Google is adding new games to Assistant-enabled smart displays like the Nest Hub to give people something new to play in time for the holidays. The additions fit broadly into three categories. In the first one, you'll find word games like Voice Quest. Here you'll play as a mage that casts spells using synonyms. In another new experience called Game of Words you'll have six letters with which you need to make as many words as possible.

Rejoice! Apple Music is finally available on Google Assistant-enabled smart speakers and displays like the Nest Audio and Nest Hub Max. Previously, only YouTube Music, Spotify, Pandora, and Deezer were able to be played natively on Google speakers. The addition of Apple Music to Google speakers is a welcome change for veteran iOS users who have previously relied on Bluetooth as a workaround to play from Apple's catalog of 70 million songs on Google Home speakers. Without further ado, here's how to start rocking out to Apple Music directly from Google speakers. Here's a look at how to link your Apple Music account in the Google Home app.

Google has announced that it's no longer making its music-focused Home Max smart speaker, which is--or was--the best-sounding smart speaker on the market. In a statement provided to TechHive, Google says that the $299 Home Max will continue to work normally, and that it will still offer software and security patches for the device. The statement also points smart speaker shoppers toward the Nest Audio, a new music-focused speaker with a smaller design and a more palatable $99 price tag. We've sold out of Google Home Max and will no longer be manufacturing the device. With the launch of Nest Audio, we're offering a range of great home audio solutions, particularly with two Nest Audios paired for stereo sound.

Although there are many good reasons to remember year 2020, but we have to accept the sad reality i.e. COVID-19 as well which is the major downside of same year. The best parts are the automation and the enhancement of customer experience through Companies are getting advanced in many ways to provide tailored and elegant solutions. Technologies like 5G (6G and Quantum computing are little far for now), cutting-edge medical diagnostics systems, real-time -powered e-commerce, consumer electronics, and smart personal assistants are few examples. 's bundle of joy (Machine learning and its advancement to and neural network) as horizontal scaling techniques have taken almost every other businesses and technology to the next level.

Spectral methods have emerged as a simple yet surprisingly effective approach for extracting information from massive, noisy and incomplete data. In a nutshell, spectral methods refer to a collection of algorithms built upon the eigenvalues (resp. singular values) and eigenvectors (resp. singular vectors) of some properly designed matrices constructed from data. A diverse array of applications have been found in machine learning, data science, and signal processing. Due to their simplicity and effectiveness, spectral methods are not only used as a stand-alone estimator, but also frequently employed to initialize other more sophisticated algorithms to improve performance. While the studies of spectral methods can be traced back to classical matrix perturbation theory and methods of moments, the past decade has witnessed tremendous theoretical advances in demystifying their efficacy through the lens of statistical modeling, with the aid of non-asymptotic random matrix theory. This monograph aims to present a systematic, comprehensive, yet accessible introduction to spectral methods from a modern statistical perspective, highlighting their algorithmic implications in diverse large-scale applications. In particular, our exposition gravitates around several central questions that span various applications: how to characterize the sample efficiency of spectral methods in reaching a target level of statistical accuracy, and how to assess their stability in the face of random noise, missing data, and adversarial corruptions? In addition to conventional $\ell_2$ perturbation analysis, we present a systematic $\ell_{\infty}$ and $\ell_{2,\infty}$ perturbation theory for eigenspace and singular subspaces, which has only recently become available owing to a powerful "leave-one-out" analysis framework.

In recent years, the field of recommendation systems has attracted increasing attention to developing predictive models that provide explanations of why an item is recommended to a user. The explanations can be either obtained by post-hoc diagnostics after fitting a relatively complex model or embedded into an intrinsically interpretable model. In this paper, we propose the explainable recommendation systems based on a generalized additive model with manifest and latent interactions (GAMMLI). This model architecture is intrinsically interpretable, as it additively consists of the user and item main effects, the manifest user-item interactions based on observed features, and the latent interaction effects from residuals. Unlike conventional collaborative filtering methods, the group effect of users and items are considered in GAMMLI. It is beneficial for enhancing the model interpretability, and can also facilitate the cold-start recommendation problem. A new Python package GAMMLI is developed for efficient model training and visualized interpretation of the results. By numerical experiments based on simulation data and real-world cases, the proposed method is shown to have advantages in both predictive performance and explainable recommendation.

Starting today, Alexa can translate conversations between two people who speak different languages. At launch, the digital assistant's new Live Translation feature works on Echo devices in the US and supports translation between English and French, German, Hindi, Italian, Spanish or Brazilian Portuguese. To start using the feature, ask Alexa to serve as an interpreter for the language that the person you want to talk to speaks. As the two of you converse, Alexa will automatically identify the person who's taking and translate as needed. If you own an Echo Show, you'll see a visual translation in addition to hearing an audio one.

You've heard so much about the new iPhones, and you want to get one. But seven models to choose from? And how to make your decision? "Upgrading from the XS Max soon," Wisconsin photographer Kevin Scott noted on Facebook. "Do you feel 12 Pro is worth the extra over the 12?" That was just one of the many questions we received during the holidays, which boiled down to variations of "Which iPhone should I buy?" and "Is the discounted Apple Watch a good buy?" You've come to the right place.

If you've spoken to Amazon's Alexa voice assistant through the Alexa app or an Echo device, you may have wondered who the woman is behind the speaker. Do you think that Alexa is voiced by a celebrity or an Amazon employee? You might be surprised to know that the voice of Alexa is not formed from any real person. Rather, Alexa's voice is generated by artificial intelligence. Alexa's voice was developed using special software that evolved from text-to-speech technology.

Alexa can tell you the weather, turn on your lights and even tell you a joke. But if you really want to have a meaningful conversation with a computer, it is probably going to have to be a quantum computer. Cambridge Quantum Computing (CQC), the 2014-founded startup, says it has built "meaning-aware" natural language processing on a quantum computer. The system understands both grammatical structure and the meaning of words, in a way that classical computers cannot. "This is quantum native, it cannot be done with a classical computer with a reasonable amount for resources."