The number of recorded sexual offences involving online dating sites and apps has almost doubled in the last four years, police figures suggest. Offences where a dating site was mentioned in a police report increased from 156 in 2015, to 286 last year, according to figures from 23 of the 43 forces in England and Wales. The Online Dating Association said apps try to protect users from harm. But the National Police Chiefs' Council said firms had a duty to do more. The figures reveal that between 2015 and 2018 there were a total of 2,029 recorded offences - including sexual offences - where an online dating website or app was mentioned in a police report. In 2015, 329 offences were recorded, compared to 658 recorded offences last year.

Ranking is a core task in E-commerce recommender systems, which aims at providing an ordered list of items to users. Typically, a ranking function is learned from the labeled dataset to optimize the global performance, which produces a ranking score for each individual item. However, it may be sub-optimal because the scoring function applies to each item individually and does not explicitly consider the mutual influence between items, as well as the differences of users' preferences or intents. Therefore, we propose a personalized context-aware re-ranking model for E-commerce recommender systems. The proposed re-ranking model can be easily deployed as a follow-up modular after ranking by directly using the existing feature vectors of ranking. It directly optimizes the whole recommendation list by employing a transformer structure to efficiently encode the information of all items in the list. Specifically, the Transformer applies a self-attention mechanism that directly models the global relationships between any pair of items in the whole list. Besides, we introduce the personalized embedding to model the differences between feature distributions for different users. Experimental results on both offline benchmarks and real-world online E-commerce systems demonstrate the significant improvements of the proposed re-ranking model.

Artificial intelligence (AI) changes the way we go about our everyday lives. Consider Apple's line of mobile devices with Siri. And think about Amazon's smart home products featuring Alexa. They make us comfortable interacting with a virtual assistant. These forms of AI make our lives easier and help us perform hands-free tasks.

Sales of smart speakers are soaring despite some people's concerns over privacy, with Amazon's Alexa leading the charge into homes in various Echo devices and Google's Home and Assistant snapping at its heels. They come in various shapes, sizes and prices, but if you just want to dip your toe into the burgeoning voice-powered world, what's the cheapest way to get Alexa or Google Assistant into your home? Voice assistants don't actually need a dedicated speaker to work. If you have a modern smartphone chances are you either already have Google Assistant, if you have an Android phone, or can install the app on an iPhone. The same goes for Amazon's Alexa, which can even be set as the default voice assistant on an Android phone.

There are hundreds of smart home devices available today, but some of them are more popular than others. In fact, there's a select group of smart gadgets that have near cult followings--we're talking thousands of users who swear by these devices. Here's why people are obsessed with them and whether they're worth the investment. The second-gen Echo is the best in its class. It should come as no surprise that the Amazon Echo makes this list.

Though Conversational AI has been around since the 1960s, it's experiencing a renewed focus in recent years. While we're still in the early days of the design and development of intelligent conversational AI, Google quite rightly announced that we were moving from a mobile-first to an AI- first world, where we expect technology to be naturally conversational, thoughtfully contextual, and evolutionarily competent. In other words, we expect technology to learn and evolve. Most chatbots today can handle simple questions and respond with prebuilt responses based on rule-based conversation processing. For instance, if user says X, respond with Y; if user says Z, call a REST API, and so forth.

Machine learning has been driving innovation in product features for several years, becoming a vital component of enhancing the consumer experience. However, in terms of what machine learning (ML) -- and specifically, artificial intelligence (AI) -- can do, we've only seen the tip of the iceberg. As a business leader, you may feel a bit like Chevy Chase in Fletch trying to flub your way into appearing knowledgeable about machine learning (it's all ball bearings nowadays…). Nevertheless, it's vital to understand what machine learning is and how to get the most out of it for your business. While it sounds technical, machine learning, in the simplest sense, is a branch of artificial intelligence that uses computer algorithms to learn from data and make better predictions.

Hyundai's Digital Key allows drivers to unlock and start their car with a smartphone using near-field communication technology. It will debut on the 2020 Hyundai Sonata. Self-driving cars are coming – someday. But for now, carmakers and suppliers are focused on technologies that improve vehicle safety, security and convenience. With the 2019 New York Auto Show set to begin next week with media previews, car companies are looking for ways to stand out from the competition in an era when quality and reliability are similar across brands.

If you use a smart speaker, you know all of the conveniences and delights that make it more than just a glorified paper weight. But, admit it, you've probably given it some privacy side-eye from time to time. After all, it is a microphone that just sits in your house waiting for a wake word to start recording what you say. Here's how to tighten the reins on what Alexa, Google Assistant, and Siri can hear, when, and how it gets used. It's a good time to take stock.

WATCH (May 24, 2018): Amazon's Alexa records family's conversation, sends it to random contact Amazon staff can listen to commands and questions users pose to the Alexa voice assistant -- and they sometimes do. The company acknowledged that the conversations aren't totally private in a statement to Global News after the news was first reported by Bloomberg. "We only annotate an extremely small number of interactions from a random set of customers in order to improve the customer experience," Amazon said in the statement. Amazon explained that the company uses samples collected to better train "speech recognition and natural language understanding systems." READ MORE: Alexa recorded one family's conversations and sent them to a friend, without them knowing Bloomberg reported Wednesday that Amazon has "thousands" of employees who are trying to improve Alexa's speech recognition technology.