The majority of NHS users would rather discuss their hospital and GP appointments with an artificial intelligence service instead of a human. That's according to new research from technology startup EBO.ai, which explores the growing role digital communication tools play in the NHS. It found that more than three quarters (76%) of NHS users would be happy to receive automated reminders from an AI-powered virtual assistant, compared to the 58% who'd rather interact with a human. Figures from NHS England claim that missed appointments cost the health service over £216 annually, and many people believe that AI technology could help minimize this cost. Dr. Gege Gatt, CEO of EBO.ai, said: "The NHS has already invested millions in the latest technologies, but the enormous potential of AI remains largely untapped. "The adoption we have seen thus far has focused on primary patient care including assessment and diagnosis, but AI can improve patient experiences outside the treatment room too." "Virtual assistants help patients manage their care 24/7, with no need to wait for opening hours or spend time on hold in a phone queue.

Using deep learning and artificial intelligence (AI), FAU scientists are bringing to life one of about a handful of these quadruped robots in the world. Astro is unique because he is the only one of these robots with a head, 3D printed to resemble a Doberman pinscher, that contains a (computerized) brain. What would you get if you combined Apple's Siri and Amazon's Alexa with Boston Dynamic's quadruped robots? You'd get "Astro," the four-legged seeing and hearing intelligent robodog. Using deep learning and artificial intelligence (AI), scientists from Florida Atlantic University's Machine Perception and Cognitive Robotics Laboratory (MPCR) in the Center for Complex Systems and Brain Sciences in FAU's Charles E. Schmidt College of Science are bringing to life one of about a handful of these quadruped robots in the world.

Written by Dr. Christine Izuakor for Veriato, a cybersecurity company Artificial intelligence (AI) is used all around us and if you've used some sort of voice activated technology to make your life easier, then there was likely some element of AI involved. Some of the most notable examples include Siri, Amazon Alexa, Google Assistant and Tesla semi-autonomous vehicles. Individual consumers no longer have to fumble around in the dark to flip the light switch at home, manually search playlists for songs, or type in a password to get into smartphones. Similarly, businesses can now analyze millions of data records and find trends that can help them predict things like when their assets may require maintenance or what purchasing decisions customers are likely to make. Thanks to AI, there are automation and optimization solutions for almost everything – including some of our most significant technical challenges.

Combinatorial linear semi-bandits (CLS) are widely applicable frameworks of sequential decision-making, in which a learner chooses a subset of arms from a given set of arms associated with feature vectors. Existing algorithms work poorly for the clustered case, in which the feature vectors form several large clusters. This shortcoming is critical in practice because it can be found in many applications, including recommender systems. In this paper, we clarify why such a shortcoming occurs, and we introduce a key technique of arm-wise randomization to overcome it. We propose two algorithms with this technique: the perturbed C${}^2$UCB (PC${}^2$UCB) and the Thompson sampling (TS). Our empirical evaluation with artificial and real-world datasets demonstrates that the proposed algorithms with the arm-wise randomization technique outperform the existing algorithms without this technique, especially for the clustered case. Our contributions also include theoretical analyses that provide high probability asymptotic regret bounds for our algorithms.

Self-attentional models are a new paradigm for sequence modelling tasks which differ from common sequence modelling methods, such as recurrence-based and convolution-based sequence learning, in the way that their architecture is only based on the attention mechanism. Self-attentional models have been used in the creation of the state-of-the-art models in many NLP tasks such as neural machine translation, but their usage has not been explored for the task of training end-to- end task-oriented dialogue generation systems yet. In this study, we apply these models on the three different datasets for training task-oriented chatbots. Our finding shows that self-attentional models can be exploited to create end-to-end task-oriented chatbots which not only achieve higher evaluation scores compared to recurrence-based models, but also do so more efficiently.

We present Distributed Equivalent Substitution (DES) training, a novel distributed training framework for recommender systems with large-scale dynamic sparse features. Our framework achieves faster convergence with less communication overhead and better computing resource utilization. DES strategy splits a weights-rich operator into sub-operators with co-located weights and aggregates partial results with much smaller communication cost to form a computationally equivalent substitution to the original operator. We show that for different types of models that recommender systems use, we can always find computational equivalent substitutions and splitting strategies for their weights-rich operators with theoretical communication load reduced ranging from 72.26% to 99.77%. We also present an implementation of DES that outperforms state-of-the-art recommender systems. Experiments show that our framework achieves up to 83% communication savings compared to other recommender systems, and can bring up to 4.5x improvement on throughput for deep models.

Communication is key they say, or at least I was told so when I was younger. However, the older I got, the more proof I saw to this age-old saying. We more often than not end up misunderstanding the other that helped create opportunities that allowed ambiguity to prosper. Communication is important, and it gets a lot more important when we're talking business. We're taught back in business 101 that effective and efficient communication is imperative for a business owner if you're looking to run a successful business.

According to a reliable source familiar with the company's plans, Google Assistant on Pixel 4 will be able to step in for you while you're on hold during a phone call. It's the latest feature Google is adding to make the day-to-day phone experience better on the Pixel using supplemental Assistant smarts… Whenever you're on a call with a business and end up on hold with music playing in the background, our source tells us you'll be able to tap a button on the display to tell Assistant you're on hold. You'll be able to then to take your attention away from the call, and Google Assistant will let you know when there's an actual human back on the other end of the call. Exact details of how it works is unclear for now, and we're told the feature is still relatively early in development, so it's likely that some aspects could change before launch. Our source tells us they're confident it will come eventually, but that they would be "surprised" if it's available on the Pixel 4 on day one.

With the rapid technological advancements of machine learning and artificial intelligence in every sphere of our lives, every industry is now experiencing a quantum shift in terms of the services being offered to the end consumer. Artificial Intelligence, or otherwise famously known as AI, deserves your attention and is a reality that is wholeheartedly being accepted by a large chunk of population knowingly or unknowingly. Although it has been a part of our lives since quite some time now, be it spam email detection or recommended videos in YouTube & Netflix, it is largely being accepted by the general population now. AI can be dated back to almost six decades now and it is empowering everyone with new and improved products and services. Artificial Intelligence as a subject is extremely vast and highly applicable in almost every sphere of life, especially in the mobile app industry.

Artificial intelligence (AI) is the human-like intelligence exhibited by machines. AI makes it possible for machines to learn from experience, adapt to new information, and accomplish specific tasks or recognize patterns in massive volumes of data. Don't worry, though, the machines haven't taken over, not yet at least. However, they are quickly infiltrating our lives, affecting how we live, work, and entertain ourselves. From voice-powered personal assistants, like Siri and Alexa, to more underlying and fundamental technologies, such as behavioral algorithms, suggestive searches, and autonomously-powered self-driving vehicles boasting powerful predictive capabilities, there are several great examples of the applications of artificial intelligence that already exist in the world around us.