Visual Lexicons: The Quest for Data - Driven Decision Making

Published on 2007-02-255034 Views

Charles H. House

The eternal AI quest - can machines think as well as man? - seems quaint today compared to the question of how can machines help man to think. True, Deep Blue can beat the world's best chess player, n

Related categories

Presentation

Visual Lexicons: The Quest for Data- Driven Decision Making00:01

Visual Lexicons are one topic – Effective Communication is the goal00:39

“Communications” means many things02:36

Enhanced Communications03:30

This research requires a multi-disciplinary approach04:18

How do we learn about “the future”? ‘9906:10

Structured meetings at Intel07:53

Meeting Effectiveness presumes . . . 10:20

Communities in Centralized vs Distributed Development12:33

Purpose of collaboration13:58

Many of you might know the Edward Tufte books Envisioning Information Horn’s work is far more useful for most business applications16:03

Many, nay MOST, employees cite DATA OVERLOAD as their 1st or 2nd biggest problem16:19

Wicked, ill-structured Problems Abound for Teams16:31

A TRULY EFFECTIVE Colab capability will deal strongly with this class of issues17:15

Intel Research Collaboratory17:58

Virtuality Index: What we found18:57

A “Big Thought” Problem Statement20:20

Better than “Being There”?21:20

Factors in Distance Learning23:15

What does the Stanford example tell us?27:46

The HP Halo Collaboration Studio29:17

Video Presence30:07

Now, imagine one staff “team” in a meeting31:44

What happens for the remote attendee?32:23

CHANGING THE DYNAMICS OF A NASDAQ 100 COMPANY STAFF: with WebeX, Full Duplex Confer’c’g Phones33:01

PITAC1 Vision -- IT Transforming our Society34:19

What are the important Research issues? 134:31

PITAC “got it right” mostly, except . . . 34:56

“What might be done?”35:19

Three simple wishes36:10

CS and Social Science36:28

When DID scientists build anything?37:41

Best “product of the year”37:48

You’re a geographer/sociologist, really a 20th century urbanologist38:12

Armed with some graphing skills, though, you show them some PowerPoint / Excel graphs of 50 years at America’s largest cities38:58

And THEN, armed with a “cool” 4-D plotting package, you show them a multi-variate dynamic graph (which this really isn’t . . . ) of the population of seventeen cities in 195039:59

And THEN, you overlay a multi-variate dynamic graph (which this really isn’t . . . ) of the population of the same seventeen cities in 200040:18

We communicate lots of things We collaborate about HARD PROBLEMS40:40

Computer Generated Graphic images40:45

Here’s another Graphic image41:01

All types of cancer; white females; age-adjusted rate by county, 1950-196941:08

This data had been compiled for multiple diseases With multi-variate discrimination ( > 17 variables) And it has taken AMA 25 years to BEGIN to learn how to use it41:53

Application Landscape: Bioinformatics42:14

Video is often a “turn-off”43:36

If a Picture = 1000 words, what’s a video stream worth?43:40

The point?44:10

In conclusion44:33