Current chart sensations Lizzo and Billie Ellish don't stand on stage with guitars around their neck like Eric Clapton, Slash from Guns N' Roses or Bruce Springsteen did (and still do.) So what are guitar makers to do to keep their factories humming? Turn to streaming, classic rock and YouTube to reach tomorrow's guitar player. The NAMM show, a collection of music store operators, music professionals and tens of thousands of fans is concluding this weekend here, where guitars of every color and imaginable shape were on display. The goal for many guitar makers: to either get older folks to spring out more money to add even more guitars to the collection, or better yet, get tomorrow's generation excited to start playing with new shapes.

Explainable recommendation is far from being well solved partly due to three challenges. The first is the personalization of preference learning, which requires that different items/users have different contributions to the learning of user preference or item quality. The second one is dynamic explanation, which is crucial for the timeliness of recommendation explanations. The last one is the granularity of explanations. In practice, aspect-level explanations are more persuasive than item-level or user-level ones. In this paper, to address these challenges simultaneously, we propose a novel model called Hybrid Deep Embedding (HDE) for aspect-based explainable recommendations, which can make recommendations with dynamic aspect-level explanations. The main idea of HDE is to learn the dynamic embeddings of users and items for rating prediction and the dynamic latent aspect preference/quality vectors for the generation of aspect-level explanations, through fusion of the dynamic implicit feedbacks extracted from reviews and the attentive user-item interactions. Particularly, as the aspect preference/quality of users/items is learned automatically, HDE is able to capture the impact of aspects that are not mentioned in reviews of a user or an item. The extensive experiments conducted on real datasets verify the recommending performance and explainability of HDE. The source code of our work is available at \url{https://github.com/lola63/HDE-Python}

Software engineers have significant expertise to offer when building intelligent systems, drawing on decades of experience and methods for building systems that are scalable, responsive and robust, even when built on unreliable components. Systems with artificial-intelligence or machine-learning (ML) components raise new challenges and require careful engineering. We designed a new course to teach software-engineering skills to students with a background in ML. We specifically go beyond traditional ML courses that teach modeling techniques under artificial conditions and focus, in lecture and assignments, on realism with large and changing datasets, robust and evolvable infrastructure, and purposeful requirements engineering that considers ethics and fairness as well. We describe the course and our infrastructure and share experience and all material from teaching the course for the first time.

Smart assistants can make life so much easier--and Google Assistant is no exception. If you're just dipping your toes into the world of smart home, a smart speaker like the Nest Mini from Google is a fantastic place to start. The Nest Mini is a much-improved version of the Google Home Mini, but it's been given a new name and a few software upgrades. The powerful little device has 360-degree sound, three far-field microphones, and Voice Match technology that can differentiate your voice from other family members. But before you get to using that, here's how to set up your new Nest Mini.

Answering questions and managing schedules are some tasks people ask voice assistants to work for. The traditional process is slow and clunky; however, with better technology, you can eliminate difficult manual tasks. Updating and checking spreadsheets is a lot of manual work. Voice technology can replace manual work by simply asking the device to tell about the upcoming work schedule. With advances in artificial intelligence and natural language processing, it's no longer a risk to understand requests from voices and styles of speech.

Google's owner Alphabet has become a trillion-dollar company for the first time, making it only the fourth US firm to reach the bumper valuation. Alphabet's value, based on the price of its Wall Street-listed shares, passed $1tn (£776bn) in the final minutes of trading on Thursday night, with shares closing at a record high of $1,450.16 It marks a stellar rise for Alphabet, which floated as Google for $85 a share in 2004. After its initial public offering, the Silicon Valley firm was worth $23bn. It has followed its tech rivals Microsoft, Apple and Amazon over the $1tn mark, amid a long rally in so-called Faang stocks. Google's value has steadily surged as it has tightened its grip on the search market, boosted its advertising revenues from web searches and YouTube, created and grown its Android mobile operating system, and launched a series of smart-tech products including Google Home and Google Assistant.

In October 2018, months after a brief reveal, Amazon brought Whisper Mode to select third- and first-party Alexa devices. It expanded the feature to all locales in November 2019, such that all smart home appliances powered by Alexa -- the company's virtual assistant -- now respond to whispered speech by whispering back. Amazon was a bit light on the technical details initially, save that Whisper Mode uses a neural network -- layers of mathematical functions loosely modeled after the human brain's neurons -- to distinguish among normal and whispered words. But in an academic paper appearing in the January 2020 issue of the journal IEEE Signal Processing Letters and an accompanying blog post, it detailed the research that led to the expansion. The principal challenge was converting normal speech into whispered speech while maintaining naturalness and speaker identity, explained Marius Cotescu, an applied scientist in Amazon's text-to-speech research group.

China is once more tightening its grip on internet content, and this time algorithms are in the spotlight. The Cyberspace Administration of China has published upcoming rules that dictate how internet companies manage content, including a push for recommendation algorithms that promote "positive" ideas (read: government policies) while excluding "bad" material. The measure explicitly forbids content that "endangers national security, leaks state secrets, subverts state power [and] undermines national unity." The new rules are due to take effect on March 1st, and also call for tighter management of accounts, sign-ups, moderation and "rumors." Governments have lately stepped up attempts to regulate algorithms, although China's approach is very different than that from other countries.

Every day, roughly 60 percent of all market trades are made by computer. When the market turns volatile, this can climb to as high as 90 percent. Robo-advisors are increasingly making this process available to the consumer, saving them time and money as a result. With humans no longer in the transaction chain, fees are slashed. Undercutting the typical 2 percent cut of profits (not to mention 20 percent incentives) charged by a wealth manager, most robo-advisors take around 0.25 percent.

Dating apps like Tinder, OkCupid and Grindr are sharing users' "highly personal" data like sexual preferences and location with advertising partners, according to a European data protection agency. The Norwegian Consumer Council released findings on Tuesday suggesting the information you enter on dating apps is being used to create comprehensive profiles, which are then sold and used for targeted advertising and other practices. "These practices are out of control and are rife with privacy violations and breaches of European law," including the General Data Protection Regulation, said Finn Myrstad, director of digital policy in the Norwegian Consumer Council in a statement. The consumer advocacy group filed three GDPR complaints against the queer dating app Grindr and five advertising divisions of tech companies that reportedly receive the personal data including Twitter's MoPub and AT&T's AppNexus. "Every time you open an app like Grindr, advertisement networks get your GPS location, device identifiers and even the fact that you use a gay dating app," said Max Schrems, founder of the European privacy non-profit noyb.