Diversity is a concept relevant to numerous domains of research varying from ecology, to information theory, and to economics, to cite a few. It is a notion that is steadily gaining attention in the information retrieval, network analysis, and artificial neural networks communities. While the use of diversity measures in network-structured data counts a growing number of applications, no clear and comprehensive description is available for the different ways in which diversities can be measured. In this article, we develop a formal framework for the application of a large family of diversity measures to heterogeneous information networks (HINs), a flexible, widely-used network data formalism. This extends the application of diversity measures, from systems of classifications and apportionments, to more complex relations that can be better modeled by networks. In doing so, we not only provide an effective organization of multiple practices from different domains, but also unearth new observables in systems modeled by heterogeneous information networks. We illustrate the pertinence of our approach by developing different applications related to various domains concerned by both diversity and networks. In particular, we illustrate the usefulness of these new proposed observables in the domains of recommender systems and social media studies, among other fields.

The future where people can delegate mundane tasks to a machine is not far from happening. From starting the laundry down to cooking dinner after a long day is about to be over. Artificial Intelligence has really helped shape our internet today. After all, we can already communicate with virtual assistants like Apple's Siri and Amazon's Alexa for small things around the house, like calling Uber or ordering a pizza. Things that we only see on sci-fi movies may be closer than you think.

Together with the rise of the Internet, access to large repositories of data has helped machine learning technology grow exponentially. The incredibly quick pace of growth was unprecedented. As a result, it is obvious that AI will make a significant impact on the world in the years to come. However, with the numerous established and emerging fields of AI around today, such a blanket statement doesn't provide much concrete meaning. What fields and applications of AI are receiving the most investment and development?

Google introduced new speech technology for the Assistant. The Google Assistant just picked up some new reading skills. Google on Tuesday announced new technology for its digital helper software that lets it read long-form text out loud. For now, the feature is meant mainly for listening to articles, blog posts and short stories on the web. Google said the technology is different from other screen-reading software because it's meant to read stuff in a natural-sounding voice and cadence, so people won't have trouble listening to the audio for longer periods of time.

The last decade saw a huge change in the way insurance industry functions. As we step into 2020 with AI-driven InsurTech initiatives, the next one promises to be even more of an adventure. Innovation and technology are going to be the forefront, but any predictions about future trends may have a tendency to be short-lived. At the beginning of the previous decade, it would have been impossible to imagine that the consumers would trust their hard-earned to anyone other than a qualified agent, who could deliver a personal touch and inspire confidence in their investment. Today, insurance customers are willing to buy insurance and take financial advice from AI virtual assistants.

Google's I/O 2018 asserts one thing – the next wave of smartphones will run on a generous amount of Artificial Intelligence. Even the recent Mobile World Congress (MWC) also had conversations that were largely revolving around Artificial Intelligence. Major smartphones makers, led by Apple, Google, Samsung, and many others are creating operating systems, mobile apps and even smartphone that have Artificial Intelligence at their core. McKinsey Global Institute estimates that the investments in Artificial Intelligence R&D made by tech giants by Google and Baidu to be in the range of $20 Billion to $30 Billion. In fact, Ai is ranked to be one among the 5 disruptive Technologies that are shaping up our future digital landscape.

Google is at CES this week touting all the different partners it has to bring the personal Assistant to speakers, smart displays, phones and the like. One partner is popping mad - wireless speaker pioneer Sonos. The Santa Barbara, California maker of speakers that can be added to home systems for improved sound without that last-century accessory, speaker wire, filed two complaints against Google Tuesday, and called for an immediate cease-and-desist order. If granted, it would mean Google would have to stop selling the Google and Nest Home speakers, Pixel phones and laptops. That was the cease-and-desist request sought by Sonos in a complaint filed with the International Trade Commission, along with a separate patent violation lawsuit in federal court in California.

This paper builds upon recent work in the declarative design of dialogue agents and proposes an exciting new tool -- D3BA -- Declarative Design for Digital Business Automation, built to optimize business processes using the power of AI planning. The tool provides a powerful framework to build, optimize, and maintain complex business processes and optimize them by composing with services that automate one or more subtasks. We illustrate salient features of this composition technique, compare with other philosophies of composition, and highlight exciting opportunities for research in this emerging field of business process automation.

For many years, achievements and discoveries made by scientists are made aware through research papers published in appropriate journals or conferences. Often, established scientists and especially newbies are caught up in the dilemma of choosing an appropriate conference to get their work through. Every scientific conference and journal is inclined towards a particular field of research and there is a vast multitude of them for any particular field. Choosing an appropriate venue is vital as it helps in reaching out to the right audience and also to further one's chance of getting their paper published. In this work, we address the problem of recommending appropriate conferences to the authors to increase their chances of acceptance. We present three different approaches for the same involving the use of social network of the authors and the content of the paper in the settings of dimensionality reduction and topic modeling. In all these approaches, we apply Correspondence Analysis (CA) to derive appropriate relationships between the entities in question, such as conferences and papers. Our models show promising results when compared with existing methods such as content-based filtering, collaborative filtering and hybrid filtering.

In previous LI articles I've written, I've discussed the intersections and similarities between human learning and machine learning/artificial intelligence (ML/AI). Others have written about the similarities and differences in the learning process itself. After several requests for my insights, I've decided it's time to write about the topic everyone has been asking experts in the learning field recently, namely, how ML/AI facilitates or augments human learning. To quote Elizabeth Barrett Browning, let me count the ways. When laypeople think about ML/AI, many people describe the commonly used recommender systems popularized by Netflix, Amazon and many, many others.