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?

Prior knowledge, including data from previous experiments and physical models, and relevant features are used to describe the materials. This information is used within a machine learning framework to make predictions that include error estimates. The results are used by an experimental design tool (for example, Global optimization) that suggests new experiments (synthesis and characterization) performed in this work, with the dual goals of model improvement and materials discovery. The results feed into a database, which provides input for the next iteration of the design loop. The green arrows represent the step-wise approach of the state-of-art using experiments or calculations, although few studies have demonstrated feedback.

You have heard of a thing called the Internet. Not really sure what it does, but apparently it is going to be really cool and will get really big with time. I choose that year, because that is when I first connected to the internet ( using Compuserve and a VERY slow dial-up modem). In any case, since you are reading this post, you know now, that things have gone far beyond anything we could have imagined back then in 1994. We could really say that things have grown Exponentially.

Paul English, was one of the founders of Kayak, a travel services metasearch engine, that was sold to Priceline group in 2013. Paul English has just launched Lola, a novel approach at intelligent travel assistants, that incorporates machine learning but also uses a small team of human travel agents. The artificial intelligence component analyses Gmail headers on authentication, and derives information on airline, hotel and travel preferences. There is also a sign up profile that asks a series of questions from the user, aimed at personalisation of the service. All of this information is served to a cluster of human travel agents, who reply to the users in an asynchronous chat based interface.