At Satisfi, we are on the hunt for ways to improve customer engagement in retail spaces and change the way brands and consumers interact. By tapping into the cognitive computing smarts of IBM Watson, coupled with our intelligent engagement platform, our goal is to uncover new ways retailers can reach customers and deliver the personalized experiences they crave.Today, Satisfi has teamed up with IBM and Macy's to unveil the pilot of Macy's On Call, a first-of-its kind, in-store shopping assistant powered by artificial intelligence. Using our platform and Watson's Natural Language Classifier and Language Translation APIs, we've built a tool to help shoppers easily access the information they need as they shop and navigate the store. Consumers can ask questions in natural language and seek out information in-store, all from the palms of their hands.Macy's On Call is being piloted at 10 Macy's across the country. In response, the tool will deliver a relevant response and the location of that product in the store.

IBM's Watson may be putting its cognitive muscle to work battling cancer and cybercriminals, but it's no slouch at shopping, either. On Wednesday, retail brand Macy's announced that it's testing out a new mobile service that lets in-store shoppers ask Watson for help. Dubbed Macy's On-Call, the tool gives smartphone-equipped shoppers a way to ask Watson questions about a store's products, services and facilities by typing their questions into a mobile browser. It's delivered through location-based engagement software from IBM partner Satisfi, which accesses Watson from the cloud, and it works in both English and Spanish. Natural-language processing allows shoppers to ask questions in their own words.

Most companies know by now that the key to making smart and strategic decisions is to look at both current and past data as a cornerstone for future business. Business intelligence teams and other analysts are brought on to enable more efficient decision making across every department. This can lead to visible changes for customers or viable improvements to process for employees. Advances in computer vision have opened up opportunities to apply data like never before. As artificial intelligence has become an increasingly popular topic of late and corresponding neural networks have improved, it's a great time to revisit how – and when – your company is applying its data.

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.

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.

It's a privilege to have Rama Akkiraju, IBM distinguished engineer and master inventor, participate as a Vision and Opportunity panelist at the 2016 Sentiment Analysis Symposium. I organize the symposium – this year's event takes place July 12 in New York – and recognize the many ways IBM has, over the years, expanded what's possible in the realm of what I'd characterize as "human data." "My team at IBM has been focused on developing technology to better understand people at a deeper level based on sentiment, emotion, attitude, and personality," said Rama. "With our work with Watson APIs – such as Tone Analyzer, Personality Insights, Emotion Analysis, and Sentiment Analysis – we're working to enable more compassion, engagement, and personalization in conversations across various channels." IBM's Marie Wallace, a 2014 sentiment symposium speaker, relates in a blog article that she "joined IBM in 2001 to build the next generation of NLP technology for IBM… the 3rd generation of IBM LanguageWare, which initially started back in the '80s." And I wrote, myself, in a 2008 InformationWeek article, BI at 50 Turns Back to the Future, about 1950s work by IBM researcher Hans Peter Luhn on the creation of business intelligence via text analysis.