‘Elementary Dear Watson’

Exploring and identifying areas and tools that can be used to develop the measurement interfaces for transformational experiences requires a process of both experimentation and comprehensive testing. The use of deep data show hubs or wearables that contain platforms that allow attendees to develop stories and experience narrative as active social multimedia data gathering; or analysis platforms that identify tendencies, advancements and best practices for show development are complex. We study the implementation of socially aware systems as well as a machine learning back-end that provides a short analysis of multiple format postings or real-time, in-situ measurement. Such hubs include elements of gamification in so much that points are awarded for relevant postings against directed criteria. These points can be translated into a variety of attendee benefits. Such approaches require the advent of a fresh set of metrics to make the data more relevant and provide greater attendee personalization as payback.

The hubs can be something like integrated, immersive displays such as the Hanging Garden prop that measures attendees’ affective responses to their environment using artificial intelligence agents to enter into multimodal conversation. Think next stage IBM’s Watson. Alternatively, they can be GPS-based, interactive wearable AI that measure the emotions of a conversation between stakeholders or augmented or hyper-reality overlays on product displays in order to gauge an attendee’s level in interest.

Maybe this is not currently so elementary, but this is the type of thinking that drives our new approaches to research criteria and measurement. Machine learning algorithms and datasets can be extended to understand how an attendee is moving or interacting with others, that can give clues about mood, physical reaction, energy level and even context.