Artificial intelligence and machine learning algorithms have become ubiquitous. Although they offer a wide range of benefits, their adoption in decision-critical fields is limited by their lack of interpretability, particularly with textual data. Moreover, with more data available than ever before, it has become increasingly important to explain automated predictions. Generally, users find it difficult to understand the underlying computational processes and interact with the models, especially when the models fail to generate the outcomes or explanations, or both, correctly. This problem highlights the growing need for users to better understand the models' inner workings and gain control over their actions. This dissertation focuses on two fundamental challenges of addressing this need. The first involves explanation generation: inferring high-quality explanations from text documents in a scalable and data-driven manner. The second challenge consists in making explanations actionable, and we refer to it as critiquing. This dissertation examines two important applications in natural language processing and recommendation tasks. Overall, we demonstrate that interpretability does not come at the cost of reduced performance in two consequential applications. Our framework is applicable to other fields as well. This dissertation presents an effective means of closing the gap between promise and practice in artificial intelligence.

Cross-domain recommendation aims to leverage knowledge from multiple domains to alleviate the data sparsity and cold-start problems in traditional recommender systems. One popular paradigm is to employ overlapping user representations to establish domain connections, thereby improving recommendation performance in all scenarios. Nevertheless, the general practice of this approach is to train user embeddings in each domain separately and then aggregate them in a plain manner, often ignoring potential cross-domain similarities between users and items. Furthermore, considering that their training objective is recommendation task-oriented without specific regularizations, the optimized embeddings disregard the interest alignment among user's views, and even violate the user's original interest distribution. To address these challenges, we propose a novel cross-domain recommendation framework, namely COAST, to improve recommendation performance on dual domains by perceiving the cross-domain similarity between entities and aligning user interests. Specifically, we first construct a unified cross-domain heterogeneous graph and redefine the message passing mechanism of graph convolutional networks to capture high-order similarity of users and items across domains. Targeted at user interest alignment, we develop deep insights from two more fine-grained perspectives of user-user and user-item interest invariance across domains by virtue of affluent unsupervised and semantic signals. We conduct intensive experiments on multiple tasks, constructed from two large recommendation data sets. Extensive results show COAST consistently and significantly outperforms state-of-the-art cross-domain recommendation algorithms as well as classic single-domain recommendation methods.

Kurt "CyberGuy" Knutsson helps you to find parking spots with this easy to use Apple Maps feature. An increasing number of you are turning to streaming as your primary way of consuming media. New streaming services and original content are also expected to drive growth in the industry. With all of this in mind, we want to ensure that you can watch all the content you love, so we've gathered up five of the best streaming devices on the market. CLICK TO GET KURT'S CYBERGUY NEWSLETTER WITH QUICK TIPS, TECH REVIEWS, SECURITY ALERTS AND EASY HOW-TO'S TO MAKE YOU SMARTER With over 208,000 reviews on Amazon and an 84% approval rating at the time of publishing, the Amazon Fire Stick is an excellent streaming device choice.

Background checks and ID verification systems in dating apps are among the measures being considered as governments around the country grapple with how to keep people safe while they are looking for love online. The strategies were discussed by ministers, victim-survivors, authorities and technology companies as part of national dating app roundtable talks in Sydney on Wednesday. The federal communications minister, Michelle Rowland, said it was an "important first step", flagging discussion of possible longer-term changes like background checks for dating app users. "None of us underestimate the complex issues around privacy, user safety, data collection and management that are involved," she said. "There's no one law that is going to fix this issue."

One night this January, as Robert Stewart scrolled through old Hinge matches, he decided to revive a conversation he had begun months ago with a woman on the dating app. After picking up where they left off and exchanging a few pleasantries, Stewart asked if the woman wanted to get on a phone call. He hoped it would lead to an in-person date. "We could do that," the woman answered, but with one caveat. Stewart, who lives in Dallas, clicked on a Google Form the woman sent, titled "Dating Compatibility Q&A".

Federated Learning (FL) is a well-established technique for privacy preserving distributed training. Much attention has been given to various aspects of FL training. A growing number of applications that consume FL-trained models, however, increasingly operate under dynamically and unpredictably variable conditions, rendering a single model insufficient. We argue for training a global family of models cost efficiently in a federated fashion. Training them independently for different tradeoff points incurs $O(k)$ cost for any k architectures of interest, however. Straightforward applications of FL techniques to recent weight-shared training approaches is either infeasible or prohibitively expensive. We propose SuperFed - an architectural framework that incurs $O(1)$ cost to co-train a large family of models in a federated fashion by leveraging weight-shared learning. We achieve an order of magnitude cost savings on both communication and computation by proposing two novel training mechanisms: (a) distribution of weight-shared models to federated clients, (b) central aggregation of arbitrarily overlapping weight-shared model parameters. The combination of these mechanisms is shown to reach an order of magnitude (9.43x) reduction in computation and communication cost for training a $5*10^{18}$-sized family of models, compared to independently training as few as $k = 9$ DNNs without any accuracy loss.

Conversational information seeking (CIS) is concerned with a sequence of interactions between one or more users and an information system. Interactions in CIS are primarily based on natural language dialogue, while they may include other types of interactions, such as click, touch, and body gestures. This monograph provides a thorough overview of CIS definitions, applications, interactions, interfaces, design, implementation, and evaluation. This monograph views CIS applications as including conversational search, conversational question answering, and conversational recommendation. Our aim is to provide an overview of past research related to CIS, introduce the current state-of-the-art in CIS, highlight the challenges still being faced in the community. and suggest future directions.

If you're struggling to stay on top of your tasks or keep track of your notes, maybe what you need are some new tools. I'm always looking for better ways to stay organized. When I find a new app that sounds promising, I pit it against my existing tools in a game of survival of fittest, leaving only the ones that work best for me. These are currently the five services I rely on the most for note-taking, bookmarking, and task management. As we head into the new year, perhaps they'll provide just the kind of fresh inspiration you're looking for.

They can help you get an appointment or order a pizza, find the best ticket deals and bring your attention to the fact you are spending a lot on entertainment instead of investments. We are talking about AI virtual assistants, which have already become a familiar part of our daily lives. But what technologies are under the hood of AI assistants and how can you leverage them in your business? Find all the answers in this article. Intelligent Virtual Assistants (IVA) also known as Intelligent Personal Assistants (IPA) are AI-powered agents capable of generating personalized responses, pulling from contexts such as customer metadata, prior conversations, knowledge bases, geolocation, and other modular databases and plug-ins. The Intelligent Virtual Assistant market, experiencing rapid growth in the 2020s, is forecasted to reach USD 6.27 billion by 2026, according to Mordor Intelligence.

Quality education, one of the seventeen sustainable development goals (SDGs) identified by the United Nations General Assembly, stands to benefit enormously from the adoption of artificial intelligence (AI) driven tools and technologies. The concurrent boom of necessary infrastructure, digitized data and general social awareness has propelled massive research and development efforts in the artificial intelligence for education (AIEd) sector. In this review article, we investigate how artificial intelligence, machine learning and deep learning methods are being utilized to support students, educators and administrative staff. We do this through the lens of a novel categorization approach. We consider the involvement of AI-driven methods in the education process in its entirety - from students admissions, course scheduling etc. in the proactive planning phase to knowledge delivery, performance assessment etc. in the reactive execution phase. We outline and analyze the major research directions under proactive and reactive engagement of AI in education using a representative group of 194 original research articles published in the past two decades i.e., 2003 - 2022. We discuss the paradigm shifts in the solution approaches proposed, i.e., in the choice of data and algorithms used over this time. We further dive into how the COVID-19 pandemic challenged and reshaped the education landscape at the fag end of this time period. Finally, we pinpoint existing limitations in adopting artificial intelligence for education and reflect on the path forward.