Internet2's Senior Vice President and Chief Innovation Officer Florence Hudson will present key information and communications technology trends for research and education enabling an endless frontier of innovation. Topics covered will include the Internet of Things, healthcare and life sciences, precision medicine, the cancer moonshot, and smart campuses and cities. This will enable a discussion on how we can all become an integral part of developing and implementing new solutions that enrich our lives and engagements through technology and business model innovations.

Research conducted since 2013 has revealed multiple unprecedented and powerful means of influence that internet search engines make possible - so powerful, in fact, that the search engine can reasonably be called the most powerful mind control device ever invented. These new means of influence include the Search Engine Manipulation Effect (SEME) (http://bit.ly/1REqzEY and http://bit.ly/2xY4nB5), the Search Suggestion Effect (SSE), and the Answer Bot Effect (ABE).

In recent years, consumer interest in voice-driven AI assistants such as Alexa, Google Home, and Siri has exploded. Today, such services are available on a billion devices. Due to this trend, consumers have grown accustomed to interacting with AI assistants somewhat as they do with fellow humans.

The Web has been one of the most impactful technologies ever, and over the past twenty years or so, has helped advance society in ways no one thought possible. Ubiquitous connectivity has enabled instant communication with anyone in the world. Social media has helped us strengthen existing relationships, and form new ones. The vast amount of content on the Web has broadened our outlook, and let us learn about things we never even knew existed.

Pairwise learning refers to a learning task which involves a loss function depending on pairs of examples, among which most notable ones include bipartite ranking, metric learning, minimum entropy error and AUC maximization. Online learning algorithms consider one instance each time to update the model sequentially, which make them amenable for streaming data analysis. However, most of such algorithms focused on the pointwise learning problems such as classification and regression.