The craze for blissful background music ideal for studying or chilling out to has spawned popular YouTube channels, streaming playlists and even AI-powered apps. AiMi -- which today is rolling out a major update for its generative music service -- sits squarely in the latter camp alongside peers Endel and Brain.fm. A little more than a year after its debut, the app for electronic music fans is launching a new interface that gives listeners six endless mixes to choose from. Their titles, including Serenity and Chill and Deep, give you an indication of the type of meditative, lo-fi and deep house beats on offer. So how does an AI-powered music app work? In AiMi's case, you hit play to listen to a feed of continuous music, including real tracks, generated by artificial intelligence.

Samsung's new virtual assistant is called Sam and looks like a Pixar character, new promo images reveal. Brazil-based animation studios Lightfarm shared its renders of Sam online at the weekend before hastily taking them down. Sam will likely power Samsung's Galaxy powered smartphones and smart'things' like home appliances, as a replacement for Bixby, which Samsung revealed in 2017. Sam could also power Samsung's first commercially available smart speaker, which has been frustratingly delayed since it was first revealed in 2018. Digital assistants like Amazon's Alexa and Apple's Siri are disembodied voices that address users through devices like phones and speakers.

As with all its smart TVs, Vizio's V-series brings the SmartCast interface to the table. It's best in show when it comes to effectively melding entertainment content from disparate sources (over-the-air, streaming, etc.). It also synergizes nicely with the company's minimalist remote, which now supports voice commands. The V5-series specifically is also relatively affordable. The 55-inch-class, model V555-J evaluated here retails for just $500.

Businesses of all sizes across different sectors are exploring or deploying Artificial Intelligence (A.I.) today more than ever before.

Christopher Nolan's cinematic success won an Oscar for best visual effects and grossed over $677 million worldwide. The film is centered around astronauts' journey to the far reaches of our galaxy to find a suitable planet for life as Earth is slowly dying. The lead character played by Oscar winner Matthew McConaughey, an astronaut and spaceship pilot, along with mission commander Brand and science specialists are heading towards a newly discovered wormhole. The mission takes the astronauts towards a spectacular interstellar journey through time and space, but at the same time they miss out on their own life back at home light years away. On board spaceship Endurance is a pair of quadrilateral robots called TARS and CASE.

Misinterpreted or misleading in cognitive science, human-computer interaction (HCI) and stories or facts are known to "go viral" and to increase the natural-language processing (NLP) to consider how analogical likelihood for incivility [11]. Referred to as "misinformation" reasoning (AR) could help inform the design of communication or "disinformation," the phenomenon is, in part, a product of and learning technologies, as well as online communities (exploiting) analogical reasoning and normal cognitive processes and digital platforms. First, analogical reasoning (AR) is [3, 19]. Problematically, digital platforms are efficient defined, and use-cases of AR in the computing sciences are mechanisms for spreading rumors, participating in misinterpretations, presented. The concept of schema is introduced, along with and for misconstruing fact-sharing as opinion [16].

Fact verification is a challenging task that requires simultaneously reasoning and aggregating over multiple retrieved pieces of evidence to evaluate the truthfulness of a claim. Existing approaches typically (i) explore the semantic interaction between the claim and evidence at different granularity levels but fail to capture their topical consistency during the reasoning process, which we believe is crucial for verification; (ii) aggregate multiple pieces of evidence equally without considering their implicit stances to the claim, thereby introducing spurious information. To alleviate the above issues, we propose a novel topic-aware evidence reasoning and stance-aware aggregation model for more accurate fact verification, with the following four key properties: 1) checking topical consistency between the claim and evidence; 2) maintaining topical coherence among multiple pieces of evidence; 3) ensuring semantic similarity between the global topic information and the semantic representation of evidence; 4) aggregating evidence based on their implicit stances to the claim. Extensive experiments conducted on the two benchmark datasets demonstrate the superiority of the proposed model over several state-of-the-art approaches for fact verification. The source code can be obtained from https://github.com/jasenchn/TARSA.

Inferring social relations from dialogues is vital for building emotionally intelligent robots to interpret human language better and act accordingly. We model the social network as an And-or Graph, named SocAoG, for the consistency of relations among a group and leveraging attributes as inference cues. Moreover, we formulate a sequential structure prediction task, and propose an $\alpha$-$\beta$-$\gamma$ strategy to incrementally parse SocAoG for the dynamic inference upon any incoming utterance: (i) an $\alpha$ process predicting attributes and relations conditioned on the semantics of dialogues, (ii) a $\beta$ process updating the social relations based on related attributes, and (iii) a $\gamma$ process updating individual's attributes based on interpersonal social relations. Empirical results on DialogRE and MovieGraph show that our model infers social relations more accurately than the state-of-the-art methods. Moreover, the ablation study shows the three processes complement each other, and the case study demonstrates the dynamic relational inference.

Question answering (QA) systems provide a way of querying the information available in various formats including, but not limited to, unstructured and structured data in natural languages. It constitutes a considerable part of conversational artificial intelligence (AI) which has led to the introduction of a special research topic on Conversational Question Answering (CQA), wherein a system is required to understand the given context and then engages in multi-turn QA to satisfy the user's information needs. Whilst the focus of most of the existing research work is subjected to single-turn QA, the field of multi-turn QA has recently grasped attention and prominence owing to the availability of large-scale, multi-turn QA datasets and the development of pre-trained language models. With a good amount of models and research papers adding to the literature every year recently, there is a dire need of arranging and presenting the related work in a unified manner to streamline future research. This survey, therefore, is an effort to present a comprehensive review of the state-of-the-art research trends of CQA primarily based on reviewed papers from 2016-2021. Our findings show that there has been a trend shift from single-turn to multi-turn QA which empowers the field of Conversational AI from different perspectives. This survey is intended to provide an epitome for the research community with the hope of laying a strong foundation for the field of CQA.

Recently, considerable literature has grown up around the theme of few-shot named entity recognition (NER), but little published benchmark data specifically focused on the practical and challenging task. Current approaches collect existing supervised NER datasets and re-organize them to the few-shot setting for empirical study. These strategies conventionally aim to recognize coarse-grained entity types with few examples, while in practice, most unseen entity types are fine-grained. In this paper, we present Few-NERD, a large-scale human-annotated few-shot NER dataset with a hierarchy of 8 coarse-grained and 66 fine-grained entity types. Few-NERD consists of 188,238 sentences from Wikipedia, 4,601,160 words are included and each is annotated as context or a part of a two-level entity type. To the best of our knowledge, this is the first few-shot NER dataset and the largest human-crafted NER dataset. We construct benchmark tasks with different emphases to comprehensively assess the generalization capability of models. Extensive empirical results and analysis show that Few-NERD is challenging and the problem requires further research. We make Few-NERD public at https://ningding97.github.io/fewnerd/.