Today, at Amazon's 2019 product event, the company launched a boatload of new Alexa-enabled Echo products, two of which you'll be wearing 24/7. While the new Echo Buds are a wearable response to Apple AirPods, Amazon has gotten even more ambitious with its wearables. The company also introduced a ring and pair of glass frames, both of which have Alexa built in. These two new products are part of Amazon's Day 1 Editions program, which aims to put new, ambitious products on the market quickly. The products will be available to customers through invitation only, and Amazon will seek feedback from users.

Artificial intelligence, as a new technology field has already gained tremendous momentum and popularity across all the digital niches. From the conversational chatbots to data analytics to the user experience design, AI is now being used for an array of niche purposes. How can AI impact mobile app development and user experience? AI is all about mimicking human intelligence to serve practical business purposes. The digital assistants like Apple's Siri and Amazon Alexa are great examples of how AI can make digital interactions intelligent and user-centric.

AI and bots will give donors a massive boost to their understanding and encourage them to donate even more according to the Charities Aid Foundation's think-tank. Almost everyone will have experienced artificial intelligence (AI) on the internet; either when using an automated chatbot linked to a website or via online ads, which use carefully crafted algorithms to target their potential customers. A recent article, entitled Is AI The Future Of Philanthropy Advice, explored the current and future use of AI by charities to help better deliver services and inform potential donors. The promise of being able to extend the reach of charities through more meaningful messaging whilst reducing the cost of delivery by offering automated advice and information, makes AI a very attractive project for charities to pursue. A notable example is Arthritis Research UK, who partnered with Microsoft to pilot an automated chatbot that offered web visitors personalised advice on arthritis.

This keynote will highlight common myths surrounding AI, how it is affecting human life and software, discuss "Deeply Inclusive AI" and future of humans in an AI world. We live in a golden age where technology influences every facet of our daily lives. This is especially true with the coming of "Artificial Intelligence" (AI). AI is everywhere – from virtual assistants like Alexa, Google Home and Siri to aiding the creation of new disruptive technology like autonomous cars, blockchain and cryptocurrency. Areas of healthcare, finance, retail, customer analytics and manufacturing have already started seeing positive effects of using AI, many of which we would have never thought is possible just a few years ago.

The news: Amazon unveiled a dizzying number of new gadgets yesterday, including wireless earphones (Echo Buds), a smart ring (Echo Loop), and smart glasses (Echo Frames). They all provide hands-free access, so you can ask Alexa to play a song, give you directions, or whatever else you may need, on the go. The products cost between $129 and $180 each. What's behind all this: Taking these three products together, there's a clear push to move Alexa beyond the home and onto your body. While Google Assistant is embedded into Android smartphones, people currently use Alexa only at home, and there's no Amazon smartphone.

In recent years, our fascination with the potential of AI has taken a more starry-eyed turn, as shown in the 2013 sci-fi drama "Her," where the main character falls in love with a virtual assistant. In reality, artificial intelligence (AI) technology is quickly permeating every aspect of our lives. From Amazon's voice-activated Alexa to writing technology that helps managers craft job postings, AI is in our hearts, homes and workplaces. And it's only going to become a bigger part of our lives: Experts call the rise of AI the driving force behind the fourth industrial revolution. On a recent afternoon at the NVIDIA robotics research lab in Seattle's University District, researchers use a simulated kitchen to test robots' ability to perform simple tasks such as grabbing objects.

Matrix completion constantly receives tremendous attention from many research fields. It is commonly applied for recommender systems such as movie ratings, computer vision such as image reconstruction or completion, multi-task learning such as collaboratively modeling time-series trends of multiple sensors, and many other applications. Matrix completion techniques are usually computationally exhaustive and/or fail to capture the heterogeneity in the data. For example, images usually contain a heterogeneous set of objects, and thus it is a challenging task to reconstruct images with high levels of missing data. In this paper, we propose the sparse reverse of principal component analysis for matrix completion. The proposed approach maintains smoothness across the matrix, produces accurate estimates of the missing data, converges iteratively, and it is computationally tractable with a controllable upper bound on the number of iterations until convergence. The accuracy of the proposed technique is validated on natural images, movie ratings, and multisensor data. It is also compared with common benchmark methods used for matrix completion.

The Social Internet of Things (SIoT), integration of Internet of Things and Social networks paradigms, has been introduced to build a network of smart nodes which are capable of establishing social links. In order to deal with misbehavioral service provider nodes, service requestor nodes must evaluate their trustworthiness levels. In this paper, we propose a novel trust management mechanism in the SIoT to predict the most reliable service provider for a service requestor, that leads to reduce the risk of exposing to malicious nodes. We model an SIoT with a flexible bipartite graph (containing two sets of nodes: service providers and requestors), then build the corresponding social network among service requestor nodes, using Hellinger distance. After that, we develop a social trust model, by using nodes' centrality and similarity measures, to extract behavioral trust between the network nodes. Finally, a matrix factorization technique is designed to extract latent features of SIoT nodes to mitigate the data sparsity and cold start problems. We analyze the effect of parameters in the proposed trust prediction mechanism on prediction accuracy. The results indicate that feedbacks from the neighboring nodes of a specific service requestor with high Hellinger similarity in our mechanism outperforms the best existing methods. We also show that utilizing social trust model, which only considers the similarity measure, significantly improves the accuracy of the prediction mechanism. Furthermore, we evaluate the effectiveness of the proposed trust management system through a real-world SIoT application. Our results demonstrate that the proposed mechanism is resilient to different types of network attacks and it can accurately find the proper service provider with high trustworthiness.

The retail industry is undergoing a sea change so massive that many industry insiders have termed it the "retail apocalypse." In a sign of this industry upheaval, in 2018 major retailers closed 5,524 stores in the U.S. and 1,432 stores in the U.K., according to figures compiled by the Coresight Research, a firm that studies the retail industry.1 In some good news for the industry, Coresight predicts that 2019 "will not be the year of retail apocalypse or even decline. Instead, it will be a year of reinvention -- for the retail sector as a whole and for physical stores in particular."2 This predicted reinvention of the industry stems in part from the use of sophisticated technology, specifically artificial intelligence.

Google Assistant is the smartest AI assistant. Microsoft's Cortana is surprisingly good, coming in second. Alexa is learning faster than any other assistant. Digital marketing firm Stone Temple tested Alexa, Cortana, Google Assistant, and Siri via an exhaustive list of 4,942 queries. For each, the company checked if the assistant answered, if the assistant understood the question, if the answer was wrong, and if the answers were sourced from a database or a third-party source, like Wikipedia.