Visual Lexicons: The Quest for Data - Driven Decision Making

author: Charles H. House, Intel Corporation
published: Feb. 25, 2007,   recorded: June 2005,   views: 5018


Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.


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.

See Also:

Download slides icon Download slides: mlmi04uk_house_qdddm_01.ppt (9.7┬áMB)

Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: