Bradesco expects artificial intelligence will drive a significant increase in sales via digital channels.Bradesco Brazil's second-largest private bank Bradesco will ramp up its efforts around artificial intelligence (AI) to boost sales, improve customer experience and reduce operating costs in 2019. The bank, which has a portfolio of over 71 million customers, has been working on a platform dubbed Bradesco Artificial Intelligence (BIA) over the last four years. BIA's capabilities translate into an improved customer experience across the bank's digital channels - especially the app, which today accounts for 60% of customer interactions with the bank. Currently, 90% of the bank's services are already available via the app, but sales made via mobile currently represents about 20-30% of the overall business volume. We want to increase sales in that channel," says Mauricio Minas, executive vice president at the bank, adding that the goal is to increase mobile sales to 50% this year. "A few years ago we invested in the idea that BIA would be the engine of a substantial increase in Bradesco's customer value perception.

Hoobox Robotics, a robotics company based in São Paulo, Brazil, has developed the "Wheelie 7", a wheelchair controlled using facial recognition technology. Incorporating AI developed by Intel, the technology allows users to control the movements of a motorized wheelchair using just their faces. The technology is envisaged as being particularly helpful for users who cannot use their hands to control a motorized device. The tech consists of a 3D camera that records a user's facial expressions (no body sensors are required) and an on-board computer that interprets the expressions and sends commands to control the movement of the wheelchair. The company claims that their facial recognition system is so sensitive that it can differentiate ten different levels of pain, detect drowsiness, agitation, and sedation, and can even detect when a person will sneeze before the event occurs.

An appropriate choice of the activation function (like ReLU, sigmoid or swish) plays an important role in the performance of (deep) multilayer perceptrons (MLP) for classification and regression learning. Prototype-based classification learning methods like (generalized) learning vector quantization (GLVQ) are powerful alternatives. These models also deal with activation functions but here they are applied to the so-called classifier function instead. In this paper we investigate successful candidates of activation functions known for MLPs for application in GLVQ and their influence on the performance.

Bradesco expects artificial intelligence will drive a significant increase in sales via digital channels.Bradesco Brazil's second-largest private bank Bradesco will ramp up its efforts around artificial intelligence (AI) to boost sales, improve customer experience and reduce operating costs in 2019. The bank, which has a portfolio of over 71 million customers, has been working on a platform dubbed Bradesco Artificial Intelligence (BIA) over the last four years. BIA's capabilities translate into an improved customer experience across the bank's digital channels – especially the app, which today accounts for 60% of customer interactions with the bank. Currently, 90% of the bank's services are already available via the app, but sales made via mobile currently represents about 20-30% of the overall business volume. We want to increase sales in that channel," says Mauricio Minas, executive vice president at the bank, adding that the goal is to increase mobile sales to 50% this year. "A few years ago we invested in the idea that BIA would be the engine of a substantial increase in Bradesco's customer value perception.

Due to the escalating growth of big data sets in recent years, new Bayesian Markov chain Monte Carlo (MCMC) parallel computing methods have been developed. These methods partition large data sets by observations into subsets. However, for Bayesian nested hierarchical models, typically only a few parameters are common for the full data set, with most parameters being group-specific. Thus, parallel Bayesian MCMC methods that take into account the structure of the model and split the full data set by groups rather than by observations are a more natural approach for analysis. Here, we adapt and extend a recently introduced two-stage Bayesian hierarchical modeling approach, and we partition complete data sets by groups. In stage 1, the group-specific parameters are estimated independently in parallel. The stage 1 posteriors are used as proposal distributions in stage 2, where the target distribution is the full model. Using three-level and four-level models, we show in both simulation and real data studies that results of our method agree closely with the full data analysis, with greatly increased MCMC efficiency and greatly reduced computation times. The advantages of our method versus existing parallel MCMC computing methods are also described.

Netflix is raising its subscription prices for all 58 million of its US users, marking the first increase since 2017. It marks the biggest price increase for monthly subscriptions since launching its streaming service in 2007, and the first since 2017. All new Netflix subscribers will be subject to the increased prices, while existing subscribers will see their subscriptions go up over the next three months. Netflix has made a major effort in recent years to produce original content in order to keep subscribers loyal to its platform and not switch to rivals like Amazon Prime. A cartoon about a talking horse, starring the goofy older brother from Arrested Development… on paper little about BoJack Horseman screams "must watch". Yet the series almost immediately transcended its format to deliver a moving and very funny rumination on depression and middle-age malaise. Will Arnett plays BoJack – one time star of Nineties hit sitcom Horsin' Around – as a lost soul whose turbo-charged narcissism prevents him getting his life together. Almost as good are a support cast including Alison Brie (Glow, Mad Men), Aaron Paul, of Breaking Bad, and Amy Sedaris as a pampered Persian cat who is also BoJack's agent.

Statistical inference involves estimation of parameters of a model based on observations. Building on the recently proposed Equilibrium Expectation approach and Persistent Contrastive Divergence, we derive a simple and fast Markov chain Monte Carlo algorithm for maximum likelihood estimation (MLE) of parameters of exponential family distributions. The algorithm has good scaling properties and is suitable for Monte Carlo inference on large network data with billions of tie variables. The performance of the algorithm is demonstrated on Markov random fields, conditional random fields, exponential random graph models and Boltzmann machines.

Abstract--Previous attempts at music artist classification use frame-level audio features which summarize frequency content within short intervals of time. Comparatively, more recent music information retrieval tasks take advantage of temporal structure in audio spectrograms using deep convolutional and recurrent models. This paper revisits artist classification with this new framework and empirically explores the impacts of incorporating temporal structure in the feature representation. To this end, an established classification architecture, a Convolutional Recurrent Neural Network (CRNN), is applied to the artist20 music artist identification dataset under a comprehensive set of conditions. These include audio clip length, which is a novel contribution in this work, and previously identified considerations such as dataset split and feature-level. Our results improve upon baseline works, verify the influence of the production details on classification performance and demonstrate the tradeoffs between sample length and training set size. The best performing model achieves an average F1-score of 0.937 across three independent trials which is a substantial improvement over the corresponding baseline under similar conditions. Finally, to showcase the effectiveness ofthe CRNN's feature extraction capabilities, we visualize audio samples at its bottleneck layer demonstrating that learned representations segment into clusters belonging to their respective artists. I. INTRODUCTION Music information retrieval (MIR) encompasses most audio analysis tasks such as genre classification, song identification, chord recognition, sound event detection, mood detection and feature extraction [1], [2].

Continuous Speech Keyword Spotting (CSKS) is the problem of spotting keywords in recorded conversations, when a small number of instances of keywords are available in training data. Unlike the more common Keyword Spotting, where an algorithm needs to detect lone keywords or short phrases like "Alexa", "Cortana", "Hi Alexa!", "Whatsup Octavia?" etc. in speech, CSKS needs to filter out embedded words from a continuous flow of speech, ie. spot "Anna" and "github" in "I know a developer named Anna who can look into this github issue." Apart from the issue of limited training data availability, CSKS is an extremely imbalanced classification problem. We address the limitations of simple keyword spotting baselines for both aforementioned challenges by using a novel combination of loss functions (Prototypical networks' loss and metric loss) and transfer learning. Our method improves F1 score by over 10%.

GANs, or generative adversarial networks, are the social-media starlet of AI algorithms. They are responsible for creating the first AI painting ever sold at an art auction and for superimposing celebrity faces on the bodies of porn stars.