On May 10, the world-class admin behind John Chambers's success was honored with one of the top awards in her field: Debbie Gross received the Colleen Barrett Award for Administrative Excellence. Clearly, Debbie is a force. This CNBC story gives us a peek into her life keeping John at the top of his game. People are rightly saying she's a role model for next-generation administrators. But what people aren't saying is that some next-gen admins are made not of flesh and blood like Debbie but of compute cycles.

May 27--As technologists race to invent the next big thing after the smartphone, many have overlooked what is starting to seem obvious: That the human voice is a powerful, perhaps the most powerful, mechanism for controlling the world around us. And, more importantly, speaking is a behavior that doesn't require a user manual. Now, Amazon, Google and Apple are speeding toward a not-too distant future when your voice will dictate commands to a personal assistant, powered by artificial intelligence, and ultimately supplant your PC and phone for most quotidian computing purposes. In other words, your voice -- not a screen -- will become the primary interface for accessing the Internet, and even controlling your home or car. "The entire Silicon Valley startup community was more enamored over virtual reality and augmented reality, and in the process completely overlooked the transformative nature of voice-first technology," said Brian Roemmele, a Temecula-based researcher and consultant in voice-based technologies and online commerce.

Would you share your home with a robot or work side by side with one? People are starting to do both, which has put the relationship we have with them under the spotlight and exposed both our love and fear of the machines that are increasingly becoming a crucial part of our lives. In Japan they grow so attached to their robot dogs that they hold funerals for them when they "die". Sony, the firm that began making the popular Aibo toys in 1999, decided to stop offering repairs in 2014, meaning once they broke down they were fit only for the scrapheap. But people weren't willing to throw them in the rubbish bin, wanting instead to say goodbye to them in the same way you would to a human or pet.

"I am six feet tall and my hazel eyes reflect my olive skin... I seek to connect with women who are romantics at heart… that are open to the possibility of true love". These are lines from the online dating profile of Robert Torres - a man who is serving four concurrent life sentences for aggravated sexual assault, including the rape of Texas nurse Lori Williams at knifepoint 20 years ago, while her two daughters slept in a room nearby. His other victims included a 63-year-old woman and her 16-year-old granddaughter. The advert contains no mention of any of these crimes.

LOS ANGELES - This week we dived into everything from the potential dark side of artificial intelligence, to making sense of Twitter's soon-to-change 140 character limits on the #TalkingTech podcast. I ranted about Uber's ever-increasing surge pricing, and offered recommendations on how to deal with them, and talked to folks from Dell Computer about how Sony used 40 of their super-PCs to animate scenes in the new Angry Birds movie. These powerful machines could render a 30 second scene in an hour that would take 18 years on a standard computer, Dell insisted. For our longform fans, our Weekend Listen collection starts with the popularity of the connected speaker, like Amazon's Echo and coming soon, Google Home. How do we feel about having our kitchen conversations monitored?

Five days from now Google will publish open-source tools that will focus its machine-learning engine on music and art. But one London startup, named Jukedeck, has been working on getting machines to automatically generate original music for years. You can even generate your own ditty, composed by artificial intelligence, right now on Jukedeck's website. Jukedeck lets anyone use its machine-learning engine to generate tunes hosted on its website. The engine then produces an original piece of music.

Though research is a slow moving and rigid process, one study shows that the rate of scientific study has exploded in the last 50 years. According to the paper, humanity's scientific output now doubles every nine years. In specific areas like healthcare, the doubling rate is even faster -- as much as every 3 years currently with an expected increase to every 73 days by the early 2020s. For overwhelmed researchers navigating the growing stack of science literature -- the value isn't in having so much new information, but finding relevant insights when they need them. According to Jacobo Elosua, a co-founder of Iris AI -- a Singularity University portfolio company -- the research process is very often tedious and unfruitful.

We present a framework for accelerating a spectrum of machine learning algorithms that require computation of bilinear inverse forms $u^\top A^{-1}u$, where $A$ is a positive definite matrix and $u$ a given vector. Our framework is built on Gauss-type quadrature and easily scales to large, sparse matrices. Further, it allows retrospective computation of lower and upper bounds on $u^\top A^{-1}u$, which in turn accelerates several algorithms. We prove that these bounds tighten iteratively and converge at a linear (geometric) rate. To our knowledge, ours is the first work to demonstrate these key properties of Gauss-type quadrature, which is a classical and deeply studied topic. We illustrate empirical consequences of our results by using quadrature to accelerate machine learning tasks involving determinantal point processes and submodular optimization, and observe tremendous speedups in several instances.

Given two sets of variables, derived from a common set of samples, sparse Canonical Correlation Analysis (CCA) seeks linear combinations of a small number of variables in each set, such that the induced canonical variables are maximally correlated. Sparse CCA is NP-hard. We propose a novel combinatorial algorithm for sparse diagonal CCA, i.e., sparse CCA under the additional assumption that variables within each set are standardized and uncorrelated. Our algorithm operates on a low rank approximation of the input data and its computational complexity scales linearly with the number of input variables. It is simple to implement, and parallelizable. In contrast to most existing approaches, our algorithm administers precise control on the sparsity of the extracted canonical vectors, and comes with theoretical data-dependent global approximation guarantees, that hinge on the spectrum of the input data. Finally, it can be straightforwardly adapted to other constrained variants of CCA enforcing structure beyond sparsity. We empirically evaluate the proposed scheme and apply it on a real neuroimaging dataset to investigate associations between brain activity and behavior measurements.

Understanding how features interact with each other is of paramount importance in many scientific discoveries and contemporary applications. Yet interaction identification becomes challenging even for a moderate number of covariates. In this paper, we suggest an efficient and flexible procedure, called the interaction pursuit (IP), for interaction identification in ultra-high dimensions. The suggested method first reduces the number of interactions and main effects to a moderate scale by a new feature screening approach, and then selects important interactions and main effects in the reduced feature space using regularization methods. Compared to existing approaches, our method screens interactions separately from main effects and thus can be more effective in interaction screening. Under a fairly general framework, we establish that for both interactions and main effects, the method enjoys the sure screening property in screening and oracle inequalities in selection. Our method and theoretical results are supported by several simulation and real data examples.