Artificial-intelligence technology is all around us, in the form of voice assistants like Siri, Alexa, Cortana, and more. But this technology extends beyond recognizing a song or telling us the weather. At last year's Business Insider IGNITION conference, David Kenny, the general manager of IBM's Watson division, discussed the AI project. According to Kenny, Watson is most advanced in the health field. One example of its success potentially saved a life.

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.

What can bots do right now to make their user experience better than that of visiting a website? They can personalize the experience -- either with user context or AI. See, most sites try to appeal to the general user by splitting the homepage in one of two ways: by sales or by product category. They'd prominently display brands that have expressed their efforts in these areas -- like Nike or Yesah They have awesome watches, btw. This might be the case when choosing an outfit for a formal event.

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.

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?

We examine the role of memorization in deep learning, drawing connections to capacity, generalization, and adversarial robustness. While deep networks are capable of memorizing noise data, our results suggest that they tend to prioritize learning simple patterns first. In our experiments, we expose qualitative differences in gradient-based optimization of deep neural networks (DNNs) on noise vs. real data. We also demonstrate that for appropriately tuned explicit regularization (e.g., dropout) we can degrade DNN training performance on noise datasets without compromising generalization on real data. Our analysis suggests that the notions of effective capacity which are dataset independent are unlikely to explain the generalization performance of deep networks when trained with gradient based methods because training data itself plays an important role in determining the degree of memorization.

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?

The Building Cognitive Applications with IBM Watson Services series is a seven-volume collection that introduces IBM Watson cognitive computing services. The series includes an overview of specific IBM Watson services with their associated architectures and simple code examples. Each volume describes how you can use and implement these services in your applications through practical use cases. Whether you are a beginner or an experienced developer, this collection provides the information you need to start your research on Watson services. If your goal is to become more familiar with Watson in relation to your current environment, or if you are evaluating cognitive computing, this collection can serve as a powerful learning tool.