Microsoft acquires AI-powered scheduling service Genee, to be shut down and merged with Office ... 'Software is eating the world': How robots, drones and artificial intelligence will change everything

To construct flexible nonlinear predictive distributions, the paper introduces a family of softplus function based regression models that convolve, stack, or combine both operations by convolving countably infinite stacked gamma distributions, whose scales depend on the covariates. Generalizing logistic regression that uses a single hyperplane to partition the covariate space into two halves, softplus regressions employ multiple hyperplanes to construct a confined space, related to a single convex polytope defined by the intersection of multiple half-spaces or a union of multiple convex polytopes, to separate one class from the other. The gamma process is introduced to support the convolution of countably infinite (stacked) covariate-dependent gamma distributions. For Bayesian inference, Gibbs sampling derived via novel data augmentation and marginalization techniques is used to deconvolve and/or demix the highly complex nonlinear predictive distribution. Example results demonstrate that softplus regressions provide flexible nonlinear decision boundaries, achieving classification accuracies comparable to that of kernel support vector machine while requiring significant less computation for out-of-sample prediction.

We develop a generic Gauss-Newton (GN) framework for solving a class of nonconvex optimization problems involving low-rank matrix variables. As opposed to standard Gauss-Newton method, our framework allows one to handle general smooth convex cost function via its surrogate. The main complexity-per-iteration consists of the inverse of two rank-size matrices and at most six small matrix multiplications to compute a closed form Gauss-Newton direction, and a backtracking linesearch. We show, under mild conditions, that the proposed algorithm globally and locally converges to a stationary point of the original nonconvex problem. We also show empirically that the Gauss-Newton algorithm achieves much higher accurate solutions compared to the well studied alternating direction method (ADM). Then, we specify our Gauss-Newton framework to handle the symmetric case and prove its convergence, where ADM is not applicable without lifting variables. Next, we incorporate our Gauss-Newton scheme into the alternating direction method of multipliers (ADMM) to design a GN-ADMM algorithm for solving the low-rank optimization problem. We prove that, under mild conditions and a proper choice of the penalty parameter, our GN-ADMM globally converges to a stationary point of the original problem. Finally, we apply our algorithms to solve several problems in practice such as low-rank approximation, matrix completion, robust low-rank matrix recovery, and matrix recovery in quantum tomography. The numerical experiments provide encouraging results to motivate the use of nonconvex optimization.

Perceptron is a classic online algorithm for learning a classification function. In this paper, we provide a novel extension of the perceptron algorithm to the learning to rank problem in information retrieval. We consider popular listwise performance measures such as Normalized Discounted Cumulative Gain (NDCG) and Average Precision (AP). A modern perspective on perceptron for classification is that it is simply an instance of online gradient descent (OGD), during mistake rounds, using the hinge loss function. Motivated by this interpretation, we propose a novel family of listwise, large margin ranking surrogates. Members of this family can be thought of as analogs of the hinge loss. Exploiting a certain self-bounding property of the proposed family, we provide a guarantee on the cumulative NDCG (or AP) induced loss incurred by our perceptron-like algorithm. We show that, if there exists a perfect oracle ranker which can correctly rank each instance in an online sequence of ranking data, with some margin, the cumulative loss of perceptron algorithm on that sequence is bounded by a constant, irrespective of the length of the sequence. This result is reminiscent of Novikoff's convergence theorem for the classification perceptron. Moreover, we prove a lower bound on the cumulative loss achievable by any deterministic algorithm, under the assumption of existence of perfect oracle ranker. The lower bound shows that our perceptron bound is not tight, and we propose another, \emph{purely online}, algorithm which achieves the lower bound. We provide empirical results on simulated and large commercial datasets to corroborate our theoretical results.

With the use of education chatbots, Prepathon CEO Allwin Agnel explained that the artificial intelligence-driven education technology bots are able to execute precise and detailed tasks that can improve or alter educational experiences by facilitating personalized learning. As the equity gap in American education continues, Microsoft co-founder Bill Gates has been urging educators, investors and tech companies to be more open in investing time and money in artificial intelligence-driven education technology programs. Gates believed that these AI-based EdTech platforms could personalize and revolutionize school learning experience while eliminating the equity gap. With that said, Gates is reportedly excited about the evolving field of personalized learning and artificial intelligence tutor bots. According to Venture Beat, the world's richest man will also like the Mumbai-based company called Prepathon as it opted to create bots with specialized single concentration and purpose.

Logistical problems in identifying impoverished communities may become relics of the past, as researchers are now combining satellite data with advanced computer algorithms to bypass traditional hurdles. In a study published Friday in the journal Science, Stanford University researchers proposed a way to use machine learning -- the science of designing computer algorithms that learn from data -- to interpret data acquired from high-resolution satellite imagery. The availability of accurate and reliable information on the location of impoverished zones is sorely lacking, which forces aid groups and other international organizations to conduct door-to-door surveys to supplement existing data -- an expensive and time-consuming process. Using earlier machine-learning methods, the team found pockets of poverty across five African nations which have previously been void of valuable survey information. "We have a limited number of surveys conducted in scattered villages across the African continent, but otherwise we have very little local-level information on poverty," said study co-author Marshall Burke.

I'd love to talk more about Flowcast, but I'm still not able to shake the image of you making a robotic submarine run by San Diego poolside (laughs). As a STEM enthusiast, I have been in awe of IBM Watson's capabilities. And I feel it's an honor to be talking to someone who has contributed to its capabilities. Now, let's come back to Flowcast. Can you share more information and shed more light on how Flowcast came about?

The winners of the Hugo Awards were announced at a gala ceremony in Kansas City, Mo., on Saturday, marking a good night for women and authors of color, and a very bad one for the "Puppies." Writers N.K. Jemisin and Nnedi Okorafor, both of whom are African American women, won the novel and novella awards, respectively. It was a defeat for the groups the Sad Puppies and the Rabid Puppies, who for two years have semi-successfully gamed the nominations for the Hugos -- which along with the Nebula Awards are generally considered the preeminent awards in science fiction and fantasy -- in an attempt to advance their anti-diversity agendas. Jemisin, who won for her novel "The Fifth Season," referenced the Puppies in her acceptance speech, io9 reports. "Only a small number of ideologues have attempted to game the Hugo Awards," Jemisin said.

Tuning inside the brain is the difference between normal and super smart people, researchers have found. They say general cognitive ability may be the result of a'well-tuned brain network' - and may even be able to develop to tune up the mind of those less intelligent. They found the brains of those with higher intelligence were extremely similar at rest and while carrying out tasks. Researchers say general cognitive ability may be the result of a'well-tuned brain network' - and say they may even be able to develop a way to tune up the mind of those less intelligent. 'Specifically, we found that brain network configuration at rest was already closer to a wide variety of task configurations in intelligent individuals,' the Rutgers University team wrote in The Journal of Neuroscience.

Computers can be trained to be more accurate than pathologists in assessing slides of lung cancer tissues, according to a new study by researchers at the Stanford University School of Medicine. The researchers found that a machine-learning approach to identifying critical disease-related features accurately differentiated between two types of lung cancers and predicted patient survival times better than the standard approach of pathologists classifying tumors by grade and stage. "Pathology as it is practiced now is very subjective," said Michael Snyder, PhD, professor and chair of genetics. "Two highly skilled pathologists assessing the same slide will agree only about 60 percent of the time. This approach replaces this subjectivity with sophisticated, quantitative measurements that we feel are likely to improve patient outcomes."