It's been recently discovered that the Google Home smart speaker is now able to show visual information on TVs that have a connected Chromecast. This is the very first of the contextual visual responses that Google previously promised last year for its Home smart speaker. When users want to hear information about the weather, all they have to do is say "OK Google what's the weather?" and listen to Google Assistant's response. With Google's new contextual visual responses activated, users can now tell Google Home to show them weather information by asking "OK Google show me the weather on my TV" or simply "show me the weather," according to Android Police. Google Assistant will show the weather information on the user's TV as long as a Chromecast device is connected to the TV.

What is a Voice Interface? Ranging from Apple's Siri, Amazon's Alexa and Microsoft's Cortana the use of voice-based communication through devices like a Mobile App is in demand. You might be doubtful with written instructions on the computer but the use of voice interface communication has exceeded the user rate is spreading virally like social media news. Several Google-based software assistants innovated in progress are developed as voice communicating tools falling into the category of VUIs. Voices fed in through a program are heard when a logical yes is read by the sensors which serve as a voice interface with certain devices running it.

Banking providers must continue to step up to plate and meet consumer expectations. Digital banking solutions are table stakes, not something only tech-savvy Millennials crave. Subscribe to The Financial Brand via email for FREE!A crowded marketplace and the speed at which new technologies are revolutionizing banking makes crafting the right digital presence overwhelming for many financial institutions. Consumers now expect their banking provider to provide a secure, frictionless digital experience -- one that's easy to use, convenient and fits seamlessly into their everyday lives. With competition increasing across the industry, the stakes are high.

In my last article I mentioned that Google and Facebook are the leaders in the artificial intelligence gold rush era we are now living in. I always was a Google guy, my first phone was an Android phone, I got the Chromecast went it first came out, I learned how to code watching YouTube videos and I am a Google Drive paying customer so one should read this article knowing that I have a preference for Alphabet products but in this fake news and paid influencers era please believe me I am not getting paid in any way by Google or any of its subsidiary corporations. I have a preference for its products like any of you reading this article have his or her preference for Facebook as a social media or Apple for its products instead of Android. Nevertheless I always try to be objective in my opinions especially in regard to artificial intelligence, machine learning and data science. In October 2017, Google CEO came out with a pretty strong Ai focused statement and said that "Google is now an Ai first company".

Apple has now confirmed that its HomePod smart speaker won't come with manual EQ adjustment options. This means users will not have the ability to manually alter the sound of the device to match their bass or treble preference. Apple Senior VP of Internet Software and Services Eddy Cue announced Wednesday during a Pollstar Live conference that the highly anticipated HomePod won't make it possible for users to make EQ adjustments on their own. In the absence of manual adjustment controls, the Amazon Echo rival will rely on analytics to automatically set levels for each song that is played, according to Apple Insider. The announcement could be upsetting for audiophiles and people who prefer to make manual adjustments to their music.

Alphabet's Google is arguably the first mover in using AI among its "big tech" peers -- a category from which I'm excluding IBM, which arrived early to the AI party. The company has been pouring massive sums of money into infusing AI -- particularly machine learning -- into its products and services. For instance, in its core search business, Google uses AI to improve the relevancy of the ads it shows users, and on YouTube, it employs AI to serve up music and video selections based on a users' previous listening and viewing habits. Like Amazon and Apple, the company also has an AI-powered smart assistant: Google Assistant started out its life helping Android-powered smartphone users, and is also now incorporated into the company's smart speaker, Google Home.

We investigate how to address the shortcomings of the popular One-Class Collaborative Filtering (OCCF) methods in handling challenging “sparse” dataset in one-class setting (e.g., clicked or bookmarked), and propose a novel graph-theoretic OCCF approach, named as gOCCF, by exploiting both positive preferences (derived from rated items) as well as negative preferences (derived from unrated items). In capturing both positive and negative preferences as a bipartite graph, further, we apply the graph shattering theory to determine the right amount of negative preferences to use. Then, we develop a suite of novel graph-based OCCF methods based on the random walk with restart and belief propagation methods. Through extensive experiments using 3 real-life datasets, we show that our gOCCF effectively addresses the sparsity challenge and significantly outperforms all of 8 competing methods in accuracy on very sparse datasets while providing comparable accuracy to the best performing OCCF methods on less sparse datasets. The datasets and implementations used in the empirical validation are available for access: https://goo.gl/sfiawn.

Plant-pollinator interaction networks are bipartite networks representing the mutualistic interactions between a set of plant species and a set of pollinator species. Data on these networks are collected by field biologists, who count visits from pollinators to flowers. Ecologists study the structure and function of these networks for scientific, conservation, and agricultural purposes. However, little research has been done to understand the underlying mechanisms that determine pairwise interactions or to predict new links from networks describing the species community. This paper explores the use of latent factor models to predict interactions that will occur in new contexts (e.g. a different distribution of the set of plant species) based on an observed network. The analysis draws on algorithms and evaluation strategies developed for recommendation systems and introduces them to this new domain. The matrix factorization methods compare favorably against several baselines on a pollination dataset collected in montane meadows over several years. Incorporating both positive and negative implicit feedback into the matrix factorization methods is particularly promising.

We propose a method to generate explainable recommendation rules on cross-domain problems. Our two main contributions are: i) using relational learning to generate the rules which are able to explain clearly why the items were recommended to the particular user, ii) using the user's preferences of items on different domains and item attributes to generate novel or unexpected recommendations for the user. To illustrate that our method is indeed feasible and applicable, we conducted experiments on music and movie domains.

The purpose of this study is to design a machine learning approach to predict the student response in mixed-format tests. Particularly, a novel contextual collaborative filtering model is proposed to extract latent factors for students and test items, by exploiting the item information. Empirical results from a simulation study validate the effectiveness of the proposed method.