Amazon's Alexa, Apple's Siri, Microsoft's Cortana, and Samsung's Bixby may be the flag bearers of voice assistants (VAs) but the technology itself is no longer limited to megacorporations. Instead, it is finding its way to numerous enterprise-level applications. Voice assistants typically found on mobile devices like Siri and Google Assistant are examples of Voice User Interfaces (VUIs). Although VUIs have existed as early as the 1950s, greater technological challenges meant that modes of communication like typing took precedence in most business implementations. A study showed that even expert typists were not faster than modern VUIs at taking down messages.

If you've wanted to add to your Google Assistant home setup without spending too much money, Best Buy has a new way that you could do that. The retailer has the original Nest Hub smart display for $40, or $50 off its normal price. This gadget came out in 2018 and has since been replaced by the sleep-tracking, second-generation Nest Hub -- but if you're willing to skip a few new features, you can get a largely similar device for one of the best prices we've seen. We gave the original Nest Hub, formerly known as the Google Home Hub, a score of 87 when it first came out for its lovely 7-inch display, charming minimalist design and extra privacy thanks to a lack of a camera. It makes a good smart alarm clock, even if it is slightly larger than something like the Echo Show 5, but it also won't look out of place on your kitchen countertop.

With the release of Rajnikanth's widely acclaimed film Robot, there came a novel perception of the term Artificial Intelligence among the people of India. It got the audience thinking -- what is AI? Is AI a threat or an opportunity for improving society? How does Chitti's wig not fall off after all those stunts? There exists no set-in-stone definition, but one way to define AI would be the ability of a machine to demonstrate human-like intelligence. Problem-solving, reasoning, and learning are a few of the many human skills aimed to be simulated by AI machines.

Search and recommendation are the two most common approaches used by people to obtain information. They share the same goal -- satisfying the user's information need at the right time. There are already a lot of Internet platforms and Apps providing both search and recommendation services, showing us the demand and opportunity to simultaneously handle both tasks. However, most platforms consider these two tasks independently -- they tend to train separate search model and recommendation model, without exploiting the relatedness and dependency between them. In this paper, we argue that jointly modeling these two tasks will benefit both of them and finally improve overall user satisfaction. We investigate the interactions between these two tasks in the specific information content service domain. We propose first integrating the user's behaviors in search and recommendation into a heterogeneous behavior sequence, then utilizing a joint model for handling both tasks based on the unified sequence. More specifically, we design the Unified Information Search and Recommendation model (USER), which mines user interests from the integrated sequence and accomplish the two tasks in a unified way.

We propose a novel combinatorial inference framework to conduct general uncertainty quantification in ranking problems. We consider the widely adopted Bradley-Terry-Luce (BTL) model, where each item is assigned a positive preference score that determines the Bernoulli distributions of pairwise comparisons' outcomes. Our proposed method aims to infer general ranking properties of the BTL model. The general ranking properties include the "local" properties such as if an item is preferred over another and the "global" properties such as if an item is among the top $K$-ranked items. We further generalize our inferential framework to multiple testing problems where we control the false discovery rate (FDR), and apply the method to infer the top-$K$ ranked items. We also derive the information-theoretic lower bound to justify the minimax optimality of the proposed method. We conduct extensive numerical studies using both synthetic and real datasets to back up our theory.

Matrix completion is the study of recovering an underlying matrix from a sparse subset of noisy observations. Traditionally, it is assumed that the entries of the matrix are "missing completely at random" (MCAR), i.e., each entry is revealed at random, independent of everything else, with uniform probability. This is likely unrealistic due to the presence of "latent confounders", i.e., unobserved factors that determine both the entries of the underlying matrix and the missingness pattern in the observed matrix. For example, in the context of movie recommender systems -- a canonical application for matrix completion -- a user who vehemently dislikes horror films is unlikely to ever watch horror films. In general, these confounders yield "missing not at random" (MNAR) data, which can severely impact any inference procedure that does not correct for this bias. We develop a formal causal model for matrix completion through the language of potential outcomes, and provide novel identification arguments for a variety of causal estimands of interest. We design a procedure, which we call "synthetic nearest neighbors" (SNN), to estimate these causal estimands. We prove finite-sample consistency and asymptotic normality of our estimator. Our analysis also leads to new theoretical results for the matrix completion literature. In particular, we establish entry-wise, i.e., max-norm, finite-sample consistency and asymptotic normality results for matrix completion with MNAR data. As a special case, this also provides entry-wise bounds for matrix completion with MCAR data. Across simulated and real data, we demonstrate the efficacy of our proposed estimator.

The crucial component making smart technologies possible – from something as small as a ring to as large as an entire city – is the IoT. Although there are varying definitions, the term IoT is mainly used for previously'dumb' devices that didn't have an Internet connection, but that now communicate with the network independently of human action. For this reason, a smartphone isn't explicitly defined as an IoT device – although it's crammed with sensors. A connected refrigerator or microwave oven however is. Nowadays, these smart technology devices devices include billions of objects of all shapes and sizes – coffee machines, lightbulbs, driver-less trucks, wearable fitness devices, jet engines and children's smart toys – all equipped with sensors and communicating data through the Internet.

From movie recommendations on Netflix and conversations with Siri and Google Assistant to AI helping a doctor diagnose his critical heart disease, Artificial Intelligence is everywhere today. It has revolutionized the way Computer Science and significant businesses have incorporated its functionality. Simply put, AI is called the'skill of the future.' Almost every industry uses AI to increase its productivity and profits. Therefore, they require individuals with advanced Artificial Intelligence knowledge and skills.

Tencent's basic research areas include computer vision, speech recognition, natural language processing, and machine learning. Applied exploration combines the company's scenarios and business advantages to create four categories of content, games, social networking, and platform-based artificial intelligence tools. At present, Weiqi AI has become a'superb art' and its technology has also been developed by Weixin, QQ, Daily Express, and QQ music. Tencent has incorporated artificial intelligence into its operations in its quest to become "the most respected internet enterprise," as the company relies completely on artificial intelligence. It has 1 billion users on its app WeChat but has extended its reach to gaming, digital assistants, mobile payments, cloud storage, live streaming, sports, education, movies, and even self-driving cars.

The £240 ($250) Alexa-powered Echo Show 15 device boasts a 15.6-inch display that you can mount to your wall or place on your counter. Users can hang it horizontally or vertically on a wall, like a photo frame, as it displays how-to videos, recipes from the web or shows streamed from Netflix and Spotify. 'We think of it [Echo Show 15] as a kitchen TV, but much, much smarter,' said Miriam Daniel, vice president of Alexa and Echo devices. Echo Show 15 can display a live-stream from your smart doorbell, streaming services interfaces, personalized sticky notes to members of the family and much more. If you've opted to hang it from the wall and want to disable the display, users can ask Alexa to show a photo frame, and Echo Show 15 just shows photos, so it blends into the background. 'Echo Show 15 brings everything that makes your household tick into one place,' said Tom Taylor, senior vice president, Amazon Alexa.