OpenText is launching an artificial intelligence platform called Magellan that hopes to use the company's knowhow with unstructured and semi-structured data and open source to compete with the likes of IBM Watson. At EnterpriseWorld, OpenText's conference in Toronto, the company outlined its artificial intelligence and machine learning platform. OpenText focuses on enterprise information management and has a portfolio that extends into content management for industries, customer experience, and data discovery. In January, OpenText completed the purchase of Dell-EMC's enterprise content unit, which includes Documentum. For the third quarter, Open Text reported revenue of $593 million, up 35 percent from a year ago.

Not many tech CEO's have the bravado to stand before an audience of their most loyal customers and promise them a game-changing a product, claiming that it will blow a ground-breaking technology like IBM Watson away. But that is exactly what OpenText CEO Mark Barrenechea did just over a year ago. Today, standing before an audience of more than 5000 gathered at Enterprise World in Toronto, Barrenechea delivered the goods. OpenText Magellan is a cognitive computing platform that provides users with machine-assisted decision making, automation, and business optimization at a price they can afford. Magellan was built to deliver actionable insights and intelligence from big data, big content, information streaming from sensors, the Internet of Things and more.

IBM's Watson computer has many talents. It discussed music with Bob Dylan, beat Ken Jennings at Jeopardy! and even ran a food truck. Now, the artificial intelligence project has picked up another skill: bartending. Working with foodies and chefs from Bon Apétit magazine and the Institute of Culinary Education, IBM programmers put the software through culinary school. The project, known as "Chef Watson," generates original recipes based on ingredients a user selects, Christopher Trout writes for Engadget. "The system doesn't look at ingredients the same way chefs do," software engineer and chef Florian Pinel, who helped IBM develop Chef Watson, says in a video showcasing the cloud-based cook.

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.