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.

These courses teach you R programming and data science interactively, at your own pace, in the comfort of your browser.

Apple's personal assistant Siri is more of a glorified voice recognition feature of your iPhone than a deep conversation partner. A personal assistant that could truly understand human conversations and written texts might actually represent an artificial intelligence capable of matching or exceeding human intelligence. The Canadian startup Maluuba hopes to help the tech industry achieve such a breakthrough by training AI to become better at understanding languages. The key, according Maluuba's leaders, is building a better way to train AIs. Like humans, AI can only get better at understanding languages by practicing.

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.

It's 8:00 am on a Tuesday morning. You've awoken, scanned the headlines on your phone, responded to an online post, ordered a holiday sweater for your mom, locked up the house, and are driving to work, listening to some great new music on the radio. You've also used artificial intelligence (AI) more than a dozen times -- to be roused, to call up local weather report, to purchase a gift, to secure your house, to be alerted to an upcoming traffic jam, and even to identify an unfamiliar song. AI is already pervasive in our world, and it's making a huge difference in our everyday lives. But this is not the AI you've seen in sci-fi movies, with nervous scientists clacking on keyboards and attempting to halt machines from destroying the world. Sometimes it's obvious, like when you ask Siri to get you directions to the nearest gas station, or Facebook suggests a friend for you to tag in an image you posted online. Sometimes less so, like when you use your Amazon Echo to make an unusual purchase on your credit card (like that goofy holiday sweater) and don't get a fraud alert from your bank.

This is how humans operate. We try something, we judge the result and modify our behavior. What some considered to be science fiction only a few years ago, AI is edging closer to reality as decades of research -- combined with advances in compute power, memory, storage, network connectivity, sensors and the software that unites them all -- is poised to enable new classes of intelligent predictive analytics. These innovations will bring benefits to multiple industries, and to society as a whole in the way we lead our everyday lives. Al is going to change our lives for the better as machines learn, reason, act and adapt -- transforming industries by amplifying human capabilities, automating tedious or dangerous tasks, and solving some of our most challenging societal problems.

Meet the Innovators of Washington DC in a brand new episode of What's Next at https://youtu.be/Wacx_Nu7kX8 Artificial intelligence is poised to disrupt industries, social norms, and ways of life across the planet. Here are our top 10 sources for artificial intelligence news, events, job postings, and more across the web: #10 Get Started with these TED Talks One way to get your mind blown about where AI is and where it's heading check out TED's artificial intelligence playlist at http://bit.ly/2gENYd9 #9 Follow the Allen Institute for Artificial Intelligence Microsoft co-founder and billionaire Paul Allen has funded the non-profit Allen Institute for Artificial Intelligence and they have a very active Twitter handle profile with job openings, events, and news. Follow them at http://bit.ly/2gRqXmy #8 Let XPRIZE Challenge You on Facebook and Instagram XPrize is an organization that runs design competitions that challenge technologists to use advanced technologies like AI to benefit mankind and it's run by people like James Cameron, Larry Page, Arianna Huffington, and Ratan Tata. Like them on Facebook at http://bit.ly/2fLkFbG and follow them on Instagram at http://bit.ly/2grBV5f #7 Subscribe to Eazl's Weekly Brain Boosts on YouTube Here at Eazl, we're often reporting on advances in AI that the mainstream media ignores. You can get weekly tech updates that often feature what's happening in AI by subscribing to the Eazl YouTube channel at http://bit.ly/eazlyoutube #6 Learn with MIT's AI Open Courseware Playlist If you're interested in learning about the tech behind AI, MIT has an amazing playlist with tons of free lectures on beginner, intermediate, and advanced AI development topics at http://bit.ly/2grGr3k #5 Bring DeepLearn007 into Your Twitter Feed @deeplearn007 is one of the best people to follow on Twitter for everything AI and futurist technology.

Comment Remember that kid in middle school who was deeply into Dungeons & Dragons, and hadn't seen his growth spurt yet? Machine learning is sort of like that kid – deep, wide, and short – and not so tall. Big data – an increased availability of large data sets for training and deployment has also driven the need for deeper nets. Deeper nets – deep neural nets have multiple layers, and often possess higher-order architecture (width) within a given layer. Clever training – it was discovered that a large dose of unsupervised learning in the earlier stages of training allowed for the net to do its own automated, lower-level feature recognition and extraction, and pass those features on to the next stage for higher-level feature recognition.