In the banking sector, AI powers the smart chatbots that provide clients with comprehensive self-help solutions while reducing the call-centers' workload. Voice-controlled virtual assistants powered by smart tech like Amazon's Alexa are also gaining traction fast, which is no surprise: boasting a self-education feature, they get smarter every day, so you should expect tremendous improvements here. Both tools can check balances, schedule payments, look up account activity and more.

In the banking sector, AI powers the smart chatbots that provide clients with comprehensive self-help solutions while reducing the call-centers' workload. Voice-controlled virtual assistants powered by smart tech like Amazon's Alexa are also gaining traction fast, which is no surprise: boasting a self-education feature, they get smarter every day, so you should expect tremendous improvements here. Both tools can check balances, schedule payments, look up account activity and more.

The fundamentals of robotic process automation combined with machine learning capabilities to robotize the mundane tasks, plus learning to do a job even better, is what intelligent process automation all about. We have evolved from room-sized mainframes to laptops, from using stick shifts to autonomous vehicles, from personal assistants to virtual assistants, and to so much more in just the blink of an eye. The fast-paced technology-driven world has made our lives extremely convenient now. We see breakthroughs happening in our lives with technological applications doing the heavy lifting most of the time. This level of sophistication and ease is only possible because of industries becoming digitized.

Alexa, Amazon's poster child for connected home devices, has recently received yet another update. Researchers have developed a method of implementing improved natural language processing in the model, cutting the error rate by 8%. This was done through a combination of transfer learning and utilising AI to generate'embeddings' of words. Transfer learning was implemented with a neural network to train an AI on a large dataset of annotated speech samples, thus enabling researchers to bootstrap training in a new domain with sparse data. This technique taps into millions of unnannotated interactions with Alexa, fueled by data from users of Amazon's Echo products.

Understanding the needs of a variety of distinct user groups is vital in designing effective, desirable dialogue systems that will be adopted by the largest possible segment of the population. Despite the increasing popularity of dialogue systems in both mobile and home formats, user studies remain relatively infrequent and often sample a segment of the user population that is not representative of the needs of the potential user population as a whole. This is especially the case for users who may be more reluctant adopters, such as older adults. In this paper we discuss the results of a recent user study performed over a large population of age 50 and over adults in the Midwestern United States that have experience using a variety of commercial dialogue systems. We show the common preferences, use cases, and feature gaps identified by older adult users in interacting with these systems. Based on these results, we propose a new, robust user modeling framework that addresses common issues facing older adult users, which can then be generalized to the wider user population.

The problem of completing high-dimensional matrices from a limited set of observations arises in many big data applications, especially, recommender systems. Existing matrix completion models generally follow either a memory- or a model-based approach, whereas, geometric matrix completion models combine the best from both approaches. Existing deep-learning-based geometric models yield good performance, but, in order to operate, they require a fixed structure graph capturing the relationships among the users and items. This graph is typically constructed by evaluating a pre-defined similarity metric on the available observations or by using side information, e.g., user profiles. In contrast, Markov-random-fields-based models do not require a fixed structure graph but rely on handcrafted features to make predictions. When no side information is available and the number of available observations becomes very low, existing solutions are pushed to their limits. In this paper, we propose a geometric matrix completion approach that addresses these challenges. We consider matrix completion as a structured prediction problem in a conditional random field (CRF), which is characterized by a maximum a posterior (MAP) inference, and we propose a deep model that predicts the missing entries by solving the MAP inference problem. The proposed model simultaneously learns the similarities among matrix entries, computes the CRF potentials, and solves the inference problem. Its training is performed in an end-to-end manner, with a method to supervise the learning of entry similarities. Comprehensive experiments demonstrate the superior performance of the proposed model compared to various state-of-the-art models on popular benchmark datasets and underline its superior capacity to deal with highly incomplete matrices.

The increasing interest in user privacy is leading to new privacy preserving machine learning paradigms. In the Federated Learning paradigm, a master machine learning model is distributed to user clients, the clients use their locally stored data and model for both inference and calculating model updates. The model updates are sent back and aggregated on the server to update the master model then redistributed to the clients. In this paradigm, the user data never leaves the client, greatly enhancing the user' privacy, in contrast to the traditional paradigm of collecting, storing and processing user data on a backend server beyond the user's control. In this paper we introduce, as far as we are aware, the first federated implementation of a Collaborative Filter. The federated updates to the model are based on a stochastic gradient approach. As a classical case study in machine learning, we explore a personalized recommendation system based on users' implicit feedback and demonstrate the method's applicability to both the MovieLens and an in-house dataset. Empirical validation confirms a collaborative filter can be federated without a loss of accuracy compared to a standard implementation, hence enhancing the user's privacy in a widely used recommender application while maintaining recommender performance.

Google Assistant hasn't been quite so... assistive lately. Numerous users reported that Google Assistant notifications have been broken on Android, preventing reminders and other important alerts from getting through. The problem appears to have started with updates to the Google app over the past several days, particularly the most recent (9.0.6). Some had success by uninstalling updates or clearing their app cache, but it didn't appear to have been truly fixed until a server-side update arrived on January 28th. We've asked Google for comment, although the bug didn't appear to affect iOS or other platforms.

Is voice technology going to become the next big opportunity for brands to engage with consumers? As we enter an era where voice assistants are becoming more popular (Amazon Alexa, Google Home, Apple HomePod, among others), new doors open for marketing and communications professionals. Experts say voice assistants can become the first interactive tool at home that provides brands the capability to dynamically offer up ads in the future that could be user controlled. In this interactive Master Class, we will understand voice technology as part of artificial intelligence, discuss real business examples, and conclude with a discussion on the future of this technology and the marketing field as we know it.

We've been sharing a lot of information about why we're really excited about HiveManager Shortcuts with Amazon Alexa. While we don't think IT Managers are going to replace 100 percent of browser-based management with voice, we do think it can be a great addition to help simplify a number of tasks. Let's watch a video to see some of the ways HiveManager Shortcuts with Amazon Alexa can simplify a project for IT departments. One of the aspects of IT management that HiveManager Shortcuts with Amazon Alexa integration simplifies is onboarding of new access points. Instead of manually typing in serial numbers of new access points, you can use an Alexa-enabled device to add it verbally.