This paper addresses the question as to what degree a BERT-based multilingual Spoken Language Understanding (SLU) model can transfer knowledge across languages. Through experiments we will show that, although it works substantially well even on distant language groups, there is still a gap to the ideal multilingual performance. In addition, we propose a novel BERT-based adversarial model architecture to learn language-shared and language-specific representations for multilingual SLU. Our experimental results prove that the proposed model is capable of narrowing the gap to the ideal multilingual performance.

There's no doubt that retail banking leaders understand the potential of artificial intelligence technology to improve customer experience. Nearly every one (94%) of more than 300 banking and insurance executives surveyed by The Capgemini Research Institute agreed that improving CX is the key objective behind launching new AI-enabled initiatives. In fact, more than half of the international sample say that at least 40% of customer interactions are already enabled by various AI applications, including conversational agents, prescriptive modeling, process automation, and complex analytics. That would be impressive -- except for one thing: Half of more than 5,000 consumers polled by Capgemini worldwide feel that the value they receive from AI-powered financial interactions was "non-existent or less than expected." What about in the U.S., the land of "Erica" and "Eno" and other digital assistants, and the many advanced mobile banking apps?

How to Do It is Slate's sex advice column. Send it to Stoya and Rich here. I am an older woman who has recently gotten back into the dating game. My problem is I hope going to be fun for you. So I am a semi-conservative woman and am hoping to meet someone similar. But I also am very sexual.

Humans are living in a truly global revolution of technology. The first two decades of the 21st century have witnessed dramatic advancements in artificial intelligence (AI) research. Machine learning has proven to be one of the most successful and widespread applications of technology, affecting a wide range of industries and impacting billions of users every day. Machine learning is a subset of artificial intelligence that involves the study and use of algorithms and statistical models for computer systems to perform specific tasks without human interaction. Machine learning utilisation opens door to futuristic technologies that people use in their daily life.

Recommender systems have found enterprise application by assisting all the top players in the online marketplace, including Amazon, Netflix, Google and many others. These systems are the decision support systems that make the personalisation process better as well as smoother. It predicts and estimates the content of user preferences by extracting from various data sources such as previous database, data history, among others. Here, we have listed the top eight open-source recommender systems in Python, in no particular order, that you must try for your next project. About: LensKit is an open-source toolkit for building, researching, and learning about recommender systems. It provides support for training, running, and evaluating recommender algorithms in a flexible fashion suitable for research and education.

Humans are living in a truly global revolution of technology. The first two decades of the 21st century have witnessed dramatic advancements in artificial intelligence (AI) research. Machine learning has proven to be one of the most successful and widespread applications of technology, affecting a wide range of industries and impacting billions of users every day. Machine learning is a subset of artificial intelligence that involves the study and use of algorithms and statistical models for computer systems to perform specific tasks without human interaction. Machine learning utilisation opens door to futuristic technologies that people use in their daily life.

It doesn't take much time with Amazon's new Fire TV Stick and Fire TV Stick Lite to understand what they're all about. The $40 third-generation Fire TV Stick is an overdue upgrade to Amazon's best-selling streaming player, replacing its four-year-old processor with one that's much faster. The $30 Fire TV Stick Lite, meanwhile, is a naked attempt to achieve price parity with Roku's budget Express streamer, with the same performance as the standard Stick but a major compromise to its remote control: There are no TV volume or power buttons onboard. Of the two, the Fire TV Stick is much easier to recommend than the Lite version. I've said it before, but having TV controls built into the remote really is worth the extra $10. Whether the new Fire TV Sticks are worth buying over other budget streamers is harder to say, because Amazon is preparing a major software overhaul for later this year.

Social recommendation system is to predict unobserved user-item rating values by taking advantage of user-user social relation and user-item ratings. However, user/item diversities in social recommendations are not well utilized in the literature. Especially, inter-factor (social and rating factors) relations and distinct rating values need taking into more consideration. In this paper, we propose an attentive social recommendation system (ASR) to address this issue from two aspects. First, in ASR, Rec-conv graph network layers are proposed to extract the social factor, user-rating and item-rated factors and then automatically assign contribution weights to aggregate these factors into the user/item embedding vectors. Second, a disentangling strategy is applied for diverse rating values. Extensive experiments on benchmarks demonstrate the effectiveness and advantages of our ASR.

Although recommender systems have received great success, We categorize recommender systems as shallow it is well known for highly skewed datasets, models and deep models. The first class engineers and researchers need to adjust their incorporates shallow machine learning technologies methods to tackle the specific problem to yield good such as matrix factorization and learning to rank, results. Inability to deal with highly skewed dataset while the second class are deep learning models like usually generates hard computational problems for Wide and Deep [6]. Although a bit of out-of-dated, big data clusters and unsatisfactory results for shallow models are still widely used in small customers. In this paper, we propose a new companies and projects where agility, usability and algorithm solving the problem in the framework of matrix factorization. We model the data skewness efficiency far outweighs boost of performance which factors in the theoretic modeling of the approach is only economically visible for huge datasets. It is with easy to interpret and easy to implement well known since the invention of the first shallow formulas. We prove in experiments our method model, that data skewness and sparsity poses generates comparably favorite results with popular serious challenges for recommender system recommender system algorithms such as Learning performance. The setbacks are two folds: data to Rank, Alternating Least Squares and Deep Matrix skewness causes problems that need special Factorization.

Have you ever caught yourself thinking that no one understands you better than Netflix or YouTube? They just seem to get what you want and are always ready to deliver. The explanation for this impressive personalization lies in advanced data analytics mechanisms. The privacy concerns around big data are not empty words – BI does help business owners monetize your desires. When one thinks of the entertainment industry, the things that come to mind first are movies, theaters, concert venues, and sporting events.