The CTO of Momentive, formerly SurveyMonkey, discusses the importance of machine learning and AI as the world of data continues to grow. SurveyMonkey has been one of the big names associated with online surveys since its inception more than 20 years ago. The company first came to Dublin in 2014 and subsequently added a European data centre to its Irish footprint in 2019. But while the well-known name remains attached to the surveys product, the company behind the product rebranded as Momentive earlier this year. Robin Ducot joined in 2017 as chief technology officer and leads engineering and infrastructure at the company from her base in San Francisco.

Thinking of home as the new workplace has put most HR teams in a driver's seat when it comes to controlling and coordinating various aspects of business operations. From creating space online for smooth employee communications to managing challenges related to talent management (hiring, onboarding and attrition / RIFs), HR teams are using a mix of HR technology tools and platforms to effectively manage the organizational goals at the people's level. Like their peers in the Marketing, Sales, IT and Finance, HR teams too have a solid reason to leverage AI, Machine Learning, Automation and Data Science to upgrade their people analysis and enhance vision of how their various policies affect human capital management. With demands for personalization and DEI at an all-time high, HR teams have a solid reason to sought powerful analytics tools to create a purposeful workforce in the'new-normal' era of business operations. That's why we are focusing on the current definition of HR Analytics and the top HR analytics software makers available today for enterprise consumption.

Not too long ago, in fact at the beginning of present decade, Communication Service Providers (CSPs) were still talking about setting up new Network Operation Centers (NOCs) to monitor customer experience! In the subsequent years, the network centric approach turned into service centric approach and NOCs were replaced by Service Operation Centers (SOC). These centers provide insights into services used by the customer, for example video quality, web access etc. Aided by big data processing, these terabytes of network data proved to be a goldmine for CSPs. Over recent years, they have provided great analytics insights into customer behavior and their experience in real time. Today, as 5G deployments gain traction and customers' appetite for ever increasing speeds grow, it has become both a challenge and opportunity for CSPs to measure and continuously improve the customer experience.

Experience Management studies AI systems that automatically adapt interactive experiences such as games to tailor to specific players and to fulfill design goals. Although it has been explored for several decades, existing work in experience management has mostly focused on single-player experiences. This paper is a first attempt at identifying the main challenges to expand EM to multi-player/multi-user games or experiences. We also make connections to related areas where solutions for similar problems have been proposed (especially group recommender systems) and discusses the potential impact and applications of multi-player EM.

Intelligent interactive narrative systems often use an experience manager to govern the behavior of non-player charactersin a way that guides the story towards its author’s agenda, which may be for entertainment, education, training, or other purposes. For such systems, a central challenge is creating believable virtual characters. The Belief Desire Intention framework is often cited as a goal for researchers in this field; for characters to seem realistic, a human audience should attribute beliefs, desires, and intentions to them. Much of my prior work has focused on belief; my goal for the future is to finish the work on belief, and to implement a new model of desire and intention that explicitly reasons about characters’ commitment to certain plans of action.

We present a new way to represent and understand experience managers — AI agents that tune the parameters of a running game to pursue a designer's goal. Existing representations of AI managers are diverse, which complicates the task of drawing useful comparisons between them. Contrary to previous representations, ours uses a point of unity as its basis: that every game/manager pair can be viewed as only a game with the manager embedded inside. From this basis, we show that several common, differently-represented concepts of experience management can be re-expressed in a unified way. We demonstrate our new representation concretely by comparing two different representations, Search-Based Drama Management and Generalized Experience Management, and we present the insights that we have gained from this effort.

Let's highlight the transition from WCM platforms to digital experience management with a hypothetical example that Acquia used in a series of presentations at its October Engage user conference in Boston. In the desktop days, a cook planning a dinner party would have to gather recipes, create a grocery list and write it all down on paper. In a digital experience management implementation, actionable data -- perhaps even fished from a data lake using artificial intelligence -- would find recipes based on a voice request, generate a shopping list from the recipes and store it on a mobile app. Automatically, the list arranges itself, so each ingredient appears in order of placement in the store, because it's hashed against the store's floor plan. Finding even the most specialized products is simple -- intelligence baked into the app combines with in-store Wi-Fi beacons to pinpoint the right shelves.

Drama Managers, a specific type of the more general Experience Manager, have become a common subject of study in the interactive narrative literature. With a range of representational and computational approaches, authors have repeatedly developed techniques that enable computers to generate, reason about, and adapt narratives in an interactive virtual setting. In order to fully realize an experience manager, seven representational and computational problems need to be solved, generally on a case-by-case basis. In other words, the choice to use an Experience Manager is the choice to model the design as, and implement solutions to, seven inter-dependent design problems. We explicitly articulate those design problems and provide a number of examples of methods that both motivate the design problems as well as illustrate a range of approaches to solving them.