Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. You're suddenly thrown into a situation where you must perform CPR to save a life. Oh, no -- you don't remember anything from that course 15 years ago. You might think a quick "Hey, Siri" would pull up the instructions quickly and clearly, but that's absolutely the worst thing to do.

In the book "Ethics in Design and Communication: Critical Perspectives," designer and researcher Sarah Edmands Martin wrote that Tinder's design, which presents users with profile cards of potential matches stacked on top of one another, means users "are urged onward" to the next profile "peeking from below the current card, subtly pressuring a user to move on."

Amazon is ringing in Presidents' Day with big sales on its Echo devices, including its fourth-generation Amazon Echo. The smart speaker is currently down to 55 from 100 -- a 45 percent discount. Though released in 2020, Amazon's 4th-gen Echo is still its latest iteration and has held its weight over the years. We even named it 2024's best smart speaker under 100. So, what makes the 4th-gen Amazon Echo so great?

Training generalist robot agents is an immensely difficult feat due to the requirement to perform a huge range of tasks in many different environments. We propose selectively training robots based on end-user preferences instead. Given a factory model that lets an end user instruct a robot to perform lower-level actions (e.g. 'Move left'), we show that end users can collect demonstrations using language to train their home model for higher-level tasks specific to their needs (e.g. 'Open the top drawer and put the block inside'). We demonstrate this hierarchical robot learning framework on robot manipulation tasks using RLBench environments. Our method results in a 16% improvement in skill success rates compared to a baseline method. In further experiments, we explore the use of the large vision-language model (VLM), Bard, to automatically break down tasks into sequences of lower-level instructions, aiming to bypass end-user involvement. The VLM is unable to break tasks down to our lowest level, but does achieve good results breaking high-level tasks into mid-level skills. We have a supplemental video and additional results at talk-through-it.github.io.

Interactions among large number of entities is naturally high-dimensional and incomplete (HDI) in many big data related tasks. Behavioral characteristics of users are hidden in these interactions, hence, effective representation of the HDI data is a fundamental task for understanding user behaviors. Latent factor analysis (LFA) model has proven to be effective in representing HDI data. The performance of an LFA model relies heavily on its training process, which is a non-convex optimization. It has been proven that incorporating local curvature and preprocessing gradients during its training process can lead to superior performance compared to LFA models built with first-order family methods. However, with the escalation of data volume, the feasibility of second-order algorithms encounters challenges. To address this pivotal issue, this paper proposes a mini-block diagonal hessian-free (Mini-Hes) optimization for building an LFA model. It leverages the dominant diagonal blocks in the generalized Gauss-Newton matrix based on the analysis of the Hessian matrix of LFA model and serves as an intermediary strategy bridging the gap between first-order and second-order optimization methods. Experiment results indicate that, with Mini-Hes, the LFA model outperforms several state-of-the-art models in addressing missing data estimation task on multiple real HDI datasets from recommender system. (The source code of Mini-Hes is available at https://github.com/Goallow/Mini-Hes)

Many of us have had bad experiences of being swiped left, ghosted, breadcrumbed and benched on internet dating apps – though few people have ever thought to take their heartbreak to court. On Valentine's Day, six dating app users filed a proposed class-action lawsuit accusing Tinder, Hinge and other Match dating apps of using addictive, game-like features to encourage compulsive use. Match's apps, according to the lawsuit filed in federal court in the Northern District of California, "employ recognised dopamine-manipulating product features" to turn users into "gamblers locked in a search for psychological rewards", generating "market success by fomenting dating app addiction that drives expensive subscriptions and perpetual use". Match said the lawsuit was "ridiculous", but online dating experts said it reflected a broader backlash to the way apps were gamifying human experience for profit and leaving people feeling manipulated. "I'm not at all surprised that this has come to litigation. I think big tech is the new big tobacco, as smartphones are just as addictive as cigarettes," said Mia Levitin, author of The Future of Seduction.

Dating apps are supposedly'designed to be deleted,' but a new class action lawsuit claims the apps are instead'designed to be addictive.' The lawsuit, filed on Valentine's Day against Match Group which owns Tinder, Hinge, Match, OkCupid, and Plenty of Fish, accused the company of using'psychological manipulation' like push notifications, rewards, and punishments to guarantee users keep swiping right. The app is designed to turn users into'addicts' who are enticed by the game-like play-to-play loop, the lawsuit claimed, accusing Match Group of prioritizing profit over promises to help users find love. Match sells subscription plans to remove like limits and see who likes you with Tinder offering its Gold package for 140 for six months or 40 for one month and its Platinum package for 50 per month or 180 for six months. The lawsuit claims that if users were content with the basic app features, they wouldn't need to purchase the additional subscription when they reach their'like limit.'

Algorithmic fairness and explainability are foundational elements for achieving responsible AI. In this paper, we focus on their interplay, a research area that is recently receiving increasing attention. To this end, we first present two comprehensive taxonomies, each representing one of the two complementary fields of study: fairness and explanations. Then, we categorize explanations for fairness into three types: (a) Explanations to enhance fairness metrics, (b) Explanations to help us understand the causes of (un)fairness, and (c) Explanations to assist us in designing methods for mitigating unfairness. Finally, based on our fairness and explanation taxonomies, we present undiscovered literature paths revealing gaps that can serve as valuable insights for future research.

Leveraging users' long engagement histories is essential for personalized content recommendations. The success of pretrained language models (PLMs) in NLP has led to their use in encoding user histories and candidate items, framing content recommendations as textual semantic matching tasks. However, existing works still struggle with processing very long user historical text and insufficient user-item interaction. In this paper, we introduce a content-based recommendation framework, SPAR, which effectively tackles the challenges of holistic user interest extraction from the long user engagement history. It achieves so by leveraging PLM, poly-attention layers and attention sparsity mechanisms to encode user's history in a session-based manner. The user and item side features are sufficiently fused for engagement prediction while maintaining standalone representations for both sides, which is efficient for practical model deployment. Moreover, we enhance user profiling by exploiting large language model (LLM) to extract global interests from user engagement history. Extensive experiments on two benchmark datasets demonstrate that our framework outperforms existing state-of-the-art (SoTA) methods.

From robots that replace workers to robots that serve as helpful colleagues, the field of robotic automation is experiencing a new trend that represents a huge challenge for component manufacturers. The contribution starts from an innovative vision that sees an ever closer collaboration between Cobot, able to do a specific physical job with precision, the AI world, able to analyze information and support the decision-making process, and the man able to have a strategic vision of the future.