Amid rapidly changing technology, many people still associate artificial intelligence (AI) with science-fiction dystopias. But in reality, AI has become an integral part of our daily lives. We now rely on search-engine algorithms and digital assistants like Alexa and Siri for almost everything, including ordering a taxi or finding out how many calories there are in a 10-inch Margherita pizza. The potential of this technology goes beyond its household use. AI is making great strides in the business world, and creativity may be its ultimate moonshot.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The Polk County Sheriff's Office in Florida on Thursday announced charges against 68 suspected drug dealers as part of an undercover operation using social media and dating apps. The six-month operation dubbed "Swipe Left for Meth" -- a play on Grindr and other dating apps that require users to "swipe" through scores of potential dates in their area -- concluded in the arrests of 60 individuals and securement of eight arrest warrants for individuals still at-large related to drug sales or possession. "We've known for some time that suspects will use the internet and social media to prey upon children online, or to engage in prostitution, but this is something we are seeing more and more of in Polk County -- suspects who are using dating apps to sell illegal narcotics," Polk County Sheriff Grady Judd said in a statement." Suspects are getting more creative, but so are our detectives."

In a building called the Partnerplex on Google's sprawling campus in Mountain View, California, I've been invited to hear a 51-second phone recording of someone making a dinner reservation. Person 2: Hi, um, I'd like to reserve a table for Friday the third. Person 1: OK, hold on one moment. As I listen to what sounds like a man and a woman talking, Google's top executives for Assistant, the search giant's digital helper, watch closely to gauge my reaction. They're showing off the Assistant's new tricks a few days before Google I/O, the company's annual developer conference that starts Tuesday. Turns out this particular trick is pretty wild. That's because Person 2, the one who sounds like a man, isn't a person at all.

A 2019 study conducted by DataChildFutures found that 46% of participating Italian households had AI-powered speakers, while 40% of toys were connected to the internet. More recent research suggests that by 2023 more than 275 million intelligent voice assistants, such as Amazon Echo or Google Home, will be installed in homes worldwide. As younger generations grow up interacting with AI-enabled devices, more consideration should be given to the impact of this technology on children, their rights and wellbeing. AI-powered learning tools and approaches are often regarded as critical drivers of innovation in the education sector. Often recognized for its ability to improve the quality of learning and teaching, AI is being used to monitor students' level of knowledge and learning habits, such as rereading and task prioritization, and ultimately to provide a personalized approach to learning. Knewton is one example of AI-enabled learning software that identifies knowledge gaps and curates education content in line with user needs.

So, let us now preprocess our data! Natural Language Processing techniques are our savior when we have to deal with textual data. Since our data cannot be fed to any machine-learning model unless we clean it, that's where NLP comes to play! Let's clean our text data – Firstly, let us create a new column in our dataframe that will hold all necessary keywords required for the model.

Amid rapidly changing technology, many people still associate artificial intelligence (AI) with science-fiction dystopias. But in reality, AI has become an integral part of our daily lives. We now rely on search-engine algorithms and digital assistants like Alexa and Siri for almost everything, including ordering a taxi or finding out how many calories there are in a 10-inch Margherita pizza. The potential of this technology goes beyond its household use. AI is making great strides in the business world, and creativity may be its ultimate moonshot.

Collaborative filtering is the de facto standard for analyzing users' activities and building recommendation systems for items. In this work we develop Sliced Anti-symmetric Decomposition (SAD), a new model for collaborative filtering based on implicit feedback. In contrast to traditional techniques where a latent representation of users (user vectors) and items (item vectors) are estimated, SAD introduces one additional latent vector to each item, using a novel three-way tensor view of user-item interactions. This new vector extends user-item preferences calculated by standard dot products to general inner products, producing interactions between items when evaluating their relative preferences. SAD reduces to state-of-the-art (SOTA) collaborative filtering models when the vector collapses to one, while in this paper we allow its value to be estimated from data. The proposed SAD model is simple, resulting in an efficient group stochastic gradient descent (SGD) algorithm. We demonstrate the efficiency of SAD in both simulated and real world datasets containing over 1M user-item interactions. By comparing SAD with seven alternative SOTA collaborative filtering models, we show that SAD is able to more consistently estimate personalized preferences.

The most common way to listen to recorded music nowadays is via streaming platforms which provide access to tens of millions of tracks. To assist users in effectively browsing these large catalogs, the integration of Music Recommender Systems (MRSs) has become essential. Current real-world MRSs are often quite complex and optimized for recommendation accuracy. They combine several building blocks based on collaborative filtering and content-based recommendation. This complexity can hinder the ability to explain recommendations to end users, which is particularly important for recommendations perceived as unexpected or inappropriate.

Recommender systems play an important role in helping people find information and make decisions in today's increasingly digitalized societies. However, the wide adoption of such machine learning applications also causes concerns in terms of data privacy. These concerns are addressed by the recent "General Data Protection Regulation" (GDPR) in Europe, which requires companies to delete personal user data upon request when users enforce their "right to be forgotten". Many researchers argue that this deletion obligation does not only apply to the data stored in primary data stores such as relational databases but also requires an update of machine learning models whose training set included the personal data to delete. We explore this direction in the context of a sequential recommendation task called Next Basket Recommendation (NBR), where the goal is to recommend a set of items based on a user's purchase history. We design efficient algorithms for incrementally and decrementally updating a state-of-the-art next basket recommendation model in response to additions and deletions of user baskets and items. Furthermore, we discuss an efficient, data-parallel implementation of our method in the Spark Structured Streaming system. We evaluate our implementation on a variety of real-world datasets, where we investigate the impact of our update techniques on several ranking metrics and measure the time to perform model updates. Our results show that our method provides constant update time efficiency with respect to an additional user basket in the incremental case, and linear efficiency in the decremental case where we delete existing baskets. With modest computational resources, we are able to update models with a latency of around 0.2~milliseconds regardless of the history size in the incremental case, and less than one millisecond in the decremental case.

Tinder is charging people over 30 up to 48 per cent more for its premium service, an investigation has revealed. Which? said its findings suggest possible discrimination and a potential breach of UK law by the popular dating app. The consumer group also initially accused Tinder of hiking prices for young gay and lesbian users aged 18-29, but has since backtracked on this. A statement from Which? said: 'Having initially chosen not to provide further information, Tinder has since revealed that it offers discounts to users aged 28 and under in the UK.' It added that the dating app'claimed that by including 29-year-olds in our analysis of the relationship between price with age and sexual orientation, "the results would be skewed to make it appear that LGBTQAI members paid more based upon orientation, when in fact, it was based upon age".' Which? said that in light of the new information, it has'no evidence that sexual orientation impacts pricing for young Tinder users'. Tinder had previously said it was'categorically untrue' that its pricing structure discriminates by sexual preference.