Last week, Mark Zuckerberg officially announced that his company was changing its name from Facebook to Meta, with a prominent new focus on creating the metaverse. A defining feature of this metaverse will be creating a feeling of presence in the virtual world. Presence could mean simply interacting with other avatars and feeling like you are immersed in a foreign landscape. Or, it could even involve engineering some sort of haptic feedback for users when they touch or interact with objects in the virtual world. As part of all this, a division of Meta called Meta AI wants to help machines learn how humans touch and feel by using a robot finger sensor called DIGIT, and a robot skin called ReSkin.

The use of natural language and voice-based interfaces gradu-ally transforms how consumers search, shop, and express their preferences. The current work explores how changes in the syntactical structure of the interaction with conversational interfaces (command vs. request based expression modalities) negatively affects consumers' subjective task enjoyment and systematically alters objective vocal features in the human voice. We show that requests (vs. commands) lead to an in-crease in phonetic convergence and lower phonetic latency, and ultimately a more natural task experience for consumers. To the best of our knowledge, this is the first work docu-menting that altering the input modality of how consumers interact with smart objects systematically affects consumers' IoT experience. We provide evidence that altering the required input to initiate a conversation with smart objects provokes systematic changes both in terms of consumers' subjective experience and objective phonetic changes in the human voice. The current research also makes a methodological con-tribution by highlighting the unexplored potential of feature extraction in human voice as a novel data format linking consumers' vocal features during speech formation and their sub-jective task experiences.

We study the problem of the identification of m arms with largest means under a fixed error rate $\delta$ (fixed-confidence Top-m identification), for misspecified linear bandit models. This problem is motivated by practical applications, especially in medicine and recommendation systems, where linear models are popular due to their simplicity and the existence of efficient algorithms, but in which data inevitably deviates from linearity. In this work, we first derive a tractable lower bound on the sample complexity of any $\delta$-correct algorithm for the general Top-m identification problem. We show that knowing the scale of the deviation from linearity is necessary to exploit the structure of the problem. We then describe the first algorithm for this setting, which is both practical and adapts to the amount of misspecification. We derive an upper bound to its sample complexity which confirms this adaptivity and that matches the lower bound when $\delta$ $\rightarrow$ 0. Finally, we evaluate our algorithm on both synthetic and real-world data, showing competitive performance with respect to existing baselines.

The fashion industry is on the verge of an unprecedented change. The implementation of machine learning, computer vision, and artificial intelligence (AI) in fashion applications is opening lots of new opportunities for this industry. This paper provides a comprehensive survey on this matter, categorizing more than 580 related articles into 22 well-defined fashion-related tasks. Such structured task-based multi-label classification of fashion research articles provides researchers with explicit research directions and facilitates their access to the related studies, improving the visibility of studies simultaneously. For each task, a time chart is provided to analyze the progress through the years. Furthermore, we provide a list of 86 public fashion datasets accompanied by a list of suggested applications and additional information for each.

Jarvis, please pull up some quick articles to teach me about AI…Jarvis? Oh wait, my bad, I forgot that you're not real outside of Marvel. Please excuse me, I'm just going to go sob in the corner while Siri tells me she "didn't quite get that" in an endless, torturous loop. If you're the singular person on Earth who has never seen an MCU movie and you didn't quite get that, absolutely no worries (but I hope you move to a more exciting rock soon)! I'm messing with you, here's the rundown: J.A.R.V.I.S. is a fictional AI system created by billionaire genius Tony Stark, essentially a virtual assistant that can do anything from making predictions from enormous piles of data to mimicking human language (and occasionally cracking a joke), which we'll soon see is harder than it seems! Right now, some of you may be thinking WTF (Well, That's Fantastic), but I don't know what this has to do with anything? If you haven't already guessed it, today we are going to be learning about AI, i.e. Artificial Intelligence (which is what our dear J.A.R.V.I.S. is)! Let's get right into it: what exactly is AI?

Hackers have taken the records from an LGBTQ dating site, used primarily by gay men, in Israel -- threatening to expose its users unless they are paid.

It's Monday, and your coworkers are digging into a long, grueling database project. If you're nice, you'll bring them coffee and bagels. But if you're feeling less charitable, there's always an animated Clippy sticker to help get their week started off on the wrong foot. Microsoft recently confirmed that, yes, you can pull a number of animated Clippy images from within Microsoft Teams. In case you're too young to remember Clippy, the animated paperclip was introduced to Microsoft Word in 1996 as an "office assistant," and is unfondly remembered as a precursor to virtual assistants like Siri and the Google Assistant.

Smart homes are no longer a luxury. They are becoming a natural choice for people who want to enjoy more comfortable, convenient, and safe living spaces. In recent years, artificial intelligence has become a constant and welcomed presence in houses around the world. AI technology has added an extra level of safety and security while allowing people to enjoy a more pleasant way of living. Due to the increased demand for smart homes, home automation tools are now more affordable, and smart household appliances are a must for technology aficionados.

Due to a large amount of information, it is difficult for users to find what they are interested in among the many choices. In order to improve users' experience, recommendation systems have been widely used in music recommendations, movie recommendations, online shopping, and other scenarios. Recently, Knowledge Graph (KG) has been proven to be an effective tool to improve the performance of recommendation systems. However, a huge challenge in applying knowledge graphs for recommendation is how to use knowledge graphs to obtain better user codes and item codes. In response to this problem, this research proposes a user Recurrent Neural Network (RNN) encoder and item encoder recommendation algorithm based on Knowledge Graph (URIR). This study encodes items by capturing high-level neighbor information to generate items' representation vectors and applies an RNN and items' representation vectors to encode users to generate users' representation vectors, and then perform inner product operation on users' representation vectors and items' representation vectors to get probabilities of users interaction with items. Numerical experiments on three real-world datasets demonstrate that URIR is superior performance to state-of-the-art algorithms in indicators such as AUC, Precision, Recall, and MRR. This implies that URIR can effectively use knowledge graph to obtain better user codes and item codes, thereby obtaining better recommendation results.

The invention of artificial intelligence has had the biggest effect on the world since electricity. And just like electricity, it will have a profound impact on virtually every sphere of human endeavor, from warfare to medicine to music. We love to use it in our technology, but artificial intelligence is everywhere. Most of these examples are things we take for granted because they have become so integrated into the fabric of our everyday lives. Artificial intelligence is all around us. We can find it in our homes, in our cars, and even in our relationships. We may not always think about the artificial intelligence that's buzzing around us because it requires an explicit action to activate it. With just a few clicks of the mouse or taps on your phone, artificial intelligence can do everything from sorting your laundry to directing your car. Artificial intelligence is both helpful and scary. From self-driving cars to analyzing medical data, artificial intelligence (AI) is already present in many aspects of our daily lives. While AI may conjure up images of humanoid robots and the "Terminator" movies, the reality is far more mundane. The idea of artificial intelligence can intimidate, but it doesn't need to be scary. AI is simply technology that can make our lives easier. For instance, AI systems handle over half of the United States stock market trades.