Startup Diagnoss has developed an artificial intelligence-based coding assistant to help automate the painstaking process of medical coding and billing. The Diagnoss AI medical coding engine acts as a "sidebar" to electronic health records (EHRs) and uses machine learning to improve a clinician's accuracy. The tool provides real-time feedback to medical practices during the administrative process and helps to reduce coding errors on claims. Abboud Chaballout, founder and CEO of Berkeley, California-based Diagnoss, compares the AI tool to an assistant whispering in a doctor's ear. The AI tool works similarly to the Grammarly AI grammar-checking tool.

Users of recommender systems often behave in a non-stationary fashion, due to their evolving preferences and tastes over time. In this work, we propose a practical approach for fast personalization to non-stationary users. The key idea is to frame this problem as a latent bandit, where the prototypical models of user behavior are learned offline and the latent state of the user is inferred online from its interactions with the models. We call this problem a non-stationary latent bandit. We propose Thompson sampling algorithms for regret minimization in non-stationary latent bandits, analyze them, and evaluate them on a real-world dataset. The main strength of our approach is that it can be combined with rich offline-learned models, which can be misspecified, and are subsequently fine-tuned online using posterior sampling. In this way, we naturally combine the strengths of offline and online learning.

That Cyber Monday special from Best Buy sounds really enticing. Which may not be a catch at all, but it is something worth considering. Because if you have Amazon Echo speakers throughout your home, you won't be able to use them to talk to your new TCL TV. As all TV sales now tend to be "smart," they operate on different software platforms, ones you need to consider before making your purchase. Before you buy a Roku-branded TV, I have three words for you: "Wonder Woman 1984."

When most people hear the term Artificial Intelligence, the first thing they usually think of is robots or some famous science fiction movie like the Terminator depicting the rise of AI against humanity. Artificial intelligence refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning, analyzing, comprehending, and problem-solving. The applications of artificial intelligence in the real-world are perhaps more than what many people know. The ideal characteristic of artificial intelligence is its ability to rationalize and take actions that have the best chance of achieving a specific goal or defined operations. With the advancements of the human mind and their deep research into the field, AI is no longer just a few machines doing basic calculations.

Two of Glasgow's leading scientists will develop cutting-edge Artificial Intelligence (AI) technology thanks to a £20 million UK Government cash boost. The Scottish projects, at the University of Glasgow and University of Strathclyde, are among fifteen innovative projects receiving the new Turing AI fellowships as part of the UK government's ambition to establish the UK as a world leader in AI and support researchers to scale up their innovations. Dr Antonio Hurtado, University of Strathclyde, received £1.16 million. He aims to meet the growing demand across the UK economy to process large volumes of data fast and efficiently, while minimising the energy required to do so. His AI technology will use laser light, similar to those used in supermarket checkouts, to perform complex tasks at ultrafast speed – from weather forecasting to processing images for medical diagnostics.

The banking industry is primarily a world of computers and networks. It's boggling that the bulk of the world's wealth is stored in databases, and transactions are simply the exchanges of information over networks. As impressive -- or scary -- as that might sound, artificial intelligence technologies aim to further revolutionize the way banking is done and the relationships between banks and their customers' experience. Banks never seem to be open when you need them most, such as later in the day or on holidays and weekends. Fortunately, AI in banking is one of the most impactful applications of artificial intelligence through the use of conversational assistants, or chatbots, to engage customers 24/7.

In this paper, we propose and experiment with techniques for extreme compression of neural natural language understanding (NLU) models, making them suitable for execution on resource-constrained devices. We propose a task-aware, end-to-end compression approach that performs word-embedding compression jointly with NLU task learning. We show our results on a large-scale, commercial NLU system trained on a varied set of intents with huge vocabulary sizes. Our approach outperforms a range of baselines and achieves a compression rate of 97.4% with less than 3.7% degradation in predictive performance. Our analysis indicates that the signal from the downstream task is important for effective compression with minimal degradation in performance.

Rapid advancements in the E-commerce sector over the last few decades have led to an imminent need for personalised, efficient and dynamic recommendation systems. To sufficiently cater to this need, we propose a novel method for generating top-k recommendations by creating an ensemble of clustering with reinforcement learning. We have incorporated DB Scan clustering to tackle vast item space, hence in-creasing the efficiency multi-fold. Moreover, by using deep contextual reinforcement learning, our proposed work leverages the user features to its full potential. With partial updates and batch updates, the model learns user patterns continuously. The Duelling Bandit based exploration provides robust exploration as compared to the state-of-art strategies due to its adaptive nature. Detailed experiments conducted on a public dataset verify our claims about the efficiency of our technique as com-pared to existing techniques.

Most of the existing medicine recommendation systems that are mainly based on electronic medical records (EMRs) are significantly assisting doctors to make better clinical decisions benefiting both patients and caregivers. Even though the growth of EMRs is at a lighting fast speed in the era of big data, content limitations in EMRs restrain the existed recommendation systems to reflect relevant medical facts, such as drug-drug interactions. Many medical knowledge graphs that contain drug-related information, such as DrugBank, may give hope for the recommendation systems. However, the direct use of these knowledge graphs in the systems suffers from robustness caused by the incompleteness of the graphs. To address these challenges, we stand on recent advances in graph embedding learning techniques and propose a novel framework, called Safe Medicine Recommendation (SMR), in this paper. Specifically, SMR first constructs a high-quality heterogeneous graph by bridging EMRs (MIMIC-III) and medical knowledge graphs (ICD-9 ontology and DrugBank). Then, SMR jointly embeds diseases, medicines, patients, and their corresponding relations into a shared lower dimensional space. Finally, SMR uses the embeddings to decompose the medicine recommendation into a link prediction process while considering the patient's diagnoses and adverse drug reactions. To our best knowledge, SMR is the first to learn embeddings of a patient-disease-medicine graph for medicine recommendation in the world. Extensive experiments on real datasets are conducted to evaluate the effectiveness of proposed framework.

Learning to control the structure of sentences is a challenging problem in text generation. Existing work either relies on simple deterministic approaches or RL-based hard structures. We explore the use of structured variational autoencoders to infer latent templates for sentence generation using a soft, continuous relaxation in order to utilize reparameterization for training. Specifically, we propose a Gumbel-CRF, a continuous relaxation of the CRF sampling algorithm using a relaxed Forward-Filtering Backward-Sampling (FFBS) approach. As a reparameterized gradient estimator, the Gumbel-CRF gives more stable gradients than score-function based estimators. As a structured inference network, we show that it learns interpretable templates during training, which allows us to control the decoder during testing. We demonstrate the effectiveness of our methods with experiments on data-to-text generation and unsupervised paraphrase generation.