Online recommendation is an essential functionality across a variety of services, including e-commerce and video streaming, where items to buy, watch, or read are suggested to users. Justifying recommendations, i.e., explaining why a user might like the recommended item, has been shown to improve user satisfaction and persuasiveness of the recommendation. In this paper, we develop a method for generating post-hoc justifications that can be applied to the output of any recommendation algorithm. Existing post-hoc methods are often limited in providing diverse justifications, as they either use only one of many available types of input data, or rely on the predefined templates. We address these limitations of earlier approaches by developing J-Recs, a method for producing concise and diverse justifications. J-Recs is a recommendation model-agnostic method that generates diverse justifications based on various types of product and user data (e.g., purchase history and product attributes). The challenge of jointly processing multiple types of data is addressed by designing a principled graph-based approach for justification generation. In addition to theoretical analysis, we present an extensive evaluation on synthetic and real-world data. Our results show that J-Recs satisfies desirable properties of justifications, and efficiently produces effective justifications, matching user preferences up to 20% more accurately than baselines.

Artificial intelligence (AI) is everywhere. In a typical day, people likely use AI multiple times without even knowing it: Alexa and Siri, Google Maps, Uber and Lyft, autopilot on commercial flights, spam filters, and smart email categorization (so anyone using Gmail, Yahoo, or Office 365/outlook), mobile check deposits, plagiarism checkers, online searches, personalized recommendations, Facebook, Instagram, and Pinterest are all examples of AI. But what happens when people are being introduced to a new AI technology? How likely are they to trust the new technology? With an interdisciplinary team of researchers from the University of Kansas, we set to find out.

Machine learning is one of the most promising technologies for the coming decades, a branch of artificial intelligence that studies how to make machines learn, and that could completely change the world as we know it today. If we manage to develop machines capable of learning by themselves, they will probably do so much faster than humans. They will also be able to make more efficient use of the information acquired, getting closer and closer to executive intelligence. Machine learning promises to bring more intelligence to all the software of the machines and devices that surround us, from a smartphone to a coffee machine or a home device, such as Amazon's Echo or Google Home. With that ability to learn, machines will gradually acquire new skills and abilities, achieving previously unthinkable things.

Amazon is making it easier for you to connect devices to your Alexa ecosystem with its latest feature. When Device Discovery is enabled on your Alexa app, you can go to the More section then Add a Device. You'll see products on your WiFi network that you can link to the voice assistant. If you'd rather Alexa didn't link to a certain device that it finds, you don't need to do anything else. Otherwise, setting it up should be straightforward.

NLPNatural Language Processing is used so commonly today that we take it for granted. We use it with Amazon's Alexa, Google Home and Translate, voice-to-text dictation on our phones etc. It is practically everywhere and makes our interactions with devices faster, convenient, and easier. NLP is a branch of Artificial Intelligence (AI) that uses Machine Learning (ML) to understand a text or a voice command's meaning. "For instance, people ask questions in different ways (word choice, tone of voice, etc.) - One customer might ask," Can you update me on my last order status?",

Conversational recommendation systems have recently gain a lot of attention, as users can continuously interact with the system over multiple conversational turns. However, conversational recommendation systems are based on complex neural architectures, thus the training cost of such models is high. To shed light on the high computational training time of state-of-the art conversational models, we examine five representative strategies and demonstrate this issue. Furthermore, we discuss possible ways to cope with the high training cost following knowledge distillation strategies, where we detail the key challenges to reduce the online inference time of the high number of model parameters in conversational recommendation systems

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.