the oldest outpost of the National Advisory Committee for Aeronautics, or NACA. Katherine listened intently as her brother-in-law described the work, her thumb cradling her chin, her index finger extended along her cheek, the signal that she was listening carefully. She and Jimmy made a living as public school teachers, but their paychecks were modest. The needs of their three growing daughters seemed greater by the day, and the couple could only just cover their basics and squeeze out a little extra for piano lessons or Girl Scouts. Deft with a sewing machine, Katherine bought fabric from the dry goods store and stayed up nights making school outfits for the girls and dresses for herself.

Yiling Chen (yiling@seas.harvard.edu) is Gordon McKay Professor of Computer Science at Harvard University, Cambridge, MA. Arpita Ghosh (arpitaghosh@cornell.edu) is an associate professor of information science at Cornell University, Ithaca, NY. Michael Kearns (mkearns@cis.upenn.edu) is a professor and National Center Chair of Computer and Information Science at the University of Pennsylvania, Philadelphia, PA. Tim Roughgarden (tim@cs.stanford.edu) is an associate professor of CS at Stanford University, Stanford, CA. Jennifer Wortman Vaughan (jenn@microsoft.com) is a senior researcher at Microsoft Research, New York, NY.