Tinder has posted a warning for its users telling them that coronavirus safety is'more important' than dating and urging them to wash their hands frequently. The dating app also encourages its singletons to carry hand sanitiser, avoid touching their face and'maintain social distance' when out in public. The warning says: 'Tinder is a great place to meet new people. While we want you to continue to have fun, protecting yourself from the coronavirus is more important'. It appears as a pop up while users are flipping between potential matches to warn of the dangers of the potentially deadly virus now called COVID-19. The pop-up warning also includes a link to the latest advice and information from the World Health Organisation (WHO) website.

The result: eBay's copywriters are now able to devote more time to more creative work, such as crafting prose, according to Molly Prosser, associate creative director at eBay and a big believer in AI writing tools. "All the time I've spent lingering over the length of a subject line, wondering'Is this word more engaging, or is this word more engaging', or'How do I convey urgency without seeming too cliché'," says Prosser. "The hours I've spent editing and having team members pour over these things -- it's just meaningless work when we have a piece of AI that can do that for us. The new capability enables Naylor to offer a personalized newsletter for each association member it services -- based on his or her interests and reading habits. The AI tool incorporates personalized content that Naylor generates in-house, as well as content it finds on the Web –including job ads that match the specific job skills of the reader. Essentially, AI enables newsletters to become ever-more personalized over time by monitoring how each reader interacts with his or her personalized newsletter, and making adjustments accordingly, says Amith Nagaranjan, executive chairman, Rasa.io.

Artificial Intelligence (AI) seems to be a unique technology of making a machine, a robot fully autonomous. AI is an analysis of how the machine is thinking, studying, determining and functioning when it is trying to solve problems. These so-called problems are present in all fields - the most emerging ones in 2020 and even beyond. The aim of Artificial Intelligence (AI) is to enhance machine functions relating to human knowledge, such as reasoning, learning and problems along with the ability to manipulate things. For example, virtual assistants or chatbots offer expert advice.

ClickZ spoke with Gonca Latif-Schmitt, Managing Director at Citi, to discuss their technologically-savvy approach to improving customer experience (CX) for their business card holders and clients. Citi engaged work with Interactions for their Citi Commercial Card initiative. Interactions is a provider of intelligent virtual assistants (IVAs) focused on helping companies improve CX via the use of AI chatbot technology. They offers several products that fall within the customer experience space, including an IVA tool utilized by leading brands such as Hyatt, Constant Contact, and Shutterfly. As a credit card business within Citi's institutional group, the commercial card team is focused on making the entire client experience frictionless and more digital.

We consider the problem faced by a recommender system which seeks to offer a user with unknown preferences an item. Before making a recommendation, the system has the opportunity to elicit the user's preferences by making queries. Each query corresponds to a pairwise comparison between items. We take the point of view of either a risk averse or regret averse recommender system which only possess set-based information on the user utility function. We investigate: a) an offline elicitation setting, where all queries are made at once, and b) an online elicitation setting, where queries are selected sequentially over time. We propose exact robust optimization formulations of these problems which integrate the elicitation and recommendation phases and study the complexity of these problems. For the offline case, where the problem takes the form of a two-stage robust optimization problem with decision-dependent information discovery, we provide an enumeration-based algorithm and also an equivalent reformulation in the form of a mixed-binary linear program which we solve via column-and-constraint generation. For the online setting, where the problem takes the form of a multi-stage robust optimization problem with decision-dependent information discovery, we propose a conservative solution approach. We evaluate the performance of our methods on both synthetic data and real data from the Homeless Management Information System. We simulate elicitation of the preferences of policy-makers in terms of characteristics of housing allocation policies to better match individuals experiencing homelessness to scarce housing resources. Our framework is shown to outperform the state-of-the-art techniques from the literature.

The ubiquity of smart phones and electronic devices has placed a wealth of information at the fingertips of consumers as well as creators of digital content. This has led to millions of notifications being issued each second from alerts about posted YouTube videos to tweets, emails and personal messages. Adding work related notifications and we can see how quickly the number of notifications increases. Not only does this cause reduced productivity and concentration but has also been shown to cause alert fatigue. This condition makes users desensitized to notifications, causing them to ignore or miss important alerts. Depending on what domain users work in, the cost of missing a notification can vary from a mere inconvenience to life and death. Therefore, in this work, we propose an alert and notification framework that intelligently issues, suppresses and aggregates notifications, based on event severity, user preferences, or schedules, to minimize the need for users to ignore, or snooze their notifications and potentially forget about addressing important ones. Our framework can be deployed as a backend service, but is better suited to be integrated into proactive conversational agents, a field receiving a lot of attention with the digital transformation era, email services, news services and others. However, the main challenge lies in developing the right machine learning algorithms that can learn models from a wide set of users while customizing these models to individual users' preferences.

SearchAI Answers is an Artificial Intelligence as a Service (AIaaS) service from SearchBlox that offers direct answers to natural language search queries using an AI model built using the customers' content, without the need for any manual tagging or a domain specific taxonomy or creating a knowledge graph. Search Engines as Question Answering (QA) Engines will replace the traditional approach of information retrieval where we are presented with a list of search results links. What is driving the transformation from search to answers? • New generation of searchers using Siri, Echo & Google Assistant using voice as a primary channel of getting information • Want to ask direct questions and receive direct answers • Ask questions in natural language or a conversational manner • Need concise, relevant and context based domain specific answers SearchAI Answers use explicit and implicit feedback to continually improve the quality of answers using MLOps. What are the business benefits of using SearchAI Answers on your website or portal? Contact us to get started and we will have you running our AI service very quickly.

When Kursat Ceylan, who is blind, was trying to find his way to a hotel, he used an app on his phone for directions, but also had to hold his cane and pull his luggage. He ended up walking into a pole, cutting his forehead. This inspired him to develop, along with a partner, Wewalk - a cane equipped with artificial intelligence (AI), that detects objects above chest level and pairs with apps including Google Maps and Amazon's Alexa, so the user can ask questions. Jean Marc Feghali, who helped to develop the product, also has an eye condition. In his case his vision is severely impaired when the light is not good. While the smart cane itself only integrates with basic AI functions right now, the aim is for Wewalk, to use information gathered from the gyroscope, accelerometer and compass installed inside the cane.

AI has proven to have a considerable impact on some major industries. While autonomous cars and virtual assistants are slowly becoming a reality, the creative industry has been experimenting with AI for several years already. Does it have meaningful implications and if so, what will it bring in the future? It's universally agreed that the first computer-assisted music score dates back to 1957 when composers Lejaren Hiller and Leonard Isaacson unveiled Illiac Suite for string quartet. Utilizing the interconnection between mathematics and music, Hiller was able to program the computer to come up with a stunning four-piece musical score. One of the most notable AI-assisted music projects happened two years ago.

Recommendation from implicit feedback is a highly challenging task due to the lack of the reliable observed negative data. A popular and effective approach for implicit recommendation is to treat unobserved data as negative but downweight their confidence. Naturally, how to assign confidence weights and how to handle the large number of the unobserved data are two key problems for implicit recommendation models. However, existing methods either pursuit fast learning by manually assigning simple confidence weights, which lacks flexibility and may create empirical bias in evaluating user's preference; or adaptively infer personalized confidence weights but suffer from low efficiency. To achieve both adaptive weights assignment and efficient model learning, we propose a fast adaptively weighted matrix factorization (FAWMF) based on variational auto-encoder. The personalized data confidence weights are adaptively assigned with a parameterized neural network (function) and the network can be inferred from the data. Further, to support fast and stable learning of FAWMF, a new specific batch-based learning algorithm fBGD has been developed, which trains on all feedback data but its complexity is linear to the number of observed data. Extensive experiments on real-world datasets demonstrate the superiority of the proposed FAWMF and its learning algorithm fBGD.