Getting too much email can make things messed up for you to manage and due to this you can miss your important emails, newsletters and offers. But there's a good for you if you are a owner of Amazon Alexa. Yes, with the last year's upgradation your assistant is now able to read and check your emails. Now you must think how you can enable Alexa's email checking features. For that you need to go through some simple set up.

Did you know that your Google Assistant can work as an excellent smart alarm to make your day better? It can make you awake with your favorite music, radio or news. Additionally you can set morning and night time routines by pairing alarm with your playlists. It absolutely doesn't matter which model of Google assistant smart speaker You are using, you can make it work with any of them. So let's get straight to how you can use Google Home and Assistant as a smart alarm clock.

We consider a special case of bandit problems, namely batched bandits. Motivated by natural restrictions of recommender systems and e-commerce platforms, we assume that a learning agent observes responses batched in groups over a certain time period. Unlike previous work, we consider a more practically relevant batch-centric scenario of batch learning. We provide a policy-agnostic regret analysis and demonstrate upper and lower bounds for the regret of a candidate policy. Our main theoretical results show that the impact of batch learning can be measured in terms of online behavior. Finally, we demonstrate the consistency of theoretical results by conducting empirical experiments and reflect on the optimal batch size choice.

When humans invented the concept of AI years ago, the end goal was to stimulate human-like intelligence. Little did they know that AI would turn out to be a breakthrough in the realm of technology. When a non-techie is subjected to the word "AI", his thought goes straight to digital assistants like Siri, Google Maps, a chatbot they conversed with while ordering food online, or even self-driving cars like Tesla. What they fail to understand is that AI goes way beyond that. From helping in fraud detection to diagnosing symptoms of some deadly illness, AI does not leave any stone unturned in helping mankind.

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.