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.

For the record, I don't own socks with sloths on them. I have three pairs with the CNET logo on them. ChatGPT thinks I might, though, and it also thinks this fact could get me matches on Hinge, or Bumble, or any dating app that has the audacity to ask me for a random fact about myself. Click to read more Love Syncs. Here's a random fact about me: When I tested how ChatGPT might handle rewriting my dating app profile, the experimental AI chatbot tried to turn me into a cringey manic pixie dream girl who forgets to water her "jungle" of houseplants, dances to her favorite "tunes" and is looking for "a fellow weirdo" to go on *shudders* "adventures" with.

Today, educators agree: that they need an Artificial Intelligence strategy. But many teachers do not know how to use artificial intelligence in education. Artificial intelligence is a significant influence on the state of education today. The implications of AI are enormous. AI has the potential to transform the functioning of the education system.

Radiology AI models have made significant progress in near-human performance or surpassing it. However, AI model's partnership with human radiologist remains an unexplored challenge due to the lack of health information standards, contextual and workflow differences, and data labeling variations. To overcome these challenges, we integrated an AI model service that uses DICOM standard SR annotations into the OHIF viewer in the open-source LibreHealth Radiology Information Systems (RIS). In this paper, we describe the novel Human-AI partnership capabilities of the platform, including few-shot learning and swarm learning approaches to retrain the AI models continuously. Building on the concept of machine teaching, we developed an active learning strategy within the RIS, so that the human radiologist can enable/disable AI annotations as well as "fix"/relabel the AI annotations. These annotations are then used to retrain the models. This helps establish a partnership between the radiologist user and a user-specific AI model. The weights of these user-specific models are then finally shared between multiple models in a swarm learning approach.

There once was a virtual assistant named Ms. Dewey, a comely librarian played by Janina Gavankar who assisted you with your inquiries on Microsoft's first attempt at a search engine. Ms. Dewey was launched in 2006, complete with over 600 lines of recorded dialog. She was ahead of her time in a few ways, but one particularly overlooked example was captured by information scholar Miriam Sweeney in her 2013 doctoral dissertation, where she detailed the gendered and racialized implications of Dewey's replies. That included lines like, "Hey, if you can get inside of your computer, you can do whatever you want to me." Or how searching for "blow jobs" caused a clip of her eating a banana to play, or inputting terms like "ghetto" made her perform a rap with lyrics including such gems as, "No, goldtooth, ghetto-fabulous mutha-fucker BEEP steps to this piece of [ass] BEEP."

Achieving human-like communication with machines remains a classic, challenging topic in the field of Knowledge Representation and Reasoning and Natural Language Processing. These Large Language Models (LLMs) rely on pattern-matching rather than a true understanding of the semantic meaning of a sentence. As a result, they may generate incorrect responses. To generate an assuredly correct response, one has to "understand" the semantics of a sentence. To achieve this "understanding", logic-based (commonsense) reasoning methods such as Answer Set Programming (ASP) are arguably needed. In this paper, we describe the AutoConcierge system that leverages LLMs and ASP to develop a conversational agent that can truly "understand" human dialogs in restricted domains. AutoConcierge is focused on a specific domain-advising users about restaurants in their local area based on their preferences. AutoConcierge will interactively understand a user's utterances, identify the missing information in them, and request the user via a natural language sentence to provide it. Once AutoConcierge has determined that all the information has been received, it computes a restaurant recommendation based on the user-preferences it has acquired from the human user. AutoConcierge is based on our STAR framework developed earlier, which uses GPT-3 to convert human dialogs into predicates that capture the deep structure of the dialog's sentence. These predicates are then input into the goal-directed s(CASP) ASP system for performing commonsense reasoning. To the best of our knowledge, AutoConcierge is the first automated conversational agent that can realistically converse like a human and provide help to humans based on truly understanding human utterances.