This weekend saw Michael Hsu, a front-end and back-end developer, and part-time university lecturer in California, win the Best use of Watson award at the AT&T Shape Hackathon in San Francisco. Michael won the hackathon with his app focused on the Pokémon Go game. The game, using augmented reality and GPS, allows players to capture, battle, and train virtual creatures, called Pokémon, who appear on device screens as though in the real world. In this video you can see Michael, using IBM's Watson IoT platform and the Watson Visual Recognition service to take periodical screenshots, identify the Pokemon characters in them, and alert other users to where the characters are.

Under Armour, through a strategic partnership with IBM, released the UA Record application with insights powered by IBM Watson.

String just means string and number means number," according to Rosenwasser. Synopsys releases Coverity 8.5 Version 8.5 of Coverity, the static analysis tool of Synopsys, has changes and updates to the security analysis and reporting capabilities of the product. Python Serverless Microframework for AWS The AWS Developer Tools team announced the preview of the Python Serverless Microframework for AWS, which makes it possible to run and create API applications without managing any servers. Future changes to Minikube include native hypervisor support for OS X and Windows, improved support for Kubernetes features, and configurable versions of Kubernetes.

Microsoft has rolled out the beta release of TypeScript 2.0. Developers can get it after downloading TypeScript 2.0 Beta for Visual Studio 2015, which will require VS 2015 Update 3. This release includes new features like a workflow for getting TypeScript type definition files. "Null and undefined are two of the most common sources of bugs in JavaScript," and before this release, null and undefined were in the domain of every type. "If you had a function that took a string, you couldn't be sure from the type alone of whether you actually had a string--you might actually have null."

Pharma's digital health ambitions: Where are the opportunities and what's hindering progress? Looking to the future, Michael Doherty, head of strategic innovation for pharma development at Roche, said: "We are heading to a new model of drug development that will be more flexible, data will be much more continual and contextual...It won't be...

Watson combines a number of cognitive techniques to help you build and train a bot - defining intents and entities and crafting dialog to simulate conversation. The system can then be further refined with supplementary technologies to make the system more human-like or to give it a higher chance of returning the right answer. Watson Conversation allows you to deploy a range of bots via many channels, from simple, narrowly focused Bots to much more sophisticated, full-blown virtual agents across mobile devices, messaging platforms like Slack, or even through a physical robot.

With an eye toward taking the healthcare supply chain to new levels, UPMC on July 8 introduced Pensiamo, a new company whose goal is to help hospitals improve supply chain performance, where costs are high and getting higher, second only to labor costs. Pensiamo is an Italian word. We can provide insights that others cannot," UPMC's James Szilagy told Healthcare IT News. Pensiamo starts up with 150 employees, spinning out certain functions of UPMC's supply chain into the new company. For now, UPMC itself is the sole customer, but Szilagy said there has been strong interest from others. "So now that we have the organization stood up, we can take them to the next level," he said. "Many healthcare entities across the country have not invested in their supply chain organizations over the years, so they are not as far down the maturity curve as UPMC is.

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Community-based Question Answering (CQA) sites play an important role in addressing health information needs. However, a significant number of posted questions remain unanswered. Automatically answering the posted questions can provide a useful source of information for online health communities. In this study, we developed an algorithm to automatically answer health-related questions based on past questions and answers (QA). We also aimed to understand information embedded within online health content that are good features in identifying valid answers. Our proposed algorithm uses information retrieval techniques to identify candidate answers from resolved QA. In order to rank these candidates, we implemented a semi-supervised leaning algorithm that extracts the best answer to a question. We assessed this approach on a curated corpus from Yahoo! Answers and compared against a rule-based string similarity baseline. On our dataset, the semi-supervised learning algorithm has an accuracy of 86.2%. UMLS-based (health-related) features used in the model enhance the algorithm's performance by proximately 8 %. A reasonably high rate of accuracy is obtained given that the data is considerably noisy. Important features distinguishing a valid answer from an invalid answer include text length, number of stop words contained in a test question, a distance between the test question and other questions in the corpus as well as a number of overlapping health-related terms between questions. Overall, our automated QA system based on historical QA pairs is shown to be effective according to the data set in this case study. It is developed for general use in the health care domain which can also be applied to other CQA sites.