The underlying factors affecting users' choices of what to watch on TV have for several years been of interest to commercial and academic research. In the midst of a rapidly changing device and multimedia landscape, TVs continue to be at the core of multimedia consumption in the home with scenarios covering, among others, social gatherings and solitary immersive moments. The inherent complexity of viewing situations challenges the creation of experiences that match personal preferences as well as temporal and social contexts. Due to the increased availability of multimedia, research has been focused on improving the users' decision process by reducing large catalogs of content to a few personalized suggestions [1]. Commercial recommender solutions are now considered core to the business of engaging users and thereby preventing abandonment [2]. To do so, recommender systems have explored various features for personalization, such as history of watching, ratings, user/item similarity, and time of the day, the last of which is an example of features characteristic to context-aware recommender systems (CARS) [3]. The main objective of a recommender system is to personalize the experience to the individual, often by studying the user-item matrix. This could be an issue, since an account on a TV is often shared by multiple members of a household that end up diluting the user profile.

Abstraction plays a key role in concept learning and knowledge discovery. While pervasive in both human and artificial intelligence, it remains mysterious how concepts are abstracted in the first place. We study the nature of abstraction through a group-theoretic approach, formalizing it as a hierarchical, interpretable, and task-free clustering problem. This clustering framework is data-free, feature-free, similarity-free, and globally hierarchical---the four key features that distinguish it from common clustering models. Beyond a theoretical foundation for abstraction, we also present a top-down and a bottom-up approach to establish an algorithmic foundation for practical abstraction-generating methods. Lastly, using both a theoretical explanation and a real-world application, we show that the coupling of our abstraction framework with statistics realizes Shannon's information lattice and even further, brings learning into the picture. This gives a first step towards a principled and cognitive way of automatic concept learning and knowledge discovery.

In this article we propose a new supervised ensemble learning method called Data Shared Adaptive Bootstrap Aggregated (AdaBag) Lasso for capturing low dimensional useful features for word based sentiment analysis and mining problems. The literature on ensemble methods is very rich in both statistics and machine learning. The algorithm is a substantial upgrade of the Data Shared Lasso uplift algorithm. The most significant conceptual addition to the existing literature lies in the final selection of bag of predictors through a special bootstrap aggregation scheme. We apply the algorithm to one simulated data and perform dimension reduction in grouped IMDb data (drama, comedy and horror) to extract reduced set of word features for predicting sentiment ratings of movie reviews demonstrating different aspects. We also compare the performance of the present method with the classical Principal Components with associated Linear Discrimination (PCA-LD) as baseline. There are few limitations in the algorithm. Firstly, the algorithm workflow does not incorporate online sequential data acquisition and it does not use sentence based models which are common in ANN algorithms . Our results produce slightly higher error rate compare to the reported state-of-the-art as a consequence.

USA TODAY's Jefferson Graham takes a look (and listen) to the new Sonos Beam which incorporates Amazon's Alexa.voice They make the best consumer wifi speakers, and their flavor of Alexa, the Sonos One connected speaker, is my favorite choice for anyone looking to bring Alexa and music into the home. So I was itching to test the Sonos Beam, the new soundbar for TV viewing with built-in Alexa. Now that I've had it for a few days, my verdict--well, it's not the same as for the Sonos One, that's for sure. The good news with the Beam is that the sound, as always, is great and it mixes nicely with other Sonos speakers.

Take the example of a video streaming service like Hulu or Netflix. In order to make recommendations for users, engineers need to decide which videos to recommend based on input from users.

Face recognition technology sold by Amazon incorrectly matched 28 politicians with people arrested for a crime, an investigation by the American Civil Liberties Union has found. The findings raise further concerns about the use of similar technology by police departments in the US and beyond. Amazon's Rekognition is an image analysis service that offers the ability to automatically comb huge quantities of still pictures and video. It can detect objects and sensitive content, extract on-screen text, and compare faces to a reference image.

Follow us on Twitter: https://twitter.com/AskTeluguBadi Bill Gates Biography in Telugu: https://youtu.be/gSqLA1EDusI Jack ma Biography in Telugu: https://youtu.be/lW56XrvKjz8 Elon Musk Biography in Telugu: https://youtu.be/-5tAtqZUJ40 Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans.

ABSTRACT This proposal introduces a Dialogue Challenge for building end-to-end task-completion dialogue systems, with the goal of encouraging the dialogue research community to collaborate and benchmark on standard datasets and unified experimental environment. In this special session, we will release humanannotated conversational data in three domains (movie-ticket booking, restaurant reservation, and taxi booking), as well as an experiment platform with built-in simulators in each domain, for training and evaluation purposes. The final submitted systems will be evaluated both in simulated setting and by human judges. Index Terms-- dialogue challenge, end-to-end taskcompletion dialogue 1. INTRODUCTION There are many virtual assistants commercially available today, such as Apple's Siri, Google's Home, Microsoft's Cortana, and Amazon's Echo. With a well-designed dialogue system as an intelligent assistant, people can accomplish tasks easily via natural language interactions.

Research on automatic music generation has seen great progress due to the development of deep neural networks. However, the generation of multi-instrument music of arbitrary genres still remains a challenge. Existing research either works on lead sheets or multi-track piano-rolls found in MIDIs, but both musical notations have their limits. In this work, we propose a new task called lead sheet arrangement to avoid such limits. A new recurrent convolutional generative model for the task is proposed, along with three new symbolic-domain harmonic features to facilitate learning from unpaired lead sheets and MIDIs. Our model can generate lead sheets and their arrangements of eight-bar long. Audio samples of the generated result can be found at https://drive.google.com/open?id=1c0FfODTpudmLvuKBbc23VBCgQizY6-Rk

I came to thinking about another benefit for ReLU, which deals with the "thresholding" behavior of the ReLU. My interpretation has to do with the dot product in convolutional neural networks. As everyone knows, when the kernel is slid over its input window, it computes the dot product as with vectors. The process of convolution can be interpreted as looking for a specific pattern throughout an image. Now, the only thing that affects the dot product's sign is the angle between the two vectors--in this case, the kernel and its window.