One of the most notable new features introduced alongside Amazon's new Echo Show is the ability to make calls with Alexa, something that's been rumored for a while now. And the good news is that this feature isn't limited to just the Echo Show. When it rolls out later today (via an update to the Alexa iOS and Android apps), you'll be able to use all existing Echo hardware to place voice calls, video calls and send messages. You'll obviously need an Echo Show for video calling, but the support for voice calling and messaging is pretty robust. If you have an Echo, Echo Dot or Echo Show, you can initiate calls (including video calls with the Show) and send messages to anyone else in your address book who has turned on Alexa calling.

THE traditional telecoms industry is going through massive disruption. Traditional revenue streams for communication service providers (CSPs) are on a decline, while stagnant regulations threaten market nimbleness. To make matters worse, global digital platforms - including Facebook, Google, Apple, and Amazon - are entering the core telecommunication market with innovative business models and technologies, leaving many CSPs to wonder if they can keep up or if they will be displaced. Digital natives are monetizing consumers' data and selling advertising space in order to provide services to users that are generally free. It is very difficult to compete with free, but it is possible to remain competitive despite this challenge.

In many real-world classification tasks such as churn prediction and fraud detection, we often encounter the class imbalance problem, which means one class is significantly outnumbered by the other class. The class imbalance problem brings great challenges to standard classification learning algorithms. Most of them tend to misclassify the minority instances more often than the majority instances on imbalanced data sets. For example, when a model is trained on a data set with 1% of instances from the minority class, a 99% accuracy rate can be achieved simply by classifying all instances as belonging to the majority class. Indeed, the problem of learning on imbalanced data sets is considered to be one of the ten challenging problems in data mining research.

Over the last couple of decades, those looking for a cluster management platform faced no shortage of choices. However, large-scale clusters are being asked to operate in different ways, namely by chewing on large-scale deep learning workloads--and this requires a specialized approach to get high utilization, efficiency, and performance. Nearly all of the cluster management tools from the high performance computing community are being bent in the machine learning direction, but for production deep learning shops, there appears to be a DIY tendency. This is not as complicated as it might sound, given the range of container-based open source tools, and such a homegrown approach can bake in tunings for specific frameworks and internal applications. The lack of a sufficiently robust cluster manager for a large-scale cluster handling large machine learning workloads pushed researchers at the Chinese machine learning giant, Sensetime, to build their own.

While the rest of the world will have to wait a little longer, Samsung yesterday rolled out voice functionality for its new assistant Bixby to Galaxy S8 owners in South Korea – and it turns out the little virtual helper has some mad skills to show off. Though Bixby was mostly designed to perform simple voice commands like making calls, setting reminders and calendar appointments as well as launching apps, it turns out the assistant can also spit some fire raps for you. We've teamed up with Product Hunt to offer you the chance to win an all expense paid trip to TNW Conference 2017! South Korean residents are flocking to YouTube to upload videos showing Bixby rhyme over various beats – both old and new school – and it sounds surprisingly good, even though my Korean is practically non-existent. As you can expect from any voice-enabled virtual assistant, the lyrics hardly qualify as what you'd call quality hip-hop, but it's still entertaining to see your phone trying to rap.

Description:We are currently going through an evolution with how we interact with technology through the use of voice interfaces. In this talk, we'll look at how the Alexa Skills Kit and Amazon Lex can be used to define a voice interface, how these interfaces work and how they can be used to allow people to work with your applications in unique ways.

This work studies the entity-wise topical behavior from massive network logs. Both the temporal and the spatial relationships of the behavior are explored with the learning architectures combing the recurrent neural network (RNN) and the convolutional neural network (CNN). To make the behavioral data appropriate for the spatial learning in CNN, several reduction steps are taken to form the topical metrics and place them homogeneously like pixels in the images. The experimental result shows both the temporal- and the spatial- gains when compared to a multilayer perceptron (MLP) network. A new learning framework called spatially connected convolutional networks (SCCN) is introduced to more efficiently predict the behavior.

The Galaxy S8 is shipping all over the world, people are raving about it, and it's breaking all sorts of sales records. Samsung's AI assistant Bixby wasn't quite ready to launch when the Galaxy S8 did, so Samsung held back its biggest feature, voice commands, with a vague promise that it will launch "later this spring." But we could be getting to know Bixby sooner than we think. Samsung has launched the voice component of Bixby in its home country of South Korea, a clear signal that the company is hard at work at perfecting its voice recognition engine. The plan has always been for Korean language support to launch first followed by English.

We investigate the 3-architecture Connected Facility Location Problem arising in the design of urban telecommunication access networks integrating wired and wireless technologies. We propose an original optimization model for the problem that includes additional variables and constraints to take into account wireless signal coverage represented through signal-to-interference ratios. Since the problem can prove very challenging even for modern state-of-the art optimization solvers, we propose to solve it by an original primal heuristic that combines a probabilistic fixing procedure, guided by peculiar Linear Programming relaxations, with an exact MIP heuristic, based on a very large neighborhood search. Computational experiments on a set of realistic instances show that our heuristic can find solutions associated with much lower optimality gaps than a state-of-the-art solver. Keywords: Telecommunications, FTTX Access Networks, Connected Facility Location, Mixed Integer Linear Programming, Tight Linear Relaxations, MIP Heuristics.