2nd Joint Workshop on Multimodal Interaction and Related Machine Learning Algorithms

Visual Lexicons: The Quest for Data - Driven Decision Making

author: Charles H. House, Intel Corporation

Description

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, not by thinking, but by exhaustively examining all permutations and combinations in blinding time against a predetermined outcome set of rules. The questions for mankind, though, seem of the form where rules are imprecise at best, and essentially unknowable perhaps. If learnable and knowable even, many other constraints exist that mitigate against "data-driven decision-making". This presentation assesses some of these constraints, and offers some perspective on the value of using visual dynamics and analytics to help overcome such issues.

Categories

Top: Society
Top: Computer Science: Machine Learning

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*Slides*
0:01	Visual Lexicons: The Quest for Data- Driven Decision Making
0:39	Visual Lexicons are one topic – Effective Communication is the goal
2:36	“Communications” means many things
3:30	Enhanced Communications
4:18	This research requires a multi-disciplinary approach
6:10	How do we learn about “the future”? ‘99
7:53	Structured meetings at Intel
10:20	Meeting Effectiveness presumes . . .
12:33	Communities in Centralized vs Distributed Development
13:58	Purpose of collaboration
16:03	Many of you might know the Edward Tufte books Envisioning Information Horn’s work is far more useful for most business applications
16:19	Many, nay MOST, employees cite DATA OVERLOAD as their 1st or 2nd biggest problem
16:31	Wicked, ill-structured Problems Abound for Teams
17:15	A TRULY EFFECTIVE Colab capability will deal strongly with this class of issues
17:58	Intel Research Collaboratory
18:57	Virtuality Index: What we found
20:20	A “Big Thought” Problem Statement
21:20	Better than “Being There”?
23:15	Factors in Distance Learning
27:46	What does the Stanford example tell us?
29:17	The HP Halo Collaboration Studio
30:07	Video Presence
31:44	Now, imagine one staff “team” in a meeting
32:23	What happens for the remote attendee?
33:01	CHANGING THE DYNAMICS OF A NASDAQ 100 COMPANY STAFF: with WebeX, Full Duplex Confer’c’g Phones
34:19	PITAC1 Vision -- IT Transforming our Society
34:31	What are the important Research issues? 1
34:56	PITAC “got it right” mostly, except . . .
35:19	“What might be done?”
36:10	Three simple wishes
36:28	CS and Social Science
37:41	When DID scientists build anything?
37:48	Best “product of the year”
38:12	You’re a geographer/sociologist, really a 20th century urbanologist
38:58	Armed with some graphing skills, though, you show them some PowerPoint / Excel graphs of 50 years at America’s largest cities
39:59	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 1950
40:18	And THEN, you overlay a multi-variate dynamic graph (which this really isn’t . . . ) of the population of the same seventeen cities in 2000
40:40	We communicate lots of things We collaborate about HARD PROBLEMS
40:45	Computer Generated Graphic images
41:01	Here’s another Graphic image
41:08	All types of cancer; white females; age-adjusted rate by county, 1950-1969
41:32	All types of cancer; white females; age-adjusted rate by county, 1950-1969
41:53	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 it
42:14	Application Landscape: Bioinformatics
43:36	Video is often a “turn-off”
43:40	If a Picture = 1000 words, what’s a video stream worth?
44:10	The point?
44:33	In conclusion

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