Advances in technology continue to change the workplace. Forty years ago, the first cell phone was introduced, enabling a virtual working environment. In 1989, the World Wide Web surfaced, soon followed by Google and other search engines. And more than a decade ago, Facebook launched, connecting users from around the globe in a digital community with a level of detail and dialogue that hadn't been seen before. Today's data-rich work environment continues to change, and successful business leaders understand the role that technology will play in simplifying business processes and increasing employee productivity.

With machines running up a series of victories against their human competitors, it almost seems like they're capable of beating humans in every aspect of life. But could they trounce us even when it comes to emotions? A special piano competition between man and machine took place Monday at Seongnam Arts Center. The performers were Italian pianist Roberto Prosseda, and a robot-pianist called Teo Tronico. Both played the same piece of music in their own style, and then assessed each other in the form of a talk show.

My name is Jonathan Armoza and I am a data science intern at Neustar and a PhD candidate in English Literature at New York University. My work focuses on the development of computational text mining and visualization methods in the emerging field of digital humanities. The era of big data has created the need to develop techniques and mechanisms to not only handle large datasets, but to understand them as well. Much of this influx of information is about people, places, and things. Although some of that data is anonymized, there are a number of reasons we might want to understand how to associate those real world "entities" with their data points.

The global population is expected to reach 9 billion people by 2050, which means double agricultural production in order to meet food demands. Farm enterprises require new and innovative technologies to face and overcome these challenges. Artificial intelligence robotics is one of these technologies that promises to provide a solution. An increasing number of farmbots are being developed that are capable of complex tasks that have not been possible with the large-scale agricultural machinery in the past. Here's a list of real use cases of robots that will help agriculture changing.

We utilize copulas to constitute a unified framework for constructing and optimizing variational proposals in hierarchical Bayesian models. For models with continuous and non-Gaussian hidden variables, we propose a semiparametric and automated variational Gaussian copula approach, in which the parametric Gaussian copula family is able to preserve multivariate posterior dependence, and the nonparametric transformations based on Bernstein polynomials provide ample flexibility in characterizing the univariate marginal posteriors.

In this paper we propose a new identification scheme for Hammerstein systems, which are dynamic systems consisting of a static nonlinearity and a linear time-invariant dynamic system in cascade. We assume that the nonlinear function can be described as a linear combination of $p$ basis functions. We reconstruct the $p$ coefficients of the nonlinearity together with the first $n$ samples of the impulse response of the linear system by estimating an $np$-dimensional overparameterized vector, which contains all the combinations of the unknown variables. To avoid high variance in these estimates, we adopt a regularized kernel-based approach and, in particular, we introduce a new kernel tailored for Hammerstein system identification. We show that the resulting scheme provides an estimate of the overparameterized vector that can be uniquely decomposed as the combination of an impulse response and $p$ coefficients of the static nonlinearity. We also show, through several numerical experiments, that the proposed method compares very favorably with two standard methods for Hammerstein system identification.

We present some arguments why existing methods for representing agents fall short in applications crucial to artificial life. Using a thought experiment involving a fictitious dynamical systems model of the biosphere we argue that the metabolism, motility, and the concept of counterfactual variation should be compatible with any agent representation in dynamical systems. We then propose an information-theoretic notion of \emph{integrated spatiotemporal patterns} which we believe can serve as the basic building block of an agent definition. We argue that these patterns are capable of solving the problems mentioned before. We also test this in some preliminary experiments.

Exponential-family harmoniums (EFHs), which extend restricted Boltzmann machines (RBMs) from Bernoulli random variables to other exponential families (Welling et al., 2005), are generative models that can be trained with unsupervised-learning techniques, like contrastive divergence (Hinton et al., 2006; Hinton, 2002), as density estimators for static data. Methods for extending RBMs--and likewise EFHs--to data with temporal dependencies have been proposed previously (Sutskever and Hinton, 2007; Sutskever et al., 2009), the learning procedure being validated by qualitative assessment of the generative model. Here we propose and justify, from a very different perspective, an alternative training procedure, proving sufficient conditions for optimal inference under that procedure. The resulting algorithm can be learned with only forward passes through the data--backprop-through-time is not required, as in previous approaches. The proof exploits a recent result about information retention in density estimators (Makin and Sabes, 2015), and applies it to a "recurrent EFH" (rEFH) by induction. Finally, we demonstrate optimality by simulation, testing the rEFH: (1) as a filter on training data generated with a linear dynamical system, the position of which is noisily reported by a population of "neurons" with Poisson-distributed spike counts; and (2) with the qualitative experiments proposed by Sutskever et al. (2009).

We study the problem of estimating the parameters of a regression model from a set of observations, each consisting of a response and a predictor. The response is assumed to be related to the predictor via a regression model of unknown parameters. Often, in such models the parameters to be estimated are assumed to be constant. Here we consider the more general scenario where the parameters are allowed to evolve over time, a more natural assumption for many applications. We model these dynamics via a linear update equation with additive noise that is often used in a wide range of engineering applications, particularly in the well-known and widely used Kalman filter (where the system state it seeks to estimate maps to the parameter values here). We derive an approximate algorithm to estimate both the mean and the variance of the parameter estimates in an online fashion for a generic regression model. This algorithm turns out to be equivalent to the extended Kalman filter. We specialize our algorithm to the multivariate exponential family distribution to obtain a generalization of the generalized linear model (GLM). Because the common regression models encountered in practice such as logistic, exponential and multinomial all have observations modeled through an exponential family distribution, our results are used to easily obtain algorithms for online mean and variance parameter estimation for all these regression models in the context of time-dependent parameters. Lastly, we propose to use these algorithms in the contextual multi-armed bandit scenario, where so far model parameters are assumed static and observations univariate and Gaussian or Bernoulli. Both of these restrictions can be relaxed using the algorithms described here, which we combine with Thompson sampling to show the resulting performance on a simulation.

To many, that might seem a frightening idea, even at a time when a few dozen of Google's driverless cars are cruising city streets in California, Texas, Washington and Arizona. But Anthony Levandowski, a robot-loving engineer who helped steer Google's self-driving technology, is convinced autonomous big rigs will be the next big thing on the road to a safer transportation system. Levandowski left Google earlier this year to pursue his vision at Otto, a San Francisco startup he co-founded with two other former Google employees, Lior Ron and Don Burnette, and another robotics expert, Claire Delaunay. Otto is aiming to equip trucks with software, sensors, lasers and cameras so they eventually will be able to navigate the more than 220,000 miles of U.S. highways on their own, while a human driver naps in the back of the cab or handles other tasks. For now, the robot truckers would only take control on the highways, leaving humans to handle the tougher task of wending through city streets.