Graph-structured data arise naturally in many different application domains. By representing data as graphs, we can capture entities (i.e., nodes) as well as their relationships (i.e., edges) with each other. Many useful insights can be derived from graph-structured data as demonstrated by an ever-growing body of work focused on graph mining. However, in the real-world, graphs can be both large - with many complex patterns - and noisy which can pose a problem for effective graph mining. An effective way to deal with this issue is to incorporate "attention" into graph mining solutions. An attention mechanism allows a method to focus on task-relevant parts of the graph, helping it to make better decisions. In this work, we conduct a comprehensive and focused survey of the literature on the emerging field of graph attention models. We introduce three intuitive taxonomies to group existing work. These are based on problem setting (type of input and output), the type of attention mechanism used, and the task (e.g., graph classification, link prediction, etc.). We motivate our taxonomies through detailed examples and use each to survey competing approaches from a unique standpoint. Finally, we highlight several challenges in the area and discuss promising directions for future work.

Robotic systems, working together as a team, are becoming valuable players in different real-world applications, from disaster response to warehouse fulfillment services. Centralized solutions for coordinating multi-robot teams often suffer from poor scalability and vulnerability to communication disruptions. This paper develops a decentralized multi-agent task allocation (Dec-MATA) algorithm for multi-robot applications. The task planning problem is posed as a maximum-weighted matching of a bipartite graph, the solution of which using the blossom algorithm allows each robot to autonomously identify the optimal sequence of tasks it should undertake. The graph weights are determined based on a soft clustering process, which also plays a problem decomposition role seeking to reduce the complexity of the individual-agents' task assignment problems. To evaluate the new Dec-MATA algorithm, a series of case studies (of varying complexity) are performed, with tasks being distributed randomly over an observable 2D environment. A centralized approach, based on a state-of-the-art MILP formulation of the multi-Traveling Salesman problem is used for comparative analysis. While getting within 7-28% of the optimal cost obtained by the centralized algorithm, the Dec-MATA algorithm is found to be 1-3 orders of magnitude faster and minimally sensitive to task-to-robot ratios, unlike the centralized algorithm.

Designing hierarchical reinforcement learning algorithms that induce a notion of safety is not only vital for safety-critical applications, but also, brings better understanding of an artificially intelligent agent's decisions. While learning end-to-end options automatically has been fully realized recently, we propose a solution to learning safe options. We introduce the idea of controllability of states based on the temporal difference errors in the option-critic framework. We then derive the policy-gradient theorem with controllability and propose a novel framework called safe option-critic. We demonstrate the effectiveness of our approach in the four-rooms grid-world, cartpole, and three games in the Arcade Learning Environment (ALE): MsPacman, Amidar and Q*Bert. Learning of end-to-end options with the proposed notion of safety achieves reduction in the variance of return and boosts the performance in environments with intrinsic variability in the reward structure. More importantly, the proposed algorithm outperforms the vanilla options in all the environments and primitive actions in two out of three ALE games.

Nicholas will give an overview of H2O, the leading open source machine learning platform for the enterprise, which integrates seamlessly with R and Python environments, as well as, Driverless AI, an enterprise automated machine learning solution. Nicholas will also be talking about some of the integration points that H2O.ai has built with Google, including: Google Cloud Engine, Kubeflow, and more. Tentative Agenda: 6:00 - 6:30 PM: Doors open and Pizza 6:30 - 7:15PM: Who is H2O.ai Introduction H2O-3 Sparkling Water Driverless AI H2O Google GCP Integrations Kubeflow 7:15 - 7:45 PM: Questions/Networking Speaker's Bio: Nicholas Png is a Partnerships Software Engineer at H2O.ai. Prior to working at H2O, he worked as a Quality Assurance Software Engineer, developing software automation testing.

The use of AI at work has grown tremendously. According to Deloitte's 2018 Global Human Capital Trends survey, 42 percent companies believe that AI will be widely deployed in their organizations within three to five years. In the world of HR technology, AI is reshaping employee experience, enabling the selection of the right candidates, enhancing employee productivity, easing and simplifying tedious HR processes. There are two kinds of application in the HR context: 1) Conversational analytics using tools like Chatbots and 2) Machine learning using pattern analysis on data. While there are a number of applications that AI-based technology helps support, there are other emerging areas that this technology can help with. A few areas include: Talent Retention, a key concern for HR teams can benefit from building predictability.

The growing importance of digital demand generation channels in the B2B marketing mix was reflected by the results of DemandGen's 2018 Benchmark Survey Report. Out of the ten channels most effective in driving "early-stage engagement" and "driving conversions later in the funnel," eight were digital. These findings aren't astonishing to anyone engaged in B2B marketing. While analog channels, like in-person events, are still effective ways to drive demand, the shift to digital has been underway for more than a decade. As technology continues to be developed and embraced at increasing speeds, demand marketers are rushing to keep up.

The benefits of autonomous vehicles (AVs) are widely acknowledged, but there are concerns about the extent of these benefits and AV risks and unintended consequences. In this article, we first examine AVs and different categories of the technological risks associated with them. We then explore strategies that can be adopted to address these risks, and explore emerging responses by governments for addressing AV risks. Our analyses reveal that, thus far, governments have in most instances avoided stringent measures in order to promote AV developments and the majority of responses are non-binding and focus on creating councils or working groups to better explore AV implications. The US has been active in introducing legislations to address issues related to privacy and cybersecurity. The UK and Germany, in particular, have enacted laws to address liability issues; other countries mostly acknowledge these issues, but have yet to implement specific strategies. To address privacy and cybersecurity risks strategies ranging from introduction or amendment of non-AV specific legislation to creating working groups have been adopted. Much less attention has been paid to issues such as environmental and employment risks, although a few governments have begun programmes to retrain workers who might be negatively affected.

With an estimated market size of 7.35 billion US dollars, artificial intelligence is growing by leaps and bounds. McKinsey predicts that AI techniques (including deep learning and reinforcement learning) have the potential to create between $3.5T and $5.8T in value annually across nine business functions in 19 industries. Although machine learning is seen as a monolith, this cutting-edge technology is diversified, with various sub-types including machine learning, deep learning, and the state-of-art technology of deep reinforcement learning. Reinforcement learning is the training of machine learning models to make a sequence of decisions. The agent learns to achieve a goal in an uncertain, potentially complex environment.

In the past few years, the area of Machine Learning (ML) has witnessed tremendous advancements, becoming a pervasive technology in a wide range of applications. One area that can significantly benefit from the use of ML is Combinatorial Optimization. The three pillars of constraint satisfaction and optimization problem solving, i.e., modeling, search, and optimization, can exploit ML techniques to boost their accuracy, efficiency and effectiveness. In this survey we focus on the modeling component, whose effectiveness is crucial for solving the problem. The modeling activity has been traditionally shaped by optimization and domain experts, interacting to provide realistic results. Machine Learning techniques can tremendously ease the process, and exploit the available data to either create models or refine expert-designed ones. In this survey we cover approaches that have been recently proposed to enhance the modeling process by learning either single constraints, objective functions, or the whole model. We highlight common themes to multiple approaches and draw connections with related fields of research.

Willis Towers Watson, a leading global advisory, broking and solutions company, and Relativity6, Inc., a machine learning and artificial intelligence (AI) insurance-technology company, today announced that Willis Towers Watson has selected the Relativity6 platform to predict and optimise customer retention and win-back. Brent Lehmann, General Manager Affinity & Commercial Australasia said the partnership with such an innovative technology company will help to ensure Willis Towers Watson remains competitive in the marketplace. "Relativity6's product offerings are a good fit to accomplish our strategic objectives across the organisation, so we are very excited to partner with them to take full advantage of the data that we have accumulated within our core systems in Australia." Alan Ringvald, Chief Executive Officer at Relativity 6, commented: "We are honoured to partner with such a distinguished organization. We believe that our solution will enable Willis Towers Watson to better serve their customers and ultimately drive significant top line revenue growth. We've engaged with top-tier insurers in the U.S. and Latin America, and this is a fantastic opportunity to expand our footprint with a truly global insurance broking brand."