Acumen Research and Consulting, recently published report "Wearable Artificial Intelligence (AI) Market - Global Industry Analysis, Size, and Forecast, 2019 - 2026" LOS ANGELES, May 03, 2019 (GLOBE NEWSWIRE) -- The Global Wearable Artificial Intelligence (AI) Market size is estimated to grow at CAGR above 27 % over the forecast time frame 2019-2026 and reach the market value around USD 185 billion by 2026. The key drivers for development will be increased demand for AI assistants, increased operations in the Healthcare industry, the emergence of IoT and the integration of wireless technologies and the growth of wearable component technology. As the majority of intelligent wearable equipment lacks basic safety mechanisms, an increasing concern for data security in smarts is preventing the growth of the wearable artificial intelligent market. Moreover, the cost of production is high and the consumption of batteries is limited. In the forecast period, the earwear market is projected to grow at more than 43 percent.

The fast pace of artificial intelligence (AI) and automation is propelling strategists to reshape their business models. This is fostering the integration of AI in the business processes but the consequences of this adoption are underexplored and need attention. This paper focuses on the overall impact of AI on businesses - from research, innovation, market deployment to future shifts in business models. To access this overall impact, we design a three-dimensional research model, based upon the Neo-Schumpeterian economics and its three forces viz. innovation, knowledge, and entrepreneurship. The first dimension deals with research and innovation in AI. In the second dimension, we explore the influence of AI on the global market and the strategic objectives of the businesses and finally, the third dimension examines how AI is shaping business contexts. Additionally, the paper explores AI implications on actors and its dark sides.

Amazon's AI assistant will soon be able to speak more than just English in the U.S. The internet giant has launched a new program that will allow U.S. developers to build skills for Spanish-speaking users. In doing so, it will allow Amazon to build a more robust experience for Alexa users when it launches full Spanish-language support later this year. Amazon's AI assistant will soon be able to speak more than just English. 'We're excited to announce that now developers can start building skills for Spanish-speaking customers in the US using the Alexa Skills Kit with the new Spanish for US voice model,' the company wrote in a blog post on Monday. 'Skills that developers create now and are certified for publication will be available for participants in the Alexa Preview program, and to all customers when Alexa launches in the US with Spanish language support later this year.'

Long an obsession of science fiction writers, "artificial intelligence" in the modern era of fast-paced technological innovation is a term that is as ubiquitous as it is nebulous. For the payments technology industry, however, the term describes advanced analytical technology that has an outsized potential to improve the payments ecosystem for banks, payments processors, merchants and consumers. In fact, financial services companies will spend US$11 billion on AI in 2020, according to an analysis by IDC -- more than any other industry cited. They'll stand to make a nice return on their investment as well, according to PwC estimates. In North America alone, AI is projected to increase the GDP of the financial and professional services industry as much as 10 percent by 2030, driven by increases in both productivity and consumption.

Mark Zuckerberg seems to have landed on a solution to turn around his untrustworthy and "not quite human" public image: playing Cupid. Harking back to its humble beginnings as a tool for ranking strangers' attractiveness, Facebook has announced a new feature called Secret Crush, wherein users select the friends for whom they carry a torch. If your crush adds you to their list – and with up to nine picks allowed, your odds aren't bad – Facebook will reveal you to each other and love will assuredly bloom. But if the feeling is not reciprocated, they need never know your identity – just that one of their friends has added them as "a secret crush". It is, for sure, a more welcome notification than "It is [former colleague]'s birthday today.

Unfortunately for singletons in the United States, Facebook will not expand this feature to the U.S. until the end of 2019. So, even though the platform originated in the States and was born out of a desire to connect with others ― both romantically and otherwise -- we will have to suffer through a few more months sans Facebook's intervention in our love lives.

In this paper, we introduce an approach for leveraging available data across multiple locales sharing the same language to 1) improve domain classification model accuracy in Spoken Language Understanding and user experience even if new locales do not have sufficient data and 2) reduce the cost of scaling the domain classifier to a large number of locales. We propose a locale-agnostic universal domain classification model based on selective multi-task learning that learns a joint representation of an utterance over locales with different sets of domains and allows locales to share knowledge selectively depending on the domains. The experimental results demonstrate the effectiveness of our approach on domain classification task in the scenario of multiple locales with imbalanced data and disparate domain sets. The proposed approach outperforms other baselines models especially when classifying locale-specific domains and also low-resourced domains.

Recommender systems are personalized information access applications; they are ubiquitous in today's online environment, and effective at finding items that meet user needs and tastes. As the reach of recommender systems has extended, it has become apparent that the single-minded focus on the user common to academic research has obscured other important aspects of recommendation outcomes. Properties such as fairness, balance, profitability, and reciprocity are not captured by typical metrics for recommender system evaluation. The concept of multistakeholder recommendation has emerged as a unifying framework for describing and understanding recommendation settings where the end user is not the sole focus. This article describes the origins of multistakeholder recommendation, and the landscape of system designs. It provides illustrative examples of current research, as well as outlining open questions and research directions for the field.

Personalized recommendation algorithms learn a user's preference for an item by measuring a distance/similarity between them. However, some of the existing recommendation models (e.g., matrix factorization) assume a linear relationship between the user and item. This approach limits the capacity of recommender systems, since the interactions between users and items in real-world applications are much more complex than the linear relationship. To overcome this limitation, in this paper, we design and propose a deep learning framework called Signed Distance-based Deep Memory Recommender, which captures non-linear relationships between users and items explicitly and implicitly, and work well in both general recommendation task and shopping basket-based recommendation task. Through an extensive empirical study on six real-world datasets in the two recommendation tasks, our proposed approach achieved significant improvement over ten state-of-the-art recommendation models.

The past decade has seen artificial intelligence develop from a mere fantasy to a fully integrated part of a marketing strategy, for brands that look to differentiate and improve their customer experiences and online strategies. Take Farfetch for example, which utilized RFID-enabled clothing racks and digital mirrors to allow its customers the choice of size and colour before directly checking out online. This particular use of AI shows the seamless integration of online and offline experiences, and proves that this technology has no end to the benefits and creativity it can bring for a brands engagement efforts. Found at the core of AI technology is data and analytics, allowing brands to streamline digital ads and offer a personalized customer service. This can result in a significant lift to brands engagement efforts and empowers them to fully engage with customer at every stage of the purchase lifecycle.