Many households include children who use voice personal assistants (VPA) such as Amazon Alexa. Children benefit from the rich functionalities of VPAs and third-party apps but are also exposed to new risks in the VPA ecosystem. In this paper, we first investigate "risky" child-directed voice apps that contain inappropriate content or ask for personal information through voice interactions. We build SkillBot - a natural language processing (NLP)-based system to automatically interact with VPA apps and analyze the resulting conversations. We find 28 risky child-directed apps and maintain a growing dataset of 31,966 non-overlapping app behaviors collected from 3,434 Alexa apps. Our findings suggest that although child-directed VPA apps are subject to stricter policy requirements and more intensive vetting, children remain vulnerable to inappropriate content and privacy violations. We then conduct a user study showing that parents are concerned about the identified risky apps. Many parents do not believe that these apps are available and designed for families/kids, although these apps are actually published in Amazon's "Kids" product category. We also find that parents often neglect basic precautions such as enabling parental controls on Alexa devices. Finally, we identify a novel risk in the VPA ecosystem: confounding utterances, or voice commands shared by multiple apps that may cause a user to interact with a different app than intended. We identify 4,487 confounding utterances, including 581 shared by child-directed and non-child-directed apps. We find that 27% of these confounding utterances prioritize invoking a non-child-directed app over a child-directed app. This indicates that children are at real risk of accidentally invoking non-child-directed apps due to confounding utterances.

Smart home devices from companies such as Amazon and Google can be hacked and used to crash websites, steal data and snoop on users, an investigation reveals. Consumer group Which? has found poor security on eight smart devices, some of which are no longer supported with vital security updates due to their age. Examples include the first generation Amazon Echo smart speaker, released in 2014, and a Virgin Media internet router from 2017. All of the products had vulnerabilities that could leave users exposed to cybercriminals, Which? Domestic abuse survivors can also be tracked and controlled by ex-partners who exploit weak security on devices including Wi-Fi routers and security cameras.

Mother's Day is just around the corner, and in honor of that fact, several retailers are now discounting a wide range of tech and accessories. We're seeing some of the lowest prices we've seen on a variety of items, ranging from the latest Kindle Paperwhite to the new Echo Show 15, as well as some of the best fitness trackers, streaming sticks, and noise-canceling wireless earbuds you can buy. That means you can still get a great deal on a present for your mom if you haven't bought one already -- or if you simply want to pick up something nice for yourself. Below, we've collected some of the top deals on tech and gadgets so you can save on a great gift ahead of the holiday. Take a look, and be sure to also peruse our 2022 Mother's Day Gift Guide, which can help you quickly narrow the field so your present will arrive in time. The Echo Show 8 is the midsized smart display in Amazon's current Echo lineup and can be used to display the weather, news, calendars, grocery lists, and more.

Banks and financial services institutions face increased competition not only from peer organizations within the industry, but also now from FinTech startups, Neobanks, and others. The way to compete is to deliver highly personalized services and innovative offerings. And increasingly, the way to do that is using AI/ML to derive data-driven insights upon which those services and offerings can be based. Financial services institutions increasingly have sought to use new data sources to expand their traditional risk analysis and make more personalized offerings to a larger customer base. Many have gone beyond traditional methods (e.g., using FICO scores, credit history, salary, and more) and developed new risk models and highly individualized creditworthiness ratings based on the analysis of additional data sources.

The phrase "dynamic time warping," at first read, might evoke images of Marty McFly driving his DeLorean at 88 MPH in the Back to the Future series. Alas, dynamic time warping does not involve time travel; instead, it's a technique used to dynamically compare time series data when the time indices between comparison data points do not sync up perfectly. As we'll explore below, one of the most salient uses of dynamic time warping is in speech recognition – determining whether one phrase matches another, even if the phrase is spoken faster or slower than its comparison. You can imagine that this comes in handy to identify the "wake words" used to activate your Google Home or Amazon Alexa device – even if your speech is slow because you haven't yet had your daily cup(s) of coffee. In time series analysis, Dynamic Time Warping (DTW) is one of the algorithms for measuring the similarity between two temporal time series sequences, which may vary in speed.

In recent years, the world has been more inclined toward technological advancement and the emergence of new-age technologies in the digital space affects every sector throughout the world. As a result of this, improvements in various machine learning techniques, like AI, have been playing a big part in digital advertising. According to The Insight Partners' analysis, the worldwide Artificial Intelligence in the marketing sector is estimated to reach US$ 107,535.57 million by 2028, growing at a 31.6% However, AI is transforming not just the overall operations, but the digital advertising landscape as well, from chatbots and virtual assistants to content development and user experience upgrades, among other things. AI helps to make appropriate judgments, AI thinks like a human to speed up and simplify the planning as well as execution process.

This week, host June Thomas talks to Jessamyn West, a librarian in rural Vermont who's working to improve computer literacy and access to library services in her community. In the interview, Jessamyn explains her process for helping people to learn basic computer skills, like building a resume, setting up an online dating profile, or learning how to use a mouse. She also talks about her broader mission to make sure technology is intuitive and accessible to everyone who needs it. After the interview, June and co-host Isaac Butler discuss mantras and understanding your strengths and weaknesses. Send your questions about creativity and any other feedback to working@slate.com or give us a call at (304) 933-9675.

There are several online newspapers in urdu but for the users it is difficult to find the content they are looking for because these most of them contain irrelevant data and most users did not get what they want to retrieve. Our proposed framework will help to predict Urdu news in the interests of users and reduce the users searching time for news. For this purpose, NLP techniques are used for pre-processing, and then TF-IDF with cosine similarity is used for gaining the highest similarity and recommended news on user preferences. Moreover, the BERT language model is also used for similarity, and by using the BERT model similarity increases as compared to TF-IDF so the approach works better with the BERT language model and recommends news to the user on their interest. The news is recommended when the similarity of the articles is above 60 percent.

Artificial intelligence has been around for decades, but only recently has the technology gotten to the point where you can begin to reap its benefits of it in your everyday life. This includes your personal and business life as well. Businesses have been taking advantage of this technology's ability to create new strategies and content on their own, without any human input whatsoever, while consumers are now able to see AI's influence on popular applications such as Siri and Amazon's Alexa. With most content creators knowing everything there is to know about their subject, at least on a basic level, it can be difficult for them to distinguish themselves from others in their field. That is where artificial intelligence comes in.

Recommender systems fundamentally address the question – What do people want? Although it is an extensive question, in the context of a consumer application like e-commerce, the answer could be to serve the best products in terms of price and quality for a consumer. For a news aggregator website, it could be to show reliable and relevant content. In a case where a user would have to look through thousands or millions of items to find what they are looking for, a recommendation engine is indispensable. The engine filters over 3,000 titles at a time using 1,300 recommendation clusters based on user preferences. It is so accurate that personalised recommendations from the engine drive 80% of Netflix viewer activity. However, building and evaluating a recommender system is very different compared to a single ML model regarding design decisions, engineering, and metrics. In this article, we will focus on testing a recommendation system. The second and third require a lot of user-item interaction data. If that is not available, one might start with the first type of recommender system.