In recommendation systems, a large portion of the ratings are missing due to the selection biases, which is known as Missing Not At Random. The counterfactual inverse propensity scoring (IPS) was used to weight the imputation error of every observed rating. Although effective in multiple scenarios, we argue that the performance of IPS estimation is limited due to the uncertainty miscalibration of propensity estimation. In this paper, we propose the uncertainty calibration for the propensity estimation in recommendation systems with multiple representative uncertainty calibration techniques. Theoretical analysis on the bias and generalization bound shows the superiority of the calibrated IPS estimator over the uncalibrated one. Experimental results on the coat and yahoo datasets shows that the uncertainty calibration is improved and hence brings the better recommendation results.

Update: March 2, 2023 (3:22 AM AT): It appears that the issue has been fixed. Original article: February 28, 2023 (12:33 PM ET): If you tried to use Google Assistant this morning to do something like activate your lights or turn on your TV, you may have noticed something weird. It appears Google Assistant has forgotten how to control smart home devices.

ChatGPT has proven to be the ultimate wingman among men looking for love online - the chatbot helped one Tinder users get a date in less than one hour. Singles are harnessing the power of OpenAI's tool to curate the perfect dating profiles and responses to snag a potential match, as some feel dating apps have always favored women and ChatGPT is helping them'tip the scales.' Men are going from zero dates to dozens in just the first month of using the chatbot that creates whimsical poems, romantic notes and confident replies for individuals who would otherwise'struggle to come up with conversation starters.' While'the results have been astounding,' some people feel it is dishonest to use ChatGPT to reel women in because they are unaware they are talking to a chatbot. However, one Tinder user is not thinking twice about getting a little help to attract more women.

Artificial intelligence (AI) has become an increasingly important tool for businesses of all sizes. While AI was once seen as a technology reserved for large corporations with deep pockets, it has now become accessible even to small businesses. In fact, AI tools can be particularly beneficial for small businesses, helping them to automate routine tasks, improve efficiency, and provide better customer experiences. In this article, we'll explore why AI tools are essential for small businesses and how they can help businesses of all types and sizes. One of the primary benefits of AI tools is their ability to automate routine tasks.

In this paper, we present a demo of an intelligent personal agent called Hey Dona (or just Dona) with virtual voice assistance in student course registration. It is a deployed project in the theme of AI for education. In this digital age with a myriad of smart devices, users often delegate tasks to agents. While pointing and clicking supersedes the erstwhile command-typing, modern devices allow users to speak commands for agents to execute tasks, enhancing speed and convenience. In line with this progress, Dona is an intelligent agent catering to student needs by automated, voice-operated course registration, spanning a multitude of accents, entailing task planning optimization, with some language translation as needed. Dona accepts voice input by microphone (Bluetooth, wired microphone), converts human voice to computer understandable language, performs query processing as per user commands, connects with the Web to search for answers, models task dependencies, imbibes quality control, and conveys output by speaking to users as well as displaying text, thus enabling human-AI interaction by speech cum text. It is meant to work seamlessly on desktops, smartphones etc. and in indoor as well as outdoor settings. To the best of our knowledge, Dona is among the first of its kind as an intelligent personal agent for voice assistance in student course registration. Due to its ubiquitous access for educational needs, Dona directly impacts AI for education. It makes a broader impact on smart city characteristics of smart living and smart people due to its contributions to providing benefits for new ways of living and assisting 21st century education, respectively.

The Reading&Machine project exploits the support of digitalization to increase the attractiveness of libraries and improve the users' experience. The project implements an application that helps the users in their decision-making process, providing recommendation system (RecSys)-generated lists of books the users might be interested in, and showing them through an interactive Virtual Reality (VR)-based Graphical User Interface (GUI). In this paper, we focus on the design and testing of the recommendation system, employing data about all users' loans over the past 9 years from the network of libraries located in Turin, Italy. In addition, we use data collected by the Anobii online social community of readers, who share their feedback and additional information about books they read. Armed with this heterogeneous data, we build and evaluate Content Based (CB) and Collaborative Filtering (CF) approaches. Our results show that the CF outperforms the CB approach, improving by up to 47\% the relevant recommendations provided to a reader. However, the performance of the CB approach is heavily dependent on the number of books the reader has already read, and it can work even better than CF for users with a large history. Finally, our evaluations highlight that the performances of both approaches are significantly improved if the system integrates and leverages the information from the Anobii dataset, which allows us to include more user readings (for CF) and richer book metadata (for CB).

While uniform sampling has been widely studied in the matrix completion literature, CUR sampling approximates a low-rank matrix via row and column samples. Unfortunately, both sampling models lack flexibility for various circumstances in real-world applications. In this work, we propose a novel and easy-to-implement sampling strategy, coined Cross-Concentrated Sampling (CCS). By bridging uniform sampling and CUR sampling, CCS provides extra flexibility that can potentially save sampling costs in applications. In addition, we also provide a sufficient condition for CCS-based matrix completion. Moreover, we propose a highly efficient non-convex algorithm, termed Iterative CUR Completion (ICURC), for the proposed CCS model. Numerical experiments verify the empirical advantages of CCS and ICURC against uniform sampling and its baseline algorithms, on both synthetic and real-world datasets.

Amazon's Echo Show 8 is our pick for the best smart display for Alexa users, and it's now on sale for $75 at various retailers. While we've seen this deal before, it's still about $20 below the 8-inch display's typical street price and $55 off Amazon's list price. For reference, the device's all-time low is $70. This is only $5 more than the lowest price we've seen for this 8-inch smart display we recommend for Alexa users. We gave the Echo Show 8 a review score of 87 when it launched a couple of years ago, and we currently recommend it in our guides to the best smart displays and best smart home devices.

Apple's big, high-quality smart speaker is back for a surprise second generation. But five years since the first model was launched, a lot has changed in the world of voice-controlled home hi-fi. Can the HomePod still cut it? The new HomePod has the same design as the old version: a marshmallow-like shape with a light-up disc at the top, fabric-covered body and a small silicone foot. The detachable power cable slots in the back but otherwise there are no ports or recesses. As with other HomePods, this speaker is for Apple users only.

An increasingly prevalent problem for intelligent technologies is text safety, as uncontrolled systems may generate recommendations to their users that lead to injury or life-threatening consequences. However, the degree of explicitness of a generated statement that can cause physical harm varies. In this paper, we distinguish types of text that can lead to physical harm and establish one particularly underexplored category: covertly unsafe text. Then, we further break down this category with respect to the system's information and discuss solutions to mitigate the generation of text in each of these subcategories. Ultimately, our work defines the problem of covertly unsafe language that causes physical harm and argues that this subtle yet dangerous issue needs to be prioritized by stakeholders and regulators. We highlight mitigation strategies to inspire future researchers to tackle this challenging problem and help improve safety within smart systems.