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.

The software has in recent months become one of AI researchers' flashiest and most viral breakthroughs, vastly reducing the time and effort it takes for artists and researchers to create dreamy landscapes and fictional people. A seemingly infinite stream of fakes can be seen at thispersondoesnotexist.com, as well as a companion AI system trained on images of cats, called thiscatdoesnotexist.com. To test whether people can tell the difference between a generated fake and the real thing, AI researchers at the University of Washington also built the side-by-side website whichfaceisreal.com.

AI has been talked about since the very early days of computing and has attained mainstream use in recent years with the likes of Amazon's Alexa and Apple's Siri. "Just as in the last 40 years, computation has enabled us to change the way we do business and create new products, AI will help us to make better decisions," Carlos Kuchovsky, chief of technology and R&D at BBVA, tells Finextra. "We are now looking at the ways in which it can help us change the way we operate and bring value." The Bank of England has recently reported that machine learning tools are in use at two thirds of UK financial firms, with the average company using it two business areas, which is expected to double in the next three years. It may be through interoperation with cloud and blockchain technology that AI's capabilities will be fully harnessed. AI Utilisation of machine learning and artificial intelligence has become commonplace in everyday life, whether it be in search engines, music streaming services or internet shopping.

Samsung have unveiled a tiny robot assistant in the shape of a ball, which can roll around and help patrol a users home - and even act as a fitness buddy. The tech giant unveiled'Ballie' during one of two keynotes at the Consumer Electronics Show in Las Vegas. Samsung consumer electronics CEO H.S. Kim demonstrated how the ball-shaped bot is able to follow its owner around, traveling closely but also recognizing personal space and speed. Samsung's'Ballie' was revealed during one of two keynotes at the Consumer Electronics Show in Las Vegas Samsung consumer electronics CEO H.S. Kim demonstrated how the ball-shaped bot works When Kim stepped forward, Ballie reacted by wheeling itself further back; when Kim began to increase his pace, Ballie sped up. 'I think he likes me,' Kim said turning to the crowd.

Existing Recommender Systems mainly focus on exploiting users' feedback, e.g., ratings, and reviews on common items to detect similar users. Thus, they might fail when there are no common items of interest among users. We call this problem the Data Sparsity With no Feedback on Common Items (DSW-n-FCI). Personality-based recommender systems have shown a great success to identify similar users based on their personality types. However, there are only a few personality-based recommender systems in the literature which either discover personality explicitly through filling a questionnaire that is a tedious task, or neglect the impact of users' personal interests and level of knowledge, as a key factor to increase recommendations' acceptance. Differently, we identifying users' personality type implicitly with no burden on users and incorporate it along with users' personal interests and their level of knowledge. Experimental results on a real-world dataset demonstrate the effectiveness of our model, especially in DSW-n-FCI situations.

Intelligent Process Automation (IPA) is an emerging technology with a primary goal to assist the knowledge worker by taking care of repetitive, routine and low-cognitive tasks. Conversational agents that can interact with users in a natural language are potential application for IPA systems. Such intelligent agents can assist the user by answering specific questions and executing routine tasks that are ordinarily performed in a natural language (i.e., customer support). In this work, we tackle a challenge of implementing an IPA conversational assistant in a real-world industrial setting with a lack of structured training data. Our proposed system brings two significant benefits: First, it reduces repetitive and time-consuming activities and, therefore, allows workers to focus on more intelligent processes. Second, by interacting with users, it augments the resources with structured and to some extent labeled training data. We showcase the usage of the latter by re-implementing several components of our system with Transfer Learning (TL) methods.