Topological Learning with Ayasdi: Ayasdi has a unique approach to machine learning and data analysis using topology. This framework represents a revolutionary way to look at and understand data that is orthogonal but complementary to traditional machine learning and statistical tools. In this presentation I will show you what is meant by this statement: How does topology help with data analysis? Why would you use topology? I will illustrate with both synthetic examples and problems we've solved for our clients.

Accenture has chosen an artificial intelligence-powered virtual agent called Amelia, to push cognitive machine learning towards businesses like banks and insurance companies. Amelia, developed by IT automation provider IPsoft, is a self-learning cognitive agent you can actually talk with in natural language. In other words, she can hold a back and forth conversation and, as well as answering questions, she can manage processes for you. For instance, in banking, she could open up an account for you. Edwin van Bommel, chief cognitive officer at IPsoft told IBTimes UK: "Amelia will answer your questions in natural language. It will all be very conversational, as opposed to AI-based services like Siri where each utterance is recognised as a new separate question."

After reading thousands of romance novels, Google Brain's AI has a new talent in writing eerie post-modern poems. Artificial Intelligence has proven to be fully capable of doing what human beings can do. Now Google has discovered that their AI has a new talent in writing haunting post-modern poetry. Google had previously encountered a problem with the artificial intelligence having a rather concise diction. Although the AI had precise grammar, the Google Brain Team wanted it to be more conversational in its interactions.

For a conference that has been used to advance Android and Chrome, and introduce Google Glass with users parachuting from a plane, people are expecting to hear about virtual reality and artificial intelligence at this week's Google I/O conference. "This year's focus will be virtual reality, no doubt," said Jeff Kagan, an independent industry analyst. "I believe Google wants to be a leader in virtual reality and, in this marketplace where there are no leaders yet, I think they will try and secure their place in the front of the line." Kagan said Google may even roll out its own early- generation virtual reality device at I/O, the company's annual developer conference, which generally draws more than 6,000 attendees. The conference is being held from Wednesday through Friday in Mountain View, Calif., the same city where Google is headquartered.

Vasant Dhar is a professor at the Stern School of Business and the Center for Data Science at New York University. The widespread conversations about AI took a new turn in March 2016 when Microsoft launched, then quickly unplugged, Tay, its artificial intelligence chat robot. Within 24 hours, interactions on Twitter had turned the bot, modeled after a teenage girl, into a "Hitler-loving sex robot." This controversy, on the heels of the Feb. 14, 2016, accident of Google's self-driving robot car, has ignited a new debate over artificial intelligence. How should we design intelligent learning machines that minimize undesirable behavior?

Detroit automakers that viewed Silicon Valley as a "serious threat" just a few years ago are now jumping into deals, acquisitions and investments with West Coast tech companies, describing some of the partnerships as "getting married." The new relationships promise to bring the tech and automotive industries closer as they rush to develop self-driving cars, ride-sharing partnerships and to take advantage of other cutting-edge technology. The change from potential adversaries to partnerships illustrates a growing awareness that neither industry is likely to conquer the other anytime soon and that they need each other to evolve at the speed necessary to remain competitive. But like any successful marriage, the two parties must recognize their differences and figure out how to work more closely together, said Xavier Mosquet, a Detroit-based senior partner in the automotive practice of the Boston Consulting Group. These are industries that move at vastly different paces, operate in entirely different regulatory environments and come from different corporate cultures, he pointed out.

The purpose of testing analysis in predictive analytics is to compare the response of a predictive model against the actual target values in an independent testing set. There are several techniques that can be used for testing the performance of a predictive model. Receiver operating characteristic (ROC) curve is one of the most useful testing methods for binary classification problems, since it provides a comprehensive and visually attractive way to summarize the accuracy of predictions. By moving the decision threshold, we change the number of instances classified as positives and negatives. If the score of an instance is greater or equal to the threshold, then it will be classified as positive.

The spring event season is coming to a close. And as usual, I'm feeling the rush of elation that comes from learning about exciting new technology, discovering smart new start-ups, and hearing industry experts talk about concepts like IoT, cognitive analytics, machine learning, neural networks and more. So many of us got into the IT industry because of technology's potential to change the world as we know it, so seeing first-hand the kind of technology capable of bringing that promise to fruition is great. But this year, something else accompanied the elation -- something I don't normally feel coming off the high of the event season. That's because when I talk with new and prospective customers, most of the conversations still revolve around the challenges of yesterday, to say nothing of today and tomorrow.

Continuing the tradition of Machine Learning Summer Schools, this year's summer school is going to be held at the University of Cádiz, in Cádiz, Spain, from 11-21th May, 2016. The MLSS is a course offered to graduate students, researchers and professionals. The courses consists of lectures by respected researchers who come from the industry as well as from academia. The lectures will touch several fundamental as well as advanced concepts related to, but not limited to, machine learning, data analysis and inference. There will also be tutorials, which will concentrate on the practical aspect of machine learning.

While there's no doubt that bots are having a moment, they've been around since the beginning of the Internet, in 1993, when Eggdrop was developed to moderate Internet Relay Chat. The reason for all the new interest is the belief that the emergence of the cloud, big data, machine learning, and AI, will enable a rapid increase in the functionality of bots. That said, early reviews for Messenger bots indicate it's off to a rocky start. Which is better than the experience of Microsoft's AI Twitter bot "Tay", which learned to be a racist in less than 24 hours. So what does 2016 hold for the world of bots?