The only thing better than a robot that vacuums for you is one that empties its own dust tray, too. The popular Shark AI Ultra robovac, which is normally a hard-to-swallow 600, is half off for Prime Day, bringing the cost down to just 295. You can control this vacuum with voice commands -- it supports both Amazon's Alexa and Google Assistant -- and it uses Matrix Clean Navigation to make sure it hits every spot in your home. This Shark AI Ultra is a version of one of our favorite robot vacuums, and comes with a bagless, self-emptying base that holds up to 60 days' worth of dirt and debris. It's made to tackle even the daily accumulation of pet hair, with powerful suction and a self-cleaning brushroll that has an anti-hair wrap to prevent tangling.

WIRED's coverage of the best Amazon Prime Day deals is, as they say, built different. For starters, we only include products someone from our team has personally tested and reviewed. That means you will not find any flimsy fad gadgets or shoddy dupes among our recommendations. What remains is all solid stuff. You'll often find a link to a longer write-up to a review or buying guide if you want to make a fully informed buying decision. Additionally, we obsessively track prices to make sure everything on the list is a genuinely good price right now. For more on that, consult our helpful guide to shopping like a pro on Prime Day. We test products year-round and handpicked these Prime Day deals. Products that are sold out or no longer discounted will be crossed out. We'll update this guide regularly throughout Prime Day by adding fresh deals and removing dead deals. Logitech makes a lot of great, functional keyboards, but the Pop Keys (9/10, WIRED Recommends) not only leverage the ...

It's a great time to get the Apple Watch Series 9 if you've been planning to buy one -- the smartwatch is currently on sale for 280 for Amazon Prime Day. That's a pretty impressive 119 discount as part of Prime Day deals, and it's even 15 lower than its previous all-time low, but the deal appears to be specifically for the smaller 41mm model. It's our best smartwatch for 2024 so far, though it first became available in September last year. We gave the Apple Watch Series 9 a score of 92 in our review and praised its on-device Siri for having the capability to work offline and even when the wearable isn't connected to a phone. If you don't want to take your phone with you to the gym, for instance, that's no problem: You'll still be able to ask Siri on your watch for health data, like the number of steps you'd already taken.

Amazon Prime Day is finally here, and you can score excellent discounts on most of Amazon's own devices. One deal of note is on the Echo Show 5, which you can snag for only 50 right now. The Echo Show 8 is also on sale for 85, which is a new record low. The Echo Show 5 easily made our list of the best smart displays, for a great many reasons. The 5.5-inch screen is diminutive, especially when compared to the Echo Show 8, but that just makes it fit better on a desk or nightstand.

In this paper we describe NudgeRank, an innovative digital algorithmic nudging system designed to foster positive health behaviors on a population-wide scale. Utilizing a novel combination of Graph Neural Networks augmented with an extensible Knowledge Graph, this Recommender System is operational in production, delivering personalized and context-aware nudges to over 1.1 million care recipients daily. This enterprise deployment marks one of the largest AI-driven health behavior change initiatives, accommodating diverse health conditions and wearable devices. Rigorous evaluation reveals statistically significant improvements in health outcomes, including a 6.17% increase in daily steps and 7.61% more exercise minutes. Moreover, user engagement and program enrollment surged, with a 13.1% open rate compared to baseline systems' 4%. Demonstrating scalability and reliability, NudgeRank operates efficiently on commodity compute resources while maintaining automation and observability standards essential for production systems.

Within the domain of short video recommendation, predicting users' watch time is a critical but challenging task. Prevailing deterministic solutions obtain accurate debiased statistical models, yet they neglect the intrinsic uncertainty inherent in user environments. In our observation, we found that this uncertainty could potentially limit these methods' accuracy in watch-time prediction on our online platform, despite that we have employed numerous features and complex network architectures. Consequently, we believe that a better solution is to model the conditional distribution of this uncertain watch time. In this paper, we introduce a novel estimation technique -- Conditional Quantile Estimation (CQE), which utilizes quantile regression to capture the nuanced distribution of watch time. The learned distribution accounts for the stochastic nature of users, thereby it provides a more accurate and robust estimation. In addition, we also design several strategies to enhance the quantile prediction including conditional expectation, conservative estimation, and dynamic quantile combination. We verify the effectiveness of our method through extensive offline evaluations using public datasets as well as deployment in a real-world video application with over 300 million daily active users.

Amazon Prime Day is a chance for Prime members to pick up all sorts of things on sale, and while most of the discounts aren't worth your time, those on gadgets actually can be. Prime Day deals have discounted plenty of our favorites in the past, and this year is shaping up to be no different. If any of the items on your list happen to be tech or tech related, Engadget has you covered. We've scoured Amazon to find the Prime Day deals on tech and gadgets that you can get this year. And the good news is that not all of the discounts are on high-priced items.

Cross-Domain Sequential Recommendation (CDSR) improves recommendation performance by utilizing information from multiple domains, which contrasts with Single-Domain Sequential Recommendation (SDSR) that relies on a historical interaction within a specific domain. However, CDSR may underperform compared to the SDSR approach in certain domains due to negative transfer, which occurs when there is a lack of relation between domains or different levels of data sparsity. To address the issue of negative transfer, our proposed CDSR model estimates the degree of negative transfer of each domain and adaptively assigns it as a weight factor to the prediction loss, to control gradient flows through domains with significant negative transfer. To this end, our model compares the performance of a model trained on multiple domains (CDSR) with a model trained solely on the specific domain (SDSR) to evaluate the negative transfer of each domain using our asymmetric cooperative network. In addition, to facilitate the transfer of valuable cues between the SDSR and CDSR tasks, we developed an auxiliary loss that maximizes the mutual information between the representation pairs from both tasks on a per-domain basis. This cooperative learning between SDSR and CDSR tasks is similar to the collaborative dynamics between pacers and runners in a marathon. Our model outperformed numerous previous works in extensive experiments on two real-world industrial datasets across ten service domains. We also have deployed our model in the recommendation system of our personal assistant app service, resulting in 21.4% increase in click-through rate compared to existing models, which is valuable to real-world business.

The modeling of users' behaviors is crucial in modern recommendation systems. A lot of research focuses on modeling users' lifelong sequences, which can be extremely long and sometimes exceed thousands of items. These models use the target item to search for the most relevant items from the historical sequence. However, training lifelong sequences in click through rate (CTR) prediction or personalized search ranking (PSR) is extremely difficult due to the insufficient learning problem of ID embedding, especially when the IDs in the lifelong sequence features do not exist in the samples of training dataset. Additionally, existing target attention mechanisms struggle to learn the multi-modal representations of items in the sequence well. The distribution of multi-modal embedding (text, image and attributes) output of user's interacted items are not properly aligned and there exist divergence across modalities. We also observe that users' search query sequences and item browsing sequences can fully depict users' intents and benefit from each other. To address these challenges, we propose a unified lifelong multi-modal sequence model called SEMINAR-Search Enhanced Multi-Modal Interest Network and Approximate Retrieval. Specifically, a network called Pretraining Search Unit (PSU) learns the lifelong sequences of multi-modal query-item pairs in a pretraining-finetuning manner with multiple objectives: multi-modal alignment, next query-item pair prediction, query-item relevance prediction, etc. After pretraining, the downstream model restores the pretrained embedding as initialization and finetunes the network. To accelerate the online retrieval speed of multi-modal embedding, we propose a multi-modal codebook-based product quantization strategy to approximate the exact attention calculati

With rapid advances in machine learning, many people in the field have been discussing the rise of digital minds and the possibility of artificial sentience. Future developments in AI capabilities and safety will depend on public opinion and human-AI interaction. To begin to fill this research gap, we present the first nationally representative survey data on the topic of sentient AI: initial results from the Artificial Intelligence, Morality, and Sentience (AIMS) survey, a preregistered and longitudinal study of U.S. public opinion that began in 2021. Across one wave of data collection in 2021 and two in 2023 (total N = 3,500), we found mind perception and moral concern for AI well-being in 2021 were higher than predicted and significantly increased in 2023: for example, 71% agree sentient AI deserve to be treated with respect, and 38% support legal rights. People have become more threatened by AI, and there is widespread opposition to new technologies: 63% support a ban on smarter-than-human AI, and 69% support a ban on sentient AI. Expected timelines are surprisingly short and shortening with a median forecast of sentient AI in only five years and artificial general intelligence in only two years. We argue that, whether or not AIs become sentient, the discussion itself may overhaul human-computer interaction and shape the future trajectory of AI technologies, including existential risks and opportunities.