As part of the World No Tobacco Day, the World Health Organization (WHO) has multiplied initiatives to fight against smoking. The dissemination of digital tools and exploitation of artificial intelligence are two of the aspects on which the WHO wishes to rely to fight against tobacco. The UN agency specialized in public health has partnered with WhatsApp, Facebook, Viber, Soul Machines and AI Company to offer a virtual assistant to help consumers in their desire to quit smoking. The "Commit to Quit" campaign was launched by WHO to support tobacco users who want to quit but need help to do so. In 29 target countries, the agency has agreed on initiatives such as national awareness campaigns, new digital tools, policy reviews, youth engagement, training of health workers, opening of new smoking cessation clinics, support for nicotine replacement therapy through WHO partners, free telephone support services, and provision of smoking cessation courses.

The Coronavirus pandemic has brought a concern for the complete world. With the increase in numbers, the healthcare industry is searching for innovative methods to gain quick and accurate results. Among the different emerging technologies, artificial intelligence is one of the promising factors. Artificial intelligence in healthcare helps us analyze information, recognize patterns, and trends to recognize the long-term health dangers inaccurate ways. Its utilization in diagnostics and treatment allows for better health outcomes.

The role of artificial intelligence in education: AI is a booming technological domain capable of changing various aspects of the education system. The global Artificial intelligence market in education is forecasted to reach $3.68 billion. The role of artificial intelligence in education can be seen boosting efficiency, productivity, and convenience, providing the sector with a range of different benefits. I compiled a list of the 5 ways AI can revamp the education industry and bring some amazing transitions. A significant portion of working time is spent on administrative and grading tasks.

Conversational AI startup Cognigy today announced that it closed a $44 million series B funding round led by Insight Partners, which brings the company's total raised to over $50 million to date. Cofounder and CEO Philipp Heltewig says that the proceeds will be put toward accelerating customer growth, creating new partnerships, and continuing to enhance Cognigy's AI platform. The ubiquity of smartphones and messaging apps -- as well as the pandemic -- have contributed to the increased adoption of conversational technologies. Fifty-six percent of companies told Accenture in a survey that conversational bots and other experiences are driving disruption in their industry. And a Twilio study showed that 9 out of 10 consumers would like the option to use messaging to contact a business.

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.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A U.S. airman from Florida accused of raping an 11-year-old girl in Alabama last year had met the child on a dating app, according to a report. Air Force Senior Airman Keith Williams, 25, of a Hurlburt Field maintenance squadron, met the 11-year-old on the Badoo dating app before the alleged rape in October 2020, Northwest Florida Daily News reported, citing an affidavit filed in Alabama's Morgan County District Court. The girl's parents did not learn of the alleged sexual encounter in the backyard of their home until Feb. 12, when Williams sent the girl a friend request on Facebook, the report said.

Predicting student performance is a fundamental task in Intelligent Tutoring Systems (ITSs), by which we can learn about students' knowledge level and provide personalized teaching strategies for them. Researchers have made plenty of efforts on this task. They either leverage educational psychology methods to predict students' scores according to the learned knowledge proficiency, or make full use of Collaborative Filtering (CF) models to represent latent factors of students and exercises. However, most of these methods either neglect the exercise-specific characteristics (e.g., exercise materials), or cannot fully explore the high-order interactions between students, exercises, as well as knowledge concepts. To this end, we propose a Graph-based Exercise- and Knowledge-Aware Learning Network for accurate student score prediction. Specifically, we learn students' mastery of exercises and knowledge concepts respectively to model the two-fold effects of exercises and knowledge concepts. Then, to model the high-order interactions, we apply graph convolution techniques in the prediction process. Extensive experiments on two real-world datasets prove the effectiveness of our proposed Graph-EKLN.

Advanced technologies are causing an upheaval in the insurance industry. While digital technologies were already consolidating their position in P&C insurance, they are now diversifying their roles and transforming all verticals of the sector. Digital transformation in insurance is no less than a juggernaut that will remain evergreen as it grows and matures. The application of predictive modeling in such ways is enhancing insurance business revenue in many ways. For instance, a study by Valen Analytics discovered that insurance technology-forward companies using predictive analysis boosted their direct written premiums by a whopping 53% margin against the market average of 18%.

Our intelligence is what makes us human, and AI is an extension of that quality. Artificial Intelligence is a branch of science producing and studying the machines aimed at the stimulation of human intelligence processes. The main objective of AI is to optimize the routine processes, improving their speed and efficiency (provided it has been implemented and supported properly). As a result, the number of companies adopting AI continues to grow worldwide. According to Research and Markets,

In this paper, we investigate a new multi-armed bandit (MAB) online learning model that considers real-world phenomena in many recommender systems: (i) the learning agent cannot pull the arms by itself and thus has to offer rewards to users to incentivize arm-pulling indirectly; and (ii) if users with specific arm preferences are well rewarded, they induce a "self-reinforcing" effect in the sense that they will attract more users of similar arm preferences. Besides addressing the tradeoff of exploration and exploitation, another key feature of this new MAB model is to balance reward and incentivizing payment. The goal of the agent is to maximize the total reward over a fixed time horizon $T$ with a low total payment. Our contributions in this paper are two-fold: (i) We propose a new MAB model with random arm selection that considers the relationship of users' self-reinforcing preferences and incentives; and (ii) We leverage the properties of a multi-color Polya urn with nonlinear feedback model to propose two MAB policies termed "At-Least-$n$ Explore-Then-Commit" and "UCB-List". We prove that both policies achieve $O(log T)$ expected regret with $O(log T)$ expected payment over a time horizon $T$. We conduct numerical simulations to demonstrate and verify the performances of these two policies and study their robustness under various settings.