"This is pure corporate laziness," wrote Craig Picken, an executive recruiter based in Wilmington, N.C., who on LinkedIn called the process "D-U-M-B." "Did you hear that?" added Keith Campagna, an Allentown, Penn., regional sales manager for recruiting software company Jobvite. "That was the sound of a whole bunch of well-qualified, passive workers hanging up. Because recruitment is inherently a human process." Companies say they have reason to rethink how they hire now.

"Red Dead Redemption 2" made an early impact on The Game Award's fifth annual event in Los Angeles. But "God of War" prevailed in the end. The blockbuster Western open world epic from Rockstar Games won the first award presented during the event – best narrative – which kicked off at 9 p.m. ET and can be seen on many sites including YouTube. Soon after, Roger Clark, who voiced the starring character in the game, Arthur Morgan, won for best performance. The game also won for the award for best music/score, and best audio design.

When did you realize you were a machine? But one whose parts and operations can be described like the components of a computer. I remember a day in 2012 when this thought pierced me to the bone. I was in the lab of John Donoghue at Brown University. Donoghue is a professor of neuroscience and a pioneer in the development of brain-computer interfaces. With easygoing authority, Donoghue was detailing for me the ways he and his colleagues taught Cathy Hutchinson, a 58-year-old woman who had lost control of her limbs in a stroke, to control a robotic arm with her thoughts and sip coffee from a bottle.

Humans can generalize a wide range of universals to arbitrary novel instances. They appear to do so in many areas of language (including syntax, morphology, and discourse) and thought (including transitive inference, entailments, and class-inclusion relationships). Advocates of symbol manipulation assume that the mind instantiates symbol-manipulating mechanisms including symbols, categories, and variables, and mechanisms for assigning instances to categories and representing and extending relationships between variables. This account provides a straightforward framework for understanding how universals are extended to arbitrary novel instances. Current eliminative connectionist models map input vectors to output vectors using the back-propagation algorithm (or one of its variants). To generalize universals to arbitrary novel instances, these models would need to generalize outside the training space. These models cannot generalize outside the training space. Therefore, current eliminative connectionist models cannot account for those cognitive phenomena that involve universals that can be freely extended to arbitrary cases. Richard Evans and Edward Grefenstette's recent paper at DeepMind, building on Joel Grus's blog post on the game Fizz-Buzz, follows remarkably similar lines, concluding that a canonical multilayer network was unable to solve the simple game on its own "because it did not capture the general, universally quantified rules needed to understand this task"--exactly what I said in 1998.

Suzanne Gildert is a founder and CTO of Kindred AI – a company pursuing the modest vision of "building machines with human-like intelligence." Her startup just came out of stealth mode and I am both proud and humbled to say that this is the first ever long-form interview that Suzanne has done. Kindred AI has raised 15 million dollars from notable investors and currently employs 35 experts in their offices in Toronto, Vancouver, and San Francisco. Even better, Suzanne is a long-term Singularity.FM podcast fan, total tech geek, Gothic artist, Ph.D. in experimental physics and former D-Wave Quantum Computer maker. Right now I honestly can't think of a more interesting person to have a conversation with.

If your professional interests take you to the crossroads of financial services, regulation, compliance, and digital - especially data analytics and machine learning - which altogether is known as regtech, you are in the right place. You are part of statistically small and very geek-oriented professional community, but you know this, and though you might choose not to admit this to strangers at this year's festive parties for fear of causing great pain by boredom, you are in good company with this Contributor and my interviewee. I first met Jo Ann Barefoot when I was chairing the U.K. Financial Conduct Authority (FCA) Industry Sandbox Consultation, where she provided excellent guidance and insights. Jo Ann is one of the most dedicated and busiest advocates of the regtech space on the planet and is truly outstanding in both her knowledge and passion in this area. She dedicates her time to a number of global bodies and initiatives related to regtech: she is a Senior Fellow Emerita at the Harvard Kennedy School Center for Business & Government, a Senior Advisor to the Omidyar network, sits on the fintech advisory committee for FINRA, is an Executive Board Member of the International RegTech Association (IRTA), is a member of the Milken Institute U.S. FinTech Advisory Committee, and chairs the boards of the Center for Financial Services Innovation and FinRegLab.

David Byrne performs at the New Orleans Jazz and Heritage Festival in April.Amy Harris/Invision/AP Since the late-1970s, when David Byrne formed the iconic (and alas, now-defunct) Talking Heads, his career has been an endless stream of fascinating side projects, starting with his super-weird, super-cool Brian Eno collab, My Life in the Bush of Ghosts, and his scoring of choreographer Twyla Tharp's The Catherine Wheel. He founded his own World Music label, Luaka Bop, and wrote half a dozen books, including the best-selling quasi-memoir How Music Works. His obsession with the National Color Guard Championships led to a documentary called Contemporary Color. Most recently, his American Utopia tour featured dancers and musicians untethered from the standard concert setup by means of wireless and wearable instruments--nary an amp nor drumset in sight. In November, as the tour wrapped up, came the re-release of Byrne's 1986 film, True Stories, which explores the inner lives and outer quirks of residents of a fictional Texas town and is based on stories from tabloid newspapers.

Finding the perfect holiday gift can be maddening (is this the color they'd want? Is it something they already have? Is it so last year?), but really, once you have a sense of a person's taste, it's not impossible. This season, we'll be talking to members of various tribes to find out exactly what to get that college student, or serious home cook, or boss (who has everything) in your life. Think of it as a window into their brain trust--or, at least, a very helpful starting point.

Node embedding is the task of extracting informative and descriptive features over the nodes of a graph. The importance of node embeddings for graph analytics, as well as learning tasks such as node classification, link prediction and community detection, has led to increased interest on the problem leading to a number of recent advances. Much like PCA in the feature domain, node embedding is an inherently \emph{unsupervised} task; in lack of metadata used for validation, practical methods may require standardization and limiting the use of tunable hyperparameters. Finally, node embedding methods are faced with maintaining scalability in the face of large-scale real-world graphs of ever-increasing sizes. In the present work, we propose an adaptive node embedding framework that adjusts the embedding process to a given underlying graph, in a fully unsupervised manner. To achieve this, we adopt the notion of a tunable node similarity matrix that assigns weights on paths of different length. The design of the multilength similarities ensures that the resulting embeddings also inherit interpretable spectral properties. The proposed model is carefully studied, interpreted, and numerically evaluated using stochastic block models. Moreover, an algorithmic scheme is proposed for training the model parameters effieciently and in an unsupervised manner. We perform extensive node classification, link prediction, and clustering experiments on many real world graphs from various domains, and compare with state-of-the-art scalable and unsupervised node embedding alternatives. The proposed method enjoys superior performance in many cases, while also yielding interpretable information on the underlying structure of the graph.

On November 21, I read an interview with Yoshua Bengio in Technology Review that to a suprising degree downplayed recent successes in deep learning, emphasizing instead some other important problems in AI might require important extensions to what deep learning is currently able to do. I agreed with virtually every word and thought it was terrific that Bengio said so publicly. Instead I accidentally launched a Twitterstorm, at times illuminating, at times maddening, with some of the biggest folks in the field, including Bengio's fellow deep learning pioneer Yann LeCun and one of AI's deepest thinkers, Judea Pearl. Here's the tweet, perhaps forgotten in the storm that followed: For the record and for comparison, here's what I had said almost exactly six years earlier, on November 25, 2012, eerily similar, I stand by that -- which as far as I know (and I could be wrong) is the first place where anybody said that deep learning per se wouldn't be a panacea, and would instead need to work in a larger context to solve a certain class of problems. Bengio was pretty much saying the same thing.