This document discusses the concepts and practices of immersive intelligence. It defines immersive intelligence as a collaborative data-driven decision process for understanding and managing complex systems using immersive virtual spaces. The document outlines workshop objectives such as sharing a vision of how virtual spaces, objects, and teams can be used to examine problems involving big data, complex systems, and hard problems. It provides examples of places to visit and things to learn in virtual worlds that demonstrate immersive intelligence approaches. The document stresses that immersion is key and discusses next steps like sponsoring contests and proof-of-concept projects to help develop skills and assess use cases.

1. Concepts and Practices of Immersive Intelligence Dr. Richard Hackathorn Bolder Technology, Inc. Federal Consortium for Virtual Worlds 2010

2. ThinkBalm Innovation Community Erica and Sam Driver, http://www.thinkbalm.com/ http://www.linkedin.com/groups?gid=2005729 Immersive Intelligence Colleagues Meet-and-Greet event in Aug 2009 Website http://IM-TEL.org/ Open community under Creative Commons Register here! Join us for coffee on Tuesdays NOTE: Slides at http://IM-TEL.org/ under Blog How we got started…

3. Immersive Intelligence: What and Why? Elevator pitch in 30 seconds! Realistic business-oriented assessment Emerging technology; unproven use case When and how . . . for what expected benefit Motivated to jump in-world and contribute! Workshop Objectives

4. Why? So, What? Sharing A Vision Places to Visit & Things to Learn Putting It Together What’s Next? Overview

5. Not your grandmother’s pie chart! What are we trying to accomplish? Hard Problems Complex Systems Big Data A. Why? So, What?

6. Not your grandma’s pie chart! © Getty Images. Used with Permission

7. What Problems Are We Solving? We Are Here!

8. No quick answers No ‘right’ answers Conflicting priorities Many valid opinions and perspectives Leveraging the Wisdom of the Crowd Managed Muddling What are Hard Problems?

9. Non-intuitive behavior Often full of surprises Tipping points When you least expect them Feedback Loops Exponential Behavior Will hit the wall… with certainty Small World effects Phase transitions from one state What are Complex Systems?

10. History of Complex Systems Chaos Theory Systems Dynamics Science Of Networks Complexity Science Cybernetics Artificial Intelligence http://en.wikipedia.org/wiki/Complex_systems

11. Stable System Chaotic System Will Wright at GameTech 2010 http://www.slideshare.net/guesta79ddf9/orando2010-5 Complex Systems are Chaotic

12. Clocks versus Clouds regular, orderly, and predictable highly irregular, disorderly, and more or less unpredictable Karl Popper http://en.wikipedia.org/wiki/Karl_Popper

13. Focusing on technology (MPP, MapReduce, Hadoop) What is Big Data? The Economist, Feb 25, 2010

14. Volume – Yes, but… More than we can store! Can we ever touch each bit? Velocity Data in motion  new types of analysis What is important to capture? Context? Variety Many types of data! …like network graphs Analytics are a necessity! Can not touch all the atomic level data! What is Big Data? The Economist, Feb 25, 2010

15. Imagine virtual space Imagine virtual objects Imagine virtual teams Imagine scaling size Imagine scaling time Imagine scaling probability B. Sharing A Vision

16. Imagine Virtual Space Vast canvas spanning kilometers Persistent and always on

17. …with rich features, such as texture, lightning, shadow, shininess, etc Imagine Virtual Objects

18. Sharing experiences & common perspectives Understanding the root cause of problems Generating & prioritizing alternative solutions Executing on action plans Imagine Virtual Teams

19. From the very small to the very large Imagine Scaling Size

20. From the very fast to the very slow Imagine Scaling Time

21. From the certain to the highly improbable (and unimaginable) Imagine Scaling Probability

22. Immersive Intelligence Park on Second Earth 6 Chakryn Forest by Bettina Tizzy and others beautiful and complex => data forest Real-time weather display on NOAA Meteora Data feed from NWS, updated every 5 min DadenDatascape by David Burden Ben Lindquist of Green Phosphor Tools to graph data in several virtual worlds Data Sculptures by Ann Cudworthat Beach Ride C. Places To Visit & Things To Learn

23. Death and Taxes by Eric Hackathorn Chromutate by Opensource Obscure Fanciful objects with playful behaviors Mars Victoria Crater on NASA Explorer Island Largest sculpted build Wind Simulator by comet Morigi Creative use of particles Asteroid strike on Mars by the Exploratorium Large-scale complex simulation; getting the physics correct Svarga ecosystem as the “God Game” http://nwn.blogs.com/nwn/2006/05/god_game.html C. Places To Visit & Things To Learn

24. NOAA Real-Time Weather

25. Exciting and insightful way of visualizing and analyzing data Using GreenPhosphor’sGlassHouse Gateway Richness of Expression More than 3D Ben Lindquist of Green Phosphor

26. Hovering between Art and Science The Spirit of the Data …listening and being one with the data ART + TOI  Engagement Ann Cudworth

27. Immersive Intelligence is an Approach Levels of Immersive Intelligence Immersion is the Secret Sauce Architecture D. Putting It Together

28. It is an Approach Not a tool, not a methodology, Collaborative data-driven decision process for understanding and managing complex systems using immersive virtual spaces Subject to continual change!  Actionable information What do you do with what you know? plus the means to execute Immersive Intelligence http://www.flickr.com/photos/arandalasch/3193076760/

29. Levels of Immersive Intelligence R. Hackathorn, Serious Games in Virtual Worlds, 2007 http://www.BeyeNETWORK.com/channels/1217/view/4163

30. Immersion is the Secret Sauce You are there … surrounded by others Feels as an actor inside, rather than observer outside Analogy: Snorkeling versus SCUBA

31. Architecture - A Abstraction of Complex System In Virtual World Complex System In Physical World Databases Describing The System

32. Architecture - B World Designer Configure Shape Definer Data Pump Databases Describing The System Abstraction of Complex System In Virtual World

33. DW Appliances as VW Backends IBM Smart Analytics System Teradata DW Appliance Netezza Twin-Fin Sun Oracle Database Machine

34. Need for an open “community of practice” By sharing information and experiences, learn from each other and develop the skills needed Methodically pool knowledge…by wiki-izing Need a diverse array of skills and experiences Too many engineer-types; need more artists! Foster learning and innovation Provide a low barrier to that “Hello, World” experience Facilitate that “A-ha… I get it!” experience Got a start with the IM-TEL community Contribute, share, and leverage…at IM-TEL.org Share alike with Creative Commons with Attribution E. What’s Next?

35. Sponsor contests within SL community Focus on data visualization How best to illustrate clustering or decision trees? Engage in Proof-of-Concept projects to… Surface the issues and roadblocks Demonstrate the benefits Offering 2-day custom workshop Bring your team to common ground Assess the business justification for specific use case E. What’s Next?

36. Immersive Intelligence: What and Why? Elevator pitch in 30 seconds! Realistic business-oriented assessment Emerging technology; unproven use case When and how . . . for what expected benefit Motivated to jump in-world and contribute! Workshop Objectives-Revisited