@InProceedings{waltz:database,
  author = {David L. Waltz},
  title = {Innovative Massively Parallel {AI} Applications},
  booktitle = {Proceedings of the 1993 DAGS/PC Symposium},
  year = {1993},
  month = {June},
  pages = {132--138},
  organization = {Dartmouth Institute for Advanced Graduate Studies},
  address = {Hanover, NH},
  keywords = {database, AI, artificial intelligence, pario-bib},
  abstract = {Massively parallel applications must address problems that will
  be too large for workstations for the next several years, or else it will not
  make sense to expend development costs on them. Suitable applications include
  one or more of the following properties: 1) large amounts of data; 2)
  intensive computations; 3) requirement for very fast response times; 4) ways
  to trade computations for human effort, as in developing applications using
  learning methods. Most of the suitable applications that we have found come
  from the general area of very large databases. Massively parallel machines
  have proved to be important not only in being able to run large applications,
  but in accelerating development (allowing the use of simpler algorithms,
  cutting the time to test performance on realistic databases) and allowing
  many different algorithms and parameter settings to be tried and compared for
  a particular task. This paper summarizes four such applications. \par The
  applications described are: 1) prediction of credit card "defaulters"
  (non-payers) and "attritters" (people who didn't renew their cards) from a
  credit card database; 2) prediction of the continuation of time series, e.g.
  stock price movements; 3) automatic keyword assignment for news articles; and
  4) protein secondary structure prediction. These add to a list identified in
  an earlier paper [Waltz 90] including: 5) automatic classification of U.S.
  Census Bureau long forms, using MBR -- Memory-Based Reasoning [Creecy et al
  92, Waltz 89, Stanfill \& Waltz 86]; 6) generating catalogs for a mail order
  company that maximize expected net returns (revenues from orders minus cost
  of the catalogs and mailings) using genetically-inspired methods; and 7)
  text-based intelligent systems for information retrieval, decision support,
  etc.},
  comment = {Invited speaker.}
}