Table of Contents
The likelihood of accurate technology forecasting can never be known.
Regardless of how "troubled" a project may be, what is learned after crossing the finish line can be a practitioner's overwhelming success.
We're interested in techniques that automatically find fundamental properties and principles that are original and useful.
Machine learning algorithms enable discovery of important "regularities" in large data sets.
Speed and smarts propel new tools for scientific applications.
ILP can be more effective than neural nets for delving into biological function data for pharmaceutical engineers developing new drugs and their multiple varieties.
The intelligence emerging from interactions among numerous self-organizing processing elements can be trained to discover the knowledge embedded in data.
New automated tools hold the promise of allowing users to find more, learn more, and build more opportunities.
Recent industry and market research apps demonstrate the decisive power of rough sets.
Building mining apps in natural language processing poses special challenges, but also offers great rewards.
There are several layers of knowledge discovery in this international project and each one poses big questions to biologists.
A vast database of human experience can be used to direct a search.
The precipitous rise of the middleman.
Moving from traditional interfaces toward interfaces offering users greater expressive power, naturalness, and portability.
Synthesizing an organization's computing infrastructure to support the spectrum of tasks performed by users in a geographically distributed organization.
Before Decision Support Systems can be shared via the Web, a common protocol for deploying them must be agreed upon.
An exercise in abstracting and factoring out failure detection.