In this episode of WRLWND Radio, Marcello Sukhdeo talks about the rapid growth of voice-powered apps on Amazon's Alexa voice platform, also, a study that reveals that extreme Internet usage can lead to mental illness and he touches on the growth of video-gamers worldwide. Amazon's Alexa voice platform has now passed 15,000 skills, these are the voice-powered apps. This is up from last September when it was around 10,000. Today, Amazon is currently the leader in voice-powered devices and it is expected to control 70 percent of the market this year -- well ahead of Google Home, Lenovo, LG and others. Due to the increased interest in voice-powered devices, Apple has joined the party with the with its own Siri-powered device, the HomePod which will be released this December.

A Master Algorithm would allow machines to learn anything from data, and has applications in fields from marketing to medicine. But to do that we need a deeper understanding of learning in our own brains. This helmet of sensors is part of a brain scanner. We may not be aware of it, but machine learning is already an integral part of our daily lives, from the product choices that Amazon offers us to the surveillance of our data by the National Security Agency. Few of us understand it or the implications, however.

Machine Learning (ML) is closely related to computational statistics which focuses on prediction-making through the use of computers. ML is a modern approach to an old problem: predictive inference. It makes an inference from "feature" space to "outcome/target" space. In order to work properly, an ML algorithm has to discover and model hidden relationships between the feature space and the outcome space and create links between the two. Doing so requires overcoming barriers such as feature noise (randomness of features due to unexplained mechanisms). In this article we argue that "Emergence" is also a barrier for predictive inference.

Video games have evolved from a childhood activity to a pastime for adults. These games are becoming more common and enjoyed in old age, outselling music and film. Now, a studypublished in Frontiers in Human Neuroscience found playing video games can shape the brain -- for better or for worse. "We focused on how the brain reacts to video game exposure, but these effects do not always translate to real-life changes," said Marc Palaus, first author of the study, in a statement. Researchers from Open University of Catalonia in Spain and Massachusetts General Hospital noted gamers experience a change in brain activity, and even structure.

Meet MiRo, a biomimetic robot that's on a mission to make friends. Developed by experts at the University of Sheffield, it's intended to be used in various environments, but particularly as a companion, perhaps for the elderly or maybe for people with disabilities. It's creators, Consequential Robotics, believe that in the future social robots will share our personal space, constantly interacting with us and providing emotional engagement. With that in mind, they might as well look cute too. MiRo is claimed to be the world's first commercial biomimetic robot.

Unlike previous revolutions in industrialisation, artificial intelligence will disrupt jobs higher up the value chain, David Young, the former secretary state of employment, has warned. Access this e-guide with 6 key articles on cloud security and learn how to protect your organisation and its data on the cloud. You forgot to provide an Email Address. This email address doesn't appear to be valid. This email address is already registered.

The Ornstein-Uhlenbeck, also called mean-reverting diffusion process, describes a process which evolves following a deterministic linear part with an added Gaussian noise, similarly to a vectorautoregressive process in discrete time. This model is ubiquitous in quantitative finance, for instance the one-dimensional version is used for modeling rates and is called the Vasicek model [Hul09]. In a multidimensional setting, it can be therefore used to describe systems with linear interactions perturbed by Gaussian noise, see Figure 1 below. Among many others, an example of application is inter-bank lending [CFS15, FI13], where lending is a flux of reserves and is proportional to the difference in reserves. A natural question is therefore how to estimate the interaction structure from the observation of the process. Unfortunately, the optimal solution based on the maximum likelihood estimator (MLE) is typically quite inaccurate in high-dimensional settings, because of the well-known curse of dimensionality, see for instance [BvdG11]. However, in real-world applications, the interaction structure is sparse: in the example mentioned above, banks have typically only a few lending partners [GG14, GSV15, BBvL15], as the lending arrangements are typically done on a personal level.

We develop a model in which interactions between nodes of a dynamic network are counted by non homogeneous Poisson processes. In a block modelling perspective, nodes belong to hidden clusters (whose number is unknown) and the intensity functions of the counting processes only depend on the clusters of nodes. In order to make inference tractable we move to discrete time by partitioning the entire time horizon in which interactions are observed in fixed-length time sub-intervals. First, we derive an exact integrated classification likelihood criterion and maximize it relying on a greedy search approach. This allows to estimate the memberships to clusters and the number of clusters simultaneously. Then a maximum-likelihood estimator is developed to estimate non parametrically the integrated intensities. We discuss the over-fitting problems of the model and propose a regularized version solving these issues. Experiments on real and simulated data are carried out in order to assess the proposed methodology.

Leasing is a popular channel to market new cars. Pricing a leasing contract is complicated because the leasing rate embodies an expectation of the residual value of the car after contract expiration. To aid lessors in their pricing decisions, the paper develops resale price forecasting models. A peculiarity of the leasing business is that forecast errors entail different costs. Identifying effective ways to address this characteristic is the main objective of the paper. More specifically, the paper contributes to the literature through i) consolidating and integrating previous work in forecasting with asymmetric cost of error functions, ii) systematically evaluating previous approaches and comparing them to a new approach, and iii) demonstrating that forecasting with asymmetric cost of error functions enhances the quality of decision support in car leasing. For example, under the assumption that the costs of overestimating resale prices is twice that of the opposite error, incorporating corresponding cost asymmetry into forecast model development reduces decision costs by about eight percent, compared to a standard forecasting model. Higher asymmetry produces even larger improvements.

We study in this paper the consequences of using the Mean Absolute Percentage Error (MAPE) as a measure of quality for regression models. We prove the existence of an optimal MAPE model and we show the universal consistency of Empirical Risk Minimization based on the MAPE. We also show that finding the best model under the MAPE is equivalent to doing weighted Mean Absolute Error (MAE) regression, and we apply this weighting strategy to kernel regression. The behavior of the MAPE kernel regression is illustrated on simulated data.