As soon as Artificial Intelligence (AI) entered the scene, it became a hot topic. Businesses are widely embracing AI technologies such as Machine Learning, which simplifies the workflow for marketers in so many ways and offer a myriad of benefits. Many hotel chains, e-commerce companies, airlines, and brick-and-mortar stores didn't think twice about whether to use AI to provide better service to their customers while increasing revenue. Face recognition, Voice assistants like Siri, Alexa, and Cortana, smart selfies, and Augmented Reality – the ways in which AI is used to offer innovative features and easy solutions for everyday tasks has revolutionized the digital world and shaped web and mobile markets. Amazon and Netflix are already leveraging Machine Learning to market to specific customers.

With tech giants announcing "AI-first" business models and investment in intelligent technology skyrocketing, it may come as a surprise to learn that only 20% of surveyed enterprises are actually implementing AI this year. While there's a prevailing sentiment among business leaders that they have missed the AI boat, the reality is that the boat is still in the harbor, and there's still plenty of room aboard. Early adopters of any technology do bystanders a great service by working out the kinks. Now that products have been tested and consumers introduced, contemporary businesses braving the AI waters will be known historically as the first generation to wield this promising technology to revolutionize industries. Certain companies are expecting returns on their AI investments to reach 30% in the coming years.

Smart speakers are becoming more and more common inside of homes, offering a convenient way to get the weather, manage your calendar, and answer any questions you may have. But this year, your Amazon Echo can also help you prepare for the upcoming flu season. Conceptualized and developed by Seattle Children's Hospital and Boston Children's Hospital, the Flu Doctor skill provides a convenient way to educate yourself (and your family) about the flu vaccine. "We know that search is increasingly going to be voice-enabled and we know increasingly more and more of us are incorporating smart speakers into our lives," Dr. Wendy Sue Swanson, general pediatrician and Chief of Digital Innovation at Seattle Children's Hospital, tells Reviewed. "The benefit of Flu Doctor is to learn more about the flu in your home, in a way that maybe you hadn't before using Alexa."

The mobile app development industry is growing at a rapid pace, with more and more enterprises opting for mobile apps to empower their business operations. The introduction of Artificial Intelligence technology is a major milestone for mobile apps. And now that developers realize the need for creating differentiated apps, they are keen to embrace AI to make their apps stand apart in the competitive landscape. Several app development firms are building AI-based apps to help business enterprises to achieve their goals. With the introduction of this smart technology, a number of innovative apps ranging from automated customer service like chatbots to high-end data science apps, are already making inroads into the market.

Artificial Intelligence (AI) is the branch of computer sciences that emphasizes the development of intelligence machines, thinking and working like humans. Today, Artificial Intelligence is a very popular subject that is widely discussed in the technology and business circles. Many experts and industry analysts argue that AI or machine learning is the future – but if we look around, we are convinced that it's not the future – it is the present. With the advancement in technology, we are already connected to AI in one way or the other – whether it is Siri, Watson or Alexa. Yes, the technology is in its initial phase and more and more companies are investing resources in machine learning, indicating a robust growth in AI products and apps in the near future.

Many will be flocking to the UK capital this week – all in the name of fashion. London Fashion Week is making a return on Friday, with fashionistas from all around the world gathering in celebration of fashion, community, diversity and creativity. In light of this, I wanted to take a look at the evolution of the fashion industry and how technology is transforming the sector at a pace faster than ever before. Wearable tech has been on the market for some time now, from the earliest days of the Fitbit to the latest iterations of the Apple Watch and Google Glass. Today, fashion leaders are merging form and function to make wearables more stylish and, well, wearable.

Artificial Intelligence is the future of growth. There is sure to be at least one article in the newspaper/internet/blogs daily on the revolutionary advancements made in the field of Artificial Intelligence or its subfield disrupting standard industries like Fintech, Banking, Law, or any other. In banking domain digital banking teams of all modern banks planning to transform the customer experience with their AI based chat-driven intelligent virtual assistant i.e. bots. Amalgamating the latest technology of artificial intelligence, predictive analytics and cognitive messaging to serve millions of customers is now a new winning strategy? AI and regulation are paving the way for Fintech.

Today, 2020 Democratic presidential candidate and Massachusetts Rep. Seth Moulton is expected to introduce a bill that would limit how smart device manufacturers like Amazon and Google can collect your data. The Automatic Listening and Exploitation Act, or the ALEXA Act for short, would empower the Federal Trade Commission to seek immediate penalties if a smart device is found to have recorded user conversations without the device's wake word being triggered. For Google's home devices, for instance, that would mean recording a conversation without being prompted by "Hey, Google." Moulton's bill also addresses smart doorbells and their video capabilities. "Smart speakers and doorbells are great, but consumers should have a way to fight back when tech companies collect more data than Americans have agreed to give up," Moulton said.

Session-based Recurrent Neural Networks (RNNs) are gaining increasing popularity for recommendation task, due to the high autocorrelation of user's behavior on the latest session and the effectiveness of RNN to capture the sequence order information. However, most existing session-based RNN recommender systems still solely focus on the short-term interactions within a single session and completely discard all the other long-term data across different sessions. While traditional Collaborative Filtering (CF) methods have many advanced research works on exploring long-term dependency, which show great value to be explored and exploited in deep learning models. Therefore, in this paper, we propose ASARS, a novel framework that effectively imports the temporal dynamics methodology in CF into session-based RNN system in DL, such that the temporal info can act as scalable weights by a parallel attentional network. Specifically, we first conduct an extensive data analysis to show the distribution and importance of such temporal interactions data both within sessions and across sessions. And then, our ASARS framework promotes two novel models: (1) an inter-session temporal dynamic model that captures the long-term user interaction for RNN recommender system. We integrate the time changes in session RNN and add user preferences as model drifting; and (2) a novel triangle parallel attention network that enhances the original RNN model by incorporating time information. Such triangle parallel network is also specially designed for realizing data argumentation in sequence-to-scalar RNN architecture, and thus it can be trained very efficiently. Our extensive experiments on four real datasets from different domains demonstrate the effectiveness and large improvement of ASARS for personalized recommendation.

In matrix factorization, available graph side-information may not be well suited for the matrix completion problem, having edges that disagree with the latent-feature relations learnt from the incomplete data matrix. We show that removing these $\textit{contested}$ edges improves prediction accuracy and scalability. We identify the contested edges through a highly-efficient graphical lasso approximation. The identification and removal of contested edges adds no computational complexity to state-of-the-art graph-regularized matrix factorization, remaining linear with respect to the number of non-zeros. Computational load even decreases proportional to the number of edges removed. Formulating a probabilistic generative model and using expectation maximization to extend graph-regularised alternating least squares (GRALS) guarantees convergence. Rich simulated experiments illustrate the desired properties of the resulting algorithm. On real data experiments we demonstrate improved prediction accuracy with fewer graph edges (empirical evidence that graph side-information is often inaccurate). A 300 thousand dimensional graph with three million edges (Yahoo music side-information) can be analyzed in under ten minutes on a standard laptop computer demonstrating the efficiency of our graph update.