A machine that can read your mind and convert thoughts into speech has been built and tested by scientists. The creation is being heralded as'life-changing' for those unable to communicate, such as stroke victims. The pioneering system combines the power of speech synthesisers and AI (artificial intelligence) to turn brain activity into intelligible sentences. It is based on the same technology used by Amazon Echo and Apple Siri. It offers hope of patients paralysed after a stroke or accident communicating with friends and loved ones - despite being unable to speak.

These findings were published today in Scientific Reports. "Our voices help connect us to our friends, family and the world around us, which is why losing the power of one's voice due to injury or disease is so devastating," said Nima Mesgarani, PhD, the paper's senior author and a principal investigator at Columbia University's Mortimer B. Zuckerman Mind Brain Behavior Institute. "With today's study, we have a potential way to restore that power. We've shown that, with the right technology, these people's thoughts could be decoded and understood by any listener." Decades of research has shown that when people speak -- or even imagine speaking -- telltale patterns of activity appear in their brain.

The Super Bowl is just around the corner, and you know what that means: awesome game day food, a whole lineup of unusually interesting commercials, and of course, the most exciting event of the year for football fans, all while gathered around the big screen. Even cord-cutters won't have to miss out on the action. In fact, you can tune in on the cheap with the help of a pretty sweet discount on Amazon's Fire TV Cube, down to $80 today from a list price of $120. This cube-shaped streaming device is best-known for its voice control, with the smarts of Amazon's Alexa digital assistant built right in. You can turn on your TV, play videos and music, adjust volume, change the channel, check the weather and news, and more with just the sound of your voice.

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.