Amazon's Echo Show 15 comes with a digital photo frame picture that enables it to display only photos or artwork, uninterrupted by random Alexa skill suggestions, recipes or your schedule. Only the 15.6-inch had that feature, though -- until now. According to The Verge, the e-commerce giant has recently added its dedicated photo frame feature to all Echo Show Models in the US, the UK, Canada, Germany, France, Italy, Spain and Australia. The Verge says you can activate the slideshow by saying the voice command: "Alexa, start Photo Frame." Your smart display will then start a slideshow using the contents of your Amazon Photos and your Facebook account. It can also display a random selection of stock images if you've yet to upload your personal photos or have yet to link your accounts with the device.

Over a decade ago, online dating existed through sites such as match.com But they did not tend to attract younger users, and for many there was a stigma attached to using them. But apps such as Grindr, which launched in 2009, and Tinder, which launched in September 2012, have radically changed the landscape of online dating. Emily Witt, the author of Future Sex, a book that examines sex in the internet age, tells Nosheen Iqbal about the origin story of Tinder, meeting the founders, and why it was such an instant success. Emily discusses her own experience of using it and the apps that followed in its wake. The apps have grown in response to a culture where people are single for a lot longer, and also where there's a changing sexual morality that's open to different kinds of relationships, Emily believes.

Speaking about the evolution of technology you can't skip mentioning artificial intelligence simply because in our day-to-day activities we do interact with the technology mostly even without knowing that we do. If you own a smartphone, laptop, smartwatches, desktop, and so many devices yes you do interact with artificial intelligence or use it to accomplish some of your tasks such as google search, Camera, meeting platforms like zoom, Google spreadsheets, Microsoft Cortana, Apple Siri, Google Assistant, Google map, Apple map, Google lens, social media interaction, etc. The scope of artificial intelligence has expanded and evolved over time. So, it is time to think about how you can leverage this technology to improve the revenue of your business in this article I will highlight the potential of conversational artificial intelligence for businesses. Conversational AI involves three concepts artificial intelligence, human language, and automation.

Embedding learning is an important technique in deep recommendation models to map categorical features to dense vectors. However, the embedding tables often demand an extremely large number of parameters, which become the storage and efficiency bottlenecks. Distributed training solutions have been adopted to partition the embedding tables into multiple devices. However, the embedding tables can easily lead to imbalances if not carefully partitioned. This is a significant design challenge of distributed systems named embedding table sharding, i.e., how we should partition the embedding tables to balance the costs across devices, which is a non-trivial task because 1) it is hard to efficiently and precisely measure the cost, and 2) the partition problem is known to be NP-hard. In this work, we introduce our novel practice in Meta, namely AutoShard, which uses a neural cost model to directly predict the multi-table costs and leverages deep reinforcement learning to solve the partition problem. Experimental results on an open-sourced large-scale synthetic dataset and Meta's production dataset demonstrate the superiority of AutoShard over the heuristics. Moreover, the learned policy of AutoShard can transfer to sharding tasks with various numbers of tables and different ratios of the unseen tables without any fine-tuning. Furthermore, AutoShard can efficiently shard hundreds of tables in seconds. The effectiveness, transferability, and efficiency of AutoShard make it desirable for production use. Our algorithms have been deployed in Meta production environment. A prototype is available at https://github.com/daochenzha/autoshard

Machine learning or commonly known as ML is a type of artificial intelligence or artificial intelligence that can help software work more accurately in predicting results without being explicitly programmed to do a job. Machine learning algorithms use historical data as input data to predict new output values. One example of the application of machine learning is recommendation machines on e-commerce platforms or social media platforms. Other applications of machine learning that are currently being widely adopted are fraud detection, spam filtering, malware threat detection, business process automation, and Predictive maintenance. Today machine learning is very important because it gives companies insight into customer behavior trends and business operational patterns and supports the development of new products.

Data analytics is the process of examining data sets in order to draw conclusions about the information they contain, increasingly with the aid of specialized systems and software. Data analytics technologies and techniques are widely used in commercial industries to enable organizations to make more-informed business decisions. Artificial Intelligence (AI) is where we first heard the term cognitive technology. Technology experts began seeing the benefits of AI among consumer and business application. Amazon's Alexa and chatbots are two that come to mind that is now ubiquitous in homes and businesses.

SHOPPING: Products featured in this article are independently selected by our shopping writers. If you make a purchase using links on this page, MailOnline will earn an affiliate commission. Calling all bargain hunters, today shoppers can score a certified refurbished Amazon Echo Show 8 (1st Gen, 2019 release) and save 44 per cent off. Now discounted to just £59.99, marked down from £107.99, it's a great deal for those on a budget that want a smart home device with a display for video calls and home security. With an 8-inch HD screen, the Echo Show 8 is one of the retailer's most versatile Echo devices; it can display recipes, give you the weather, traffic updates and more.

With the huge growth in e-commerce domain, product recommendations have become an increasing field of interest amongst e-commerce companies. One of the more difficult tasks in product recommendations is size and fit predictions. There are a lot of size related returns and refunds in e-fashion domain which causes inconvenience to the customers as well as costs the company. Thus having a good size and fit recommendation system, which can predict the correct sizes for the customers will not only reduce size related returns and refunds but also improve customer experience. Early works in this field used traditional machine learning approaches to estimate customer and product sizes from purchase history. These methods suffered from cold start problem due to huge sparsity in the customer-product data. More recently, people have used deep learning to address this problem by embedding customer and product features. But none of them incorporates valuable customer feedback present on product pages along with the customer and product features. We propose a novel approach which can use information from customer reviews along with customer and product features for size and fit predictions. We demonstrate the effectiveness of our approach compared to using just product and customer features on 4 datasets. Our method shows an improvement of 1.37% - 4.31% in F1 (macro) score over the baseline across the 4 different datasets.

How can we accurately recommend actions for users to control their devices at home? Action recommendation for smart home has attracted increasing attention due to its potential impact on the markets of virtual assistants and Internet of Things (IoT). However, designing an effective action recommender system for smart home is challenging because it requires handling context correlations, considering both queried contexts and previous histories of users, and dealing with capricious intentions in history. In this work, we propose SmartSense, an accurate action recommendation method for smart home. For individual action, SmartSense summarizes its device control and its temporal contexts in a self-attentive manner, to reflect the importance of the correlation between them. SmartSense then summarizes sequences of users considering queried contexts in a query-attentive manner to extract the query-related patterns from the sequential actions. SmartSense also transfers the commonsense knowledge from routine data to better handle intentions in action sequences. As a result, SmartSense addresses all three main challenges of action recommendation for smart home, and achieves the state-of-the-art performance giving up to 9.8% higher mAP@1 than the best competitor.

Chatbots and smart speakers don't seem so smart today. But soon, they will perform a multitude of tasks. Billions of people use instant messaging apps daily to exchange bite-sized chunks of information on platforms including WhatsApp, iMessage, WeChat, Signal, Slack, Facebook Messenger, and Snapchat. In fact, as of this year, the amount of time we spend on messaging apps may surpass our use of social networks. These messaging platforms have become increasingly sophisticated, with capabilities far beyond simply enabling users to send and receive text messages, photos, and videos.