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Abstract
The Open Mind Initiative provides a framework for large-scale collaborative efforts in building components of “intelligent” systems that address common-sense reasoning, document and language understanding, speech and character recognition, and so on. These areas have highly developed and adequate theory; progress is held back by lack of sufficiently large datasets of ‘informal’ knowledge, which can be provided by non-expert netizens. Based on the Open Source methodology, the Open Mind Intitiative allows domain specialists to contribute algorithms, tool developers to provide software infrastructure and tools, and most importantly non-specialist netizens to contribute data to large knowledge bases via the internet. An important challenge is to make it easy and rewarding — for instance by novel game interfaces, financial incentives, and educational interest — for netizens to provide data.
We review free software and open source approaches, including their business and economic models, and past software projects of particular relevance to Open Mind.
Traditional Open Source

texpert knowledge contributed by expert machine learning irrelevant
tmost work is directly on the released software (e.g., Linux device drivers)
thacker and programmer culture (e.g., ~100,000 contributors to Linux)
tResult: software

Open Mind Initiative

tinformal knowledge contributed by programmers
tnon-expert netizens
tmachine learning essential
tmost work is on data acquisition, machine learning, and infrastructure (e.g., collecting labelled patterns)
tnetizen and business culture (e.g., ~100,000,000 netizens on the web)
tResult: software and databases

We describe three Open Mind projects: handwriting recognition, speech recognition and commonsense reasoning, as well as some of the challenges and opportunities for computer-human interactions, particularly novel game interfaces, insuring data integrity and learning from heterogeneous contributors.
[1] “The Open Mind Initiative” by David G. Stork, IEEE Expert systems and their applications pp. 19-20 May/June 1999.
[2] “Character and Document Research in the Open Mind Initiative” by David G. Stork, International Conference on Document Analysis and Recognition (ICDAR99), 1999.
[3] “Open Mind Speech Recognition” by Jean-Marc Valin and David G. Stork, Proceedings of Automatic Speech Recognition and Understanding Workshop (ASRU), Keystone CO, Dec. 1999.

Biography
Dr. David G. Stork is Chief Scientist of Ricoh Silicon Valley as well as Consulting Associate Professor of Electrical Engineering at Stanford University. A graduate of MIT and the University of Maryland, he has been on the faculties of Wellesley College, Swarthmore College, Clark University, Boston University and Stanford University. Dr. Stork sits on the editorial boards of five journals, has published numerous peer-reviewed papers and book chapters and holds over a dozen patents. A distinguished lecturer of the Association for Computing Machinery, he has published five books, including most recently Pattern Classification (2nd ed.) with R. O. Duda, P. E. Hart and D. G. Stork, and HAL’s Legacy: 2001’s computer as dream and reality for popular audiences, the basis of his forthcoming PBS television documentary 2001: HAL’s Legacy. His deepest interests are in pattern recognition by machines and humans.