Claire Denis is a filmmaker's filmmaker. Though the French writer-director has never had a commercial breakthrough in the U.S., she has been a steady presence in international cinema circles from her debut feature "Chocolat" in 1988 through such titles as 1999's "Beau Travail," 2010's "White Material," starring Isabelle Huppert, and "Let the Sunshine In," which starred Juliette Binoche and was released in the U.S. last year. In part, Denis is so well-regarded because she remains so unpredictable. There is no signature style to her work and it remains surprising with each and every film. Her latest, "High Life," which opens in New York and Los Angeles this week via A24, arrives with higher than usual commercial expectations.

Jefferson Graham takes a look at two products that bring Alexa and voice-activated controls to the auto, Garmin Speak and Roav Viv. LOS ANGELES -- "Alexa, what music should I listen to?" It's an interesting question that follows up with Amazon's Alexa asking follow-ups, one of the few times the personal assistant gets really conversational and makes suggestions. Alexa, via Amazon Music, gave me options: the late, legendary gypsy guitarist Django Reinhardt or singers Adele or James Taylor. I went for Django, and with that, my personal DJ robot had followed through successfully, finding three artists I love. Such it is with music commands for personal assistants.

PewDiePie has conceded his hard-fought battle with T-Series with a music video of his own. The YouTube star whose real name is Felix Kjellberg has finally stopped being the site's biggest channel – a slot he held for years – after a challenge from T-Series, which hosts Indian music videos and has grown quickly. Over recent months, the two channels have fought to get control of the top spot, with PewDiePie's fans using a whole host of publicity techniques in an attempt to keep him as its most popular channel. We'll tell you what's true. You can form your own view. But T-Series kept outpacing its growth and has now settled as YouTube's biggest channel.

This paper presents a study on discriminative artificial neural network classifiers in the context of open-set speaker identification. Both 2-class and multi-class architectures are tested against the conventional Gaussian mixture model based classifier on enrolled speaker sets of different sizes. The performance evaluation shows that the multi-class neural network system has superior performance for large population sizes.

Code-switching, the alternation of languages within a conversation or utterance, is a common communicative phenomenon that occurs in multilingual communities across the world. This survey reviews computational approaches for code-switched Speech and Natural Language Processing. We motivate why processing code-switched text and speech is essential for building intelligent agents and systems that interact with users in multilingual communities. As code-switching data and resources are scarce, we list what is available in various code-switched language pairs with the language processing tasks they can be used for. We review code-switching research in various Speech and NLP applications, including language processing tools and end-to-end systems. We conclude with future directions and open problems in the field.

Broadband customers who are having internet problems are about to start getting refunds – without even having to ask. At the moment, only about one in seven people who have internet or landline problems such as repairs, installations or missed engineer appointments are given any kind of compensation from the companies responsible, according to regulator Ofcom. Even if they do, the amounts are usually small. But now customers will find themselves being given those refunds automatically, for any kind of broadband problems, Ofcom said. We'll tell you what's true.

In the context of fake news, bias, and propaganda, we study two important but relatively under-explored problems: (i) trustworthiness estimation (on a 3-point scale) and (ii) political ideology detection (left/right bias on a 7-point scale) of entire news outlets, as opposed to evaluating individual articles. In particular, we propose a multi-task ordinal regression framework that models the two problems jointly. This is motivated by the observation that hyper-partisanship is often linked to low trustworthiness, e.g., appealing to emotions rather than sticking to the facts, while center media tend to be generally more impartial and trustworthy. We further use several auxiliary tasks, modeling centrality, hyperpartisanship, as well as left-vs.-right bias on a coarse-grained scale. The evaluation results show sizable performance gains by the joint models over models that target the problems in isolation.

In this paper we address the problem of discovering a small set of frequent serial episodes from sequential data so as to adequately characterize or summarize the data. We discuss an algorithm based on the Minimum Description Length (MDL) principle and the algorithm is a slight modification of an earlier method, called CSC-2. We present a novel generative model for sequence data containing prominent pairs of serial episodes and, using this, provide some statistical justification for the algorithm. We believe this is the first instance of such a statistical justification for an MDL based algorithm for summarizing event sequence data. We then present a novel application of this data mining algorithm in text classification. By considering text documents as temporal sequences of words, the data mining algorithm can find a set of characteristic episodes for all the training data as a whole. The words that are part of these characteristic episodes could then be considered the only relevant words for the dictionary thus resulting in a considerably reduced feature vector dimension. We show, through simulation experiments using benchmark data sets, that the discovered frequent episodes can be used to achieve more than four-fold reduction in dictionary size without losing any classification accuracy.

Killer robots may sound like the name of a science fiction film, but they could be becoming a reality - and soon. Scientists say artificial intelligence has developed so quickly that we could soon see weapons that can choose a target and kill without being controlled by a human. The United Nations has held five days of talks in Geneva, Switzerland on banning what are known as lethal autonomous weapons. But the United States, Russia, Israel and the United Kingdom are against any restrictions, saying these developments could make war safer. How likely are killer robots?

As the founding document of our present hypermodern unreality, it'll always be, 20 years after its release or 200, fair game for chat. Over medium-rare steaks that may or may not be 1s and 0s, guests happily quote the Oracle ("Take a cookie"), defend Keanu's acting, quote Agent Smith ("It's the smell!"), rehash Baudrillardian basics, and convince each other that there is no soup spoon (but pass the soup). Then the inevitable moment comes, and it is not fine. Some dweeby gasbag in attendance--picture him now; he may very well be you--gathers up the requisite oxygen to declare, with huffing sense of purpose and in sweaty anticipation of back slaps and applause: "Those sequels sure did suck, though!" Dammit, there goes the buzz.