Many practical applications, such as recommender systems and learning to rank, involve solving multiple similar tasks. One example is learning of recommendation policies for users with similar movie preferences, where the users may still rank the individual movies slightly differently. Such tasks can be organized in a hierarchy, where similar tasks are related through a shared structure. In this work, we formulate this problem as a contextual off-policy optimization in a hierarchical graphical model from logged bandit feedback. To solve the problem, we propose a hierarchical off-policy optimization algorithm (HierOPO), which estimates the parameters of the hierarchical model and then acts pessimistically with respect to them. We instantiate HierOPO in linear Gaussian models, for which we also provide an efficient implementation and analysis. We prove per-task bounds on the suboptimality of the learned policies, which show a clear improvement over not using the hierarchical model. We also evaluate the policies empirically. Our theoretical and empirical results show a clear advantage of using the hierarchy over solving each task independently.

Have you ever thought how YouTube adjust your feed as per your favorite content? Why is your Netflix recommending you your favorite TV shows? Have you ever wanted to build a customized deep learning-based recommender system for yourself? If Yes! Then this is the course you are looking for. You might have searched for many relevant courses, but this course is different!

Intelligent agents have great potential as facilitators of group conversation among older adults. However, little is known about how to design agents for this purpose and user group, especially in terms of agent embodiment. To this end, we conducted a mixed methods study of older adults' reactions to voice and body in a group conversation facilitation agent. Two agent forms with the same underlying artificial intelligence (AI) and voice system were compared: a humanoid robot and a voice assistant. One preliminary study (total n=24) and one experimental study comparing voice and body morphologies (n=36) were conducted with older adults and an experienced human facilitator. Findings revealed that the artificiality of the agent, regardless of its form, was beneficial for the socially uncomfortable task of conversation facilitation. Even so, talkative personality types had a poorer experience with the "bodied" robot version. Design implications and supplementary reactions, especially to agent voice, are also discussed.

Deutsche Bank is looking to deploy artificial intelligence (AI) acceleration technology from Nvidia to power financial services applications. The bank hopes AI will improve its efforts to serve customers worldwide and enable it to build new data-driven products and services, increase efficiency and recruit tech talent. Using Nvidia AI Enterprise software, Deutsche Bank said its AI developers, data scientists and IT professionals would be able to build and run AI workflows in hosted on-premise datacentres as well as on Google Cloud, which the bank uses as its public cloud provider. The bank plans to use the latest version of Nvidia's enterprise AI tool – AI Enterprise 3.0. This introduces workflows for contact centre intelligent virtual assistants, audio transcription and digital fingerprinting for cyber security.

Amazon's Echo Show 15 is now useful as a tiny TV. The company has released a promised free update that brings the Fire TV interface to the smart display. As on other devices, you can stream from a range of apps (including Netflix, Prime Video and YouTube) with an on-screen carousel that helps you find content and resume shows. This will be familiar to anyone who's used even a basic Fire TV Stick, but the Echo Show's design offers a few interface twists. You can use Alexa to open apps or stream specific content, and use either a paired Alexa Voice Remote or the controls in the mobile Fire TV app to navigate without smudging the screen.

The Echo Show 5 is one of Amazon's most versatile Alexa devices thanks to its 5.5-inch display, it's relatively solid sound quality and it's compact size. The online retailer discounted the smart display to $35 for the holiday shopping season, but it also has a deal on a bundle that would make a good smart-home starter kit. You can pick up the Echo Show 5 with a Blink Mini for a total of $50, which is 58 percent off the normal price of the bundle. Amazon's nightstand-friendly smart display paired with a Blink Mini security camera is 58 percent off right now. Amazon last updated the Echo Show 5 in 2021, giving it a 2-megapixel front camera that will slightly improve the quality of your video chats.

Software-related platforms have enabled their users to collaboratively label software entities with topics. Tagging software repositories with relevant topics can be exploited for facilitating various downstream tasks. For instance, a correct and complete set of topics assigned to a repository can increase its visibility. Consequently, this improves the outcome of tasks such as browsing, searching, navigation, and organization of repositories. Unfortunately, assigned topics are usually highly noisy, and some repositories do not have well-assigned topics. Thus, there have been efforts on recommending topics for software projects, however, the semantic relationships among these topics have not been exploited so far. We propose two recommender models for tagging software projects that incorporate the semantic relationship among topics. Our approach has two main phases; (1) we first take a collaborative approach to curate a dataset of quality topics specifically for the domain of software engineering and development. We also enrich this data with the semantic relationships among these topics and encapsulate them in a knowledge graph we call SED-KGraph. Then, (2) we build two recommender systems; The first one operates only based on the list of original topics assigned to a repository and the relationships specified in our knowledge graph. The second predictive model, however, assumes there are no topics available for a repository, hence it proceeds to predict the relevant topics based on both textual information of a software project and SED-KGraph. We built SED-KGraph in a crowd-sourced project with 170 contributors from both academia and industry. The experiment results indicate that our solutions outperform baselines that neglect the semantic relationships among topics by at least 25% and 23% in terms of ASR and MAP metrics.

It's really important to discuss the benefits and drawbacks of artificial intelligence before it gets out of hand because this technology is improving and evolving at such a pace. As a computer science field, AI focuses on developing software and machines that mimic human thinking. Some artificial intelligence systems can analyze large amounts of data to learn from the past and enhance their performance without the input of programmers. AI is now widespread in both business and daily life. People interact with AI-powered virtual assistants or software daily to enhance their lives.

My reading so far, is that designing and implementing a real time automated recommendation is a probem that requires multiple objectives to solve and this is the need of the hour. . Such multi-objective system needs competing objectives of consumers, possible tensions between goals of different stakeholders, conflicts when optimizing for different time horizons, competing design choices at the UI level, as well as system-level and engineering-related considerations. Solution I am using the @kaggle competation is kind of enesemble modle RNN, Transformers and Linear Programming.

This research explores substitution of the fittest (SF), a technique designed to counteract the problem of disengagement in two-population competitive coevolutionary genetic algorithms. SF is domain-independent and requires no calibration. We first perform a controlled comparative evaluation of SF's ability to maintain engagement and discover optimal solutions in a minimal toy domain. Experimental results demonstrate that SF is able to maintain engagement better than other techniques in the literature. We then address the more complex real-world problem of evolving recommendations for health and well-being. We introduce a coevolutionary extension of EvoRecSys, a previously published evolutionary recommender system. We demonstrate that SF is able to maintain engagement better than other techniques in the literature, and the resultant recommendations using SF are higher quality and more diverse than those produced by EvoRecSys.