At least, that was my takeaway after my first chat with the founder of Rabbit Inc., a new AI startup debuting a pocket-friendly device called the R1 at CES 2024. Instead of taking out your smartphone to complete some task, hunting for the right app, and then tapping around inside it, Lyu wants us to just ask the R1 via a push-to-talk button. Then a series of automated scripts called "rabbits" will carry out the task so you can go about your day. The R1 is a red-orange, square-ish device about the size of a stack of Post-It notes. It was designed in collaboration with the Swedish firm Teenage Engineering.

Google is expanding its smart home integration at CES 2024. The company said Tuesday that, in the future, LG TVs and some Google TV (and other Android TV) products will work as Google Home hubs. Considering Google's support for the Matter smart home standard, the move could make it easier for customers to set up and control their smart homes without buying a Nest device. "In the future, LG TVs and select Google TV and other Android TV OS devices will act as hubs for Google Home," Google Android VP Sameer Samat wrote in today's announcement blog post. "So if you have a Nest Hub, Nest Mini or compatible TV, it's easy to add Matter devices to your home network and locally control them with the Google Home app."

How to Do It is Slate's sex advice column. Send it to Stoya and Rich here. As a sexually dominant-leaning female, I get a lot of instant gratification out of gorgeous women online telling me my assertiveness is impressive and sexy. When I have sex with women in my dreams, it's perfect. While my "traditional" long-term relationships have been with male-presenting people, I slept with several women in my early 20s--though I struggled to find satisfying connections.

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Modeling long user histories plays a pivotal role in enhancing recommendation systems, allowing to capture users' evolving preferences, resulting in more precise and personalized recommendations. In this study, we tackle the challenges of modeling long user histories for preference understanding in natural language. Specifically, we introduce a new User Embedding Module (UEM) that efficiently processes user history in free-form text by compressing and representing them as embeddings, to use them as soft prompts to a LM. Our experiments demonstrate the superior capability of this approach in handling significantly longer histories compared to conventional text-based methods, yielding substantial improvements in predictive performance. Models trained using our approach exhibit substantial enhancements, with up to 0.21 and 0.25 F1 points improvement over the text-based prompting baselines. The main contribution of this research is to demonstrate the ability to bias language models via user signals.

In today's data-rich environment, recommender systems play a crucial role in decision support systems. They provide to users personalized recommendations and explanations about these recommendations. Embedding-based models, despite their widespread use, often suffer from a lack of interpretability, which can undermine trust and user engagement. This paper presents an approach that combines embedding-based and semantic-based models to generate post-hoc explanations in recommender systems, leveraging ontology-based knowledge graphs to improve interpretability and explainability. By organizing data within a structured framework, ontologies enable the modeling of intricate relationships between entities, which is essential for generating explanations. By combining embedding-based and semantic based models for post-hoc explanations in recommender systems, the framework we defined aims at producing meaningful and easy-to-understand explanations, enhancing user trust and satisfaction, and potentially promoting the adoption of recommender systems across the e-commerce sector.

Huge embedding tables in modern Deep Learning Recommender Models (DLRM) require prohibitively large memory during training and inference. Aiming to reduce the memory footprint of training, this paper proposes FIne-grained In-Training Embedding Dimension optimization (FIITED). Given the observation that embedding vectors are not equally important, FIITED adjusts the dimension of each individual embedding vector continuously during training, assigning longer dimensions to more important embeddings while adapting to dynamic changes in data. A novel embedding storage system based on virtually-hashed physically-indexed hash tables is designed to efficiently implement the embedding dimension adjustment and effectively enable memory saving. Experiments on two industry models show that FIITED is able to reduce the size of embeddings by more than 65% while maintaining the trained model's quality, saving significantly more memory than a state-of-the-art in-training embedding pruning method. On public click-through rate prediction datasets, FIITED is able to prune up to 93.75%-99.75% embeddings without significant accuracy loss. Huge embedding tables in modern Deep Learning Recommendation Models (DLRM) reach terabytes in size (Lian et al., 2022). Training DLRMs usually requires model parallelism (Ivchenko et al., 2022; Sethi et al., 2023), but even with embedding tables distributed over multiple compute nodes, memory still proves a scarce resource (Lian et al., 2022). Reducing the memory cost of embedding tables is crucial to enable efficient model training and deployment of DLRM and allow for sustainable model development. The size of an embedding table is determined by the number of rows (i.e., hash size), the number of columns (i.e., embedding dimension), and the size of each value in the embedding.

At CES 2024 this week, Philips teased its first-ever smart deadbolt that works using a touch-free palm reading system that allows homeowners to unlock their front doors. The Philips 5000 Series Wi-Fi Palm Recognition Smart Deadbolt, will go on sale in the US early this year and will retail for 360. The deadbolt will join the Philips home security smart lock product lineup and will integrate with the Phillips Home Access app where users can remotely control the lock system through smart home assistants like Amazon Alexa or Google Assistant. It'll also have built-in Wi-Fi that makes it easier to pair and link to other smart devices. The system works by automatically detecting unique palm vein patterns through its built-in proximity sensors.

A single man from Michigan recounted in a viral video how he nearly gave up on dating entirely and went "mentally insane" after a woman he met on an online dating app committed a heist on the date, earning the nickname "Felony Melanie." After reviewing security footage from the restaurant – he's convinced he may have finally solved the mystery of what really happened and why his eye is slightly red. I may take a sabbatical from going on internet dates," influencer Ryan Michael Annese said. The date nightmare story went viral on TikTok, amassing over 3 million views. "I doubt any of you guys can top it.

Heuristics and cognitive biases are an integral part of human decision-making. Automatically detecting a particular cognitive bias could enable intelligent tools to provide better decision-support. Detecting the presence of a cognitive bias currently requires a hand-crafted experiment and human interpretation. Our research aims to explore conversational agents as an effective tool to measure various cognitive biases in different domains. Our proposed conversational agent incorporates a bias measurement mechanism that is informed by the existing experimental designs and various experimental tasks identified in the literature. Our initial experiments to measure framing and loss-aversion biases indicate that the conversational agents can be effectively used to measure the biases.