Does Google Assistant always say your name wrong, or maybe the names of people you know? You can soon teach the digital assistant how to pronounce them correctly. Google announced an update rolling out soon to Assistant, available on smartphones and Google Home speakers, that will allow users to teach it how to properly pronounce your name or those in your contacts. Google said the feature will initially be available in English but will roll out to offer more languages soon. "Names matter, and it's frustrating when you're trying to send a text or make a call and Google Assistant mispronounces or simply doesn't recognize a contact," said Yury Pinsky, director of product management at Google, in a blog post published Wednesday.

Google has launched a way for users to better improve how its Assistant pronounces names. Users will now be able to teach Google Assistant to enunciate and recognise names of contacts as they are supposed to be said. This can be found in Google Assistant's settings, under Basic Info, and then Nickname. "Assistant will listen to your pronunciation and remember it, without keeping a recording of your voice", Google says in a blog post. "The feature will be available in English and we hope to expand to more languages soon."

Not long ago, consumers shopped for financial services by visiting branches of multiple banks and credit unions, collecting an assortment of brochures from a rack, talking to branch personnel, and comparing various value propositions. The decision was based on the human connection with the people at the branch, combined with the alignment of the products offered and the financial needs of the consumer. For today's consumer, shopping for a new banking relationship is far different. The increase in online and mobile banking options has empowered the consumer with far more alternatives, while making the decision-making easier. Consumers can browse, compare and purchase virtually any financial product or service from their computer or mobile device 24/7, without ever stepping foot in a branch.

Amazon's book recommendation algorithms that help customers discover new titles may have a dark side. A new report from the Institute for Strategic Dialogue says these algorithms steer people to books about conspiracy theories and extremism, sometimes introducing them to the work of conspiracy theorists who've been banned by other online platforms. People browsing a book about one conspiracy on Amazon are likely to get suggestions for more books on that topic as well as books about other conspiracy theories about everything from QAnon to COVID-19 vaccine, the report found. Other features, such as auto-complete in the search bar and content suggestions for the author or similar authors can also lead users down an extremist rabbit hole, said Chloe Colliver, head of digital policy and strategy at ISD. The pattern is similar to problems observed on other major online platforms like Google's YouTube whose algorithms have been found to direct users to extreme content, sucking them into violent ideologies.

For business owners and marketers, the question is, am I going to passively allow AI to happen to me, or am I instead going to find ways to harness, control and benefit from it? A traditional definition of artificial intelligence, as defined by Alan Turing and put to the test in movie The Imitation Game, is the ability of machines to replicate human thinking and reasoning. In practice, we can now see this in our everyday lives when our navigation apps help us to avoid a traffic accident, Google helps us find information, Gmail finishes our sentences or Siri responds to our questions. In all of these situations the underlying software or algorithm is processing huge amounts of data or information to predict an outcome and direct us to the best possible solution. This speaks to one of the fundamental principles of marketing – namely identifying what is it that your customers need or want that your product or service can satisfy.

We propose to assess the fairness of personalized recommender systems in the sense of envy-freeness: every (group of) user(s) should prefer their recommendations to the recommendations of other (groups of) users. Auditing for envy-freeness requires probing user preferences to detect potential blind spots, which may deteriorate recommendation performance. To control the cost of exploration, we propose an auditing algorithm based on pure exploration and conservative constraints in multi-armed bandits. We study, both theoretically and empirically, the trade-offs achieved by this algorithm.

Recommender systems have achieved great success in modeling user's preferences on items and predicting the next item the user would consume. Recently, there have been many efforts to utilize time information of users' interactions with items to capture inherent temporal patterns of user behaviors and offer timely recommendations at a given time. Existing studies regard the time information as a single type of feature and focus on how to associate it with user preferences on items. However, we argue they are insufficient for fully learning the time information because the temporal patterns of user preference are usually heterogeneous. A user's preference for a particular item may 1) increase periodically or 2) evolve over time under the influence of significant recent events, and each of these two kinds of temporal pattern appears with some unique characteristics. In this paper, we first define the unique characteristics of the two kinds of temporal pattern of user preference that should be considered in time-aware recommender systems. Then we propose a novel recommender system for timely recommendations, called TimelyRec, which jointly learns the heterogeneous temporal patterns of user preference considering all of the defined characteristics. In TimelyRec, a cascade of two encoders captures the temporal patterns of user preference using a proposed attention module for each encoder. Moreover, we introduce an evaluation scenario that evaluates the performance on predicting an interesting item and when to recommend the item simultaneously in top-K recommendation (i.e., item-timing recommendation). Our extensive experiments on a scenario for item recommendation and the proposed scenario for item-timing recommendation on real-world datasets demonstrate the superiority of TimelyRec and the proposed attention modules.

Google is introducing a handful new updates that will make Assistant better at pronouncing tricky names and understanding the context of conversations you share with it. To start, you'll soon have the opportunity to teach it how to pronounce the names of your contacts, much like you can already coach it on how to properly say your own name. According to Google, "Assistant will listen to your pronunciation and remember it," with no need for the recording to be kept by the company. Google says the feature will be available in English first, though it "hopes" to make it available in other languages as well. Once you get the new update on your phone, tap the "Record your own" option under the contact field in the Assistant settings menu to get started.

Fox News Flash top entertainment and celebrity headlines are here. Check out what's clicking today in entertainment. Bethenny Frankel took a chance on a dating app and it worked out pretty well. The Skinnygirl founder, 50, revealed she met now-fiancé Paul Bernon online and felt a spark between them immediately. "We met on a dating app," Frankel told People magazine.

I'm not much of a cook, but the few times I've asked Google Assistant on my Nest Mini to start a timer in the kitchen have been hit or miss. All too often, the timer disappears into a void and Google can't tell me how many minutes are left. Other times, it takes multiple attempts to set it properly because Assistant struggled with understanding context. Those problems (and a few others) are about to be resolved. Google's latest update to its voice assistant, which begins rolling out today, greatly improves its contextual understanding when you're asking it to perform a task like setting an alarm or a timer.