Key Points: – We're helping Wimbledon deliver new levels of engagement for ticket holders and 70 million online fans – Watson-powered real time match reports are expected to rival global outlets in breaking news and uncovering player insights – Watson Discovery Service is using 22 years of unstructured data to analyze an estimated 53,713,514 tennis data points – A voice-activated, watson-powered digital assistant "Fred," helps attendees find their way around the venue At the All England Lawn Tennis Club (AELTC), more than a half-million fans gather for the Wimbledon Championships every year. For two weeks in July, enthusiastic fans descend on the SW19 venue, with thousands of fans swarming onto the iconic Henman Hill to watch the tournament unfold. That's not including the more than 70 millions fans who watch the tournament online and on their televisions. All the fanfare isn't complete without the ongoing effort of the AELTC to enrich fan experiences through cutting-edge technology in new ways. For the 2017 Championships, IBM Watson is working with AELTC to deliver ticket holders and 70 million online fans with new levels of engagement and user assistance.

An Australian startup revealed its flagship product, an earpiece that can interpret 8 different languages in real-time, at a United Nations event in Switzerland on Friday. Lingmo International, a startup based in West Gosford north of Sydney, launched its TranslateOne2One earpiece at the UN's Artificial Intelligence for Good Summit in Geneva, revealing that IBM Watson machine learning technology had been used for its algorithms. Traditionally, converting one language to another orally in real-time is called "interpreting" whereas the term "translation" is reserved for processing text across languages with some delay. Lingmo founder Danny May, however, describes his product as performing "translation in real-time". And what I mean by independent is that it doesn't require any connectivity to your phone by Bluetooth or wi-fi.

On the May 8th edition of Closing Bell on CNBC, venture capitalist Chamath Palihapitiya, founder and CEO of Social Capital, created quite a stir in enterprise artificial intelligence (AI) circles, when he took on Watson, Big Blue's AI platform. "Human intelligence outperforms machine-learning applications in complex decision making routinely required during the course of care, because machines do not yet possess mature capabilities for perceiving, reasoning, or explaining," explained Ernest Sohn, a chief data scientist in Booz Allen's Data Solutions and Machine Intelligence group; Joachim Roski, a principal at Booz Allen Hamilton; Steven Escaravage, vice president in Booz Allen's Strategic Innovation Group; and Kevin Maloy, MD, assistant professor of emergency medicine at Georgetown University School of Medicine. "A health care organization that relies on a single EHR [Electronic Health Record] vendor's analytic solutions, as well as its own legacy analytics infrastructure created before the era of big data, may see limited progress," they continued. "While many machine-learning solutions are not yet mature and sophisticated enough to support complex clinical decisions, machine learning can be effectively deployed today to reduce more routine, time-consuming, and resource-intensive tasks, allowing freed-up personnel to be redeployed to support higher-end work."

On the May 8th edition of Closing Bell on CNBC, venture capitalist Chamath Palihapitiya, founder and CEO of Social Capital, created quite a stir in enterprise artificial intelligence (AI) circles, when he took on Watson, Big Blue's AI platform. "Human intelligence outperforms machine-learning applications in complex decision making routinely required during the course of care, because machines do not yet possess mature capabilities for perceiving, reasoning, or explaining," explained Ernest Sohn, a chief data scientist in Booz Allen's Data Solutions and Machine Intelligence group; Joachim Roski, a principal at Booz Allen Hamilton; Steven Escaravage, vice president in Booz Allen's Strategic Innovation Group; and Kevin Maloy, MD, assistant professor of emergency medicine at Georgetown University School of Medicine. "A health care organization that relies on a single EHR [Electronic Health Record] vendor's analytic solutions, as well as its own legacy analytics infrastructure created before the era of big data, may see limited progress," they continued. "While many machine-learning solutions are not yet mature and sophisticated enough to support complex clinical decisions, machine learning can be effectively deployed today to reduce more routine, time-consuming, and resource-intensive tasks, allowing freed-up personnel to be redeployed to support higher-end work."

In our world of high expectations and ever expanding data on individuals and brands, mastering this data and transforming it into valuable insights to inspire our human connections has become essential for brands. Take a minute and think about this: How do your customers perceive your brand? Is your brand "shy" online and only speaks when spoken to? Or is your brand overly enthusiastic and always waving its hands in your customers' inboxes and social feeds? Is your brand a captivating conversationalist that encourages interaction or one that's a little socially awkward or a little too forced?

WatsonPaths: Scenario-Based Question Answering and Inference over Unstructured Information

We present WatsonPaths, a novel system that can answer scenario-based questions. These include medical questions that present a patient summary and ask for the most likely diagnosis or most appropriate treatment. WatsonPaths builds on the IBM Watson question answering system. WatsonPaths breaks down the input scenario into individual pieces of information, asks relevant subquestions of Watson to conclude new information, and represents these results in a graphical model. Probabilistic inference is performed over the graph to conclude the answer. On a set of medical test preparation questions, WatsonPaths shows a significant improvement in accuracy over multiple baselines.

If you ask IBM about its plans for a given business opportunity--health care, financial services, pharma, even sports coverage--the answer will likely center on Watson, IBM's take on artificial intelligence, or cognitive computing, in IBM parlance. Since beating human champions in Jeopardy six years ago, Watson has been very long on promise and generated untold numbers of headlines. It's unclear as IBM (ibm) chief executive Ginni Rometty has said the company does not break that out in order to protect this crucial but nascent business. Over the past year, critics have voiced skepticism about Watson's real-world prospects especially as AI competitors--from Google (goog) to Microsoft (msft)--have brought their AI software offerings to market. The perception now is that Watson has not met lofty expectations.

IBM's Watson can apparently do everything. From manufacturing and medical treatment planning to portrait drawing and filing your taxes, there seems to be no limit to what the Jeopardy-winning AI can do. And next week, Watson will be offering its services to the Wimbledon tennis tournament. Those attending the event will be able to access a Watson-driven digital assistant named Fred via a mobile app. Fred will be able to help them navigate the courts, find food stands and vendors as well as figure out who is playing at any given time.

In a recent report, the National Retail Federation projected online sales in 2017 will grow three times faster than in-store sales. The report suggests 51% of Americans prefer to shop online rather than in stores--a figure that jumps to 67% for millennials and 56% for Gen Xers. With Amazon accounting for 43% of online sales in the US, only store sales will be disrupted more in the coming months. Additionally, eCommerce sales are also expected to reach $4 trillion by 2020 – making it 14.6% of total retail spending that year. So what does this mean for retailers?