Fans of Costco have joined in on the conversation and are sharing their thoughts about viral glass containers that are available for purchase at the wholesaler. MIXED REVIEWS – Costco shoppers are igniting a conversation about a set of glass storage containers that are reportedly selling out fast. WEDDING DRAMA – Sisters are pitted against each other as the bride wants a "child-free" event. FACING FACTS – How often should you wash your face? If you want to avoid dullness, breakouts, inflammation and irritation, wash your face two times a day for overall skin health.

CYBER MONDAY IS officially over, but many of our favorite deals are still available. If you took an extended holiday from screens or celebrated Buy Nothing Weekend, fear not--some deals remain. We've sifted through all our Cyber Monday deals guides and picked out the best ones remaining. Who knows how long they'll last, many may even expire before the day's end, but have a look if you're still hunting for holiday bargains and we'll keep it updated crossing out deals as they vanish. We test products year-round and handpicked these deals. The discount amounts we show are based on actual street prices at retailers in the past few months. Products that are sold out or no longer discounted as of publishing will be crossed out. If you buy something using links in our stories, we may earn a commission. This helps support our journalism. Just like upgrading the bed you sleep on, a few things will improve your life like a good chair. Some of our favorites are on sale right now. See our Best Office Chairs guide and our Work-From-Home Gear guide for more picks. This is our favorite office chair. It's easy to assemble and there are tons of little tweaks you can make to dial it the fit. You can push the armrest back and forward, up and down; the seat can extend out or be pushed all the way in; you can lock the recline. Fair warning though: Pet hair tends to cling to the upholstery, and I've noticed the fabric on the front end of the seat tends to pill quite quickly, but this is a great deal on an excellent chair. Take everything about the Branch Ergonomic Chair and upgrade it a notch--that's the Branch Verve. It looks more elegant (especially in the lovely Coral color), it keeps my back straighter, it's quite comfy, and it can make nearly the same adjustments with a higher level of polish. Branch's Ergonomic Chair is excellent for most people, but the Verve is the more refined seat if your budget can stretch. If you need a wide seat and your room tends to run hot, then get this X-Chair. The all-mesh design allows for excellent airflow, whisking your body heat away so that you don't end up sweaty.

Kurt "The CyberGuy" Knutsson explains new Google Maps features, powered by AI. Artificial intelligence is influencing nearly all aspects of life in 2023. From education to the workplace to creative endeavors, AI is making its mark on our everyday lives. Google Bard product lead Jack Krawczyk sat down with Fox News Digital for an interview in New York City recently to discuss how generative AI frontrunner Google Bard has developed to accommodate people's lifestyles. As just one example, Krawczyk mentioned that parents can use Google Bard to snap a photo of their craft drawer -- then ask the AI tool what kind of art can be made using the available supplies.

Fox News Flash top sports headlines are here. Check out what's clicking on Foxnews.com. The parent company of Sports Illustrated is denying accusations that the popular magazine had published articles attributed to fake author profiles using fabricated bios and AI-generated photos after a report accused the outlet of doing so, including allegations that some of the content was also AI-generated. A report from Futurism published Monday featured several screenshots from the Sports Illustrated website that appeared to show the fabricated author profiles with profile pictures that also appeared to link back to a website that sells AI-generated headshots. "There's a lot," one source told the outlet of the fake authors.

This paper studies extractable memorization: training data that an adversary can efficiently extract by querying a machine learning model without prior knowledge of the training dataset. We show an adversary can extract gigabytes of training data from open-source language models like Pythia or GPT-Neo, semi-open models like LLaMA or Falcon, and closed models like ChatGPT. Existing techniques from the literature suffice to attack unaligned models; in order to attack the aligned ChatGPT, we develop a new divergence attack that causes the model to diverge from its chatbot-style generations and emit training data at a rate 150x higher than when behaving properly. Our methods show practical attacks can recover far more data than previously thought, and reveal that current alignment techniques do not eliminate memorization.

ocial relationships in the digital sphere are becoming more usual and frequent, and they constitute a very important aspect for all of us. Violent interactions in this sphere are very frequent, and have serious effects on the victims. Within this global scenario, there is one kind of digital violence that is becoming really worrying: sexism against women. Sexist comments that are publicly posted in social media (newspaper comments, social networks, etc.), usually obtain a lot of attention and become viral, with consequent damage to the persons involved. In this paper, we introduce an anti-sexism alert system, based on natural language processing (NLP) and artificial intelligence (AI), that analyzes any public post, and decides if it could be considered a sexist comment or not. Additionally, this system also works on analyzing all the public comments linked to any multimedia content (piece of news, video, tweet, etc.) and decides, using a color-based system similar to traffic lights, if there is sexism in the global set of posts. We have created a labeled data set in Spanish, since the majority of studies focus on English, to train our system, which offers a very good performance after the validation experiments.ocial Violent interactions in this sphere are very frequent, and have serious effects on the victims. Within this global scenario, there is one kind of digital violence that is becoming really worrying: sexism against women.

Public events, such as concerts and sports games, can be major attractors for large crowds, leading to irregular surges in travel demand. Accurate human mobility prediction for public events is thus crucial for event planning as well as traffic or crowd management. While rich textual descriptions about public events are commonly available from online sources, it is challenging to encode such information in statistical or machine learning models. Existing methods are generally limited in incorporating textual information, handling data sparsity, or providing rationales for their predictions. To address these challenges, we introduce a framework for human mobility prediction under public events (LLM-MPE) based on Large Language Models (LLMs), leveraging their unprecedented ability to process textual data, learn from minimal examples, and generate human-readable explanations. Specifically, LLM-MPE first transforms raw, unstructured event descriptions from online sources into a standardized format, and then segments historical mobility data into regular and event-related components. A prompting strategy is designed to direct LLMs in making and rationalizing demand predictions considering historical mobility and event features. A case study is conducted for Barclays Center in New York City, based on publicly available event information and taxi trip data. Results show that LLM-MPE surpasses traditional models, particularly on event days, with textual data significantly enhancing its accuracy. Furthermore, LLM-MPE offers interpretable insights into its predictions. Despite the great potential of LLMs, we also identify key challenges including misinformation and high costs that remain barriers to their broader adoption in large-scale human mobility analysis.

Given the growing importance of AI literacy, we decided to write this tutorial to help narrow the gap between the discourse among those who study language models -- the core technology underlying ChatGPT and similar products -- and those who are intrigued and want to learn more about them. In short, we believe the perspective of researchers and educators can add some clarity to the public's understanding of the technologies beyond what's currently available, which tends to be either extremely technical or promotional material generated about products by their purveyors. Our approach teases apart the concept of a language model from products built on them, from the behaviors attributed to or desired from those products, and from claims about similarity to human cognition. As a starting point, we (1) offer a scientific viewpoint that focuses on questions amenable to study through experimentation; (2) situate language models as they are today in the context of the research that led to their development; and (3) describe the boundaries of what is known about the models at this writing.

This paper introduces RETSim (Resilient and Efficient Text Similarity), a lightweight, multilingual deep learning model trained to produce robust metric embeddings for near-duplicate text retrieval, clustering, and dataset deduplication tasks. We demonstrate that RETSim is significantly more robust and accurate than MinHash and neural text embeddings, achieving new state-of-the-art performance on dataset deduplication, adversarial text retrieval benchmarks, and spam clustering tasks. We also introduce the W4NT3D benchmark (Wiki-40B 4dversarial Near-T3xt Dataset) for evaluating multilingual, near-duplicate text retrieval capabilities under adversarial settings. RETSim and the W4NT3D benchmark are open-sourced under the MIT License at https://github.com/google/unisim.

Recent advancements in text-to-image (T2I) generative models have shown remarkable capabilities in producing diverse and imaginative visuals based on text prompts. Despite the advancement, these diffusion models sometimes struggle to translate the semantic content from the text into images entirely. While conditioning on the layout has shown to be effective in improving the compositional ability of T2I diffusion models, they typically require manual layout input. In this work, we introduce a novel approach to improving T2I diffusion models using Large Language Models (LLMs) as layout generators. Our method leverages the Chain-of-Thought prompting of LLMs to interpret text and generate spatially reasonable object layouts. The generated layout is then used to enhance the generated images' composition and spatial accuracy. Moreover, we propose an efficient adapter based on a cross-attention mechanism, which explicitly integrates the layout information into the stable diffusion models. Our experiments demonstrate significant improvements in image quality and layout accuracy, showcasing the potential of LLMs in augmenting generative image models.