Over the last eight years, four workshops on machine learning have been held. Participation in these workshops was by invitation only. In response to the rapid growth in the number of researchers active in machine learning, it was decided that the fifth meeting should be a conference with open attendance and full review for presented papers. Thus, the first open conference on machine learning took place 12 to 14 June 1988 at The University of Michigan at Ann Arbor. Of the 150 papers submitted, 49 were accepted for publication in the conference proceedings (available from Morgan Kaufmann).
IBM Palo Alto Scientific Center, 2530 Page Mill Road, Palo Alto, CA 94303 Abstract A pattern recognition algorithm is described that learns a transition net grammar from positive examples. Two sets of examples-one in English and one in Chinese-are presented. It is hoped that language learning will reduce the knowledge acquisition effort for expert systems and make the natural language interface to database systems more transportable. The algorithm presented makes a step in that direction by providing a robust parser and reducing special interaction for introduction of new words and terms. We are developing a natural language interface to an expert system for message processing.
If there aren't enough examples of a particular accent or vernacular, then these systems may simply fail to understand you (see "AI's Language Problem"). "If you analyze Twitter for people's opinions on a politician and you're not even considering what African-Americans are saying or young adults are saying, that seems problematic," O'Connor says. Solon Barocas, an assistant professor at Cornell and a cofounder of the event, says the field is growing, with more and more researchers exploring the issue of bias in AI systems. Shared Goel, an assistant professor at Stanford University who studies algorithmic fairness and public policy, says the issue is not always straightforward.
All too often people make snap judgments based on how you speak. Some AI systems are also learning to be prejudiced against some dialects. And as language-based AI systems become ever more common, some minorities may automatically be discriminated against by machines, warn researchers studying the issue. Anyone with a strong or unusual accent may know what it's like to have trouble being understood by Siri or Alexa. This is because voice-recognition systems use natural-language technology to parse the contents of speech, and it often relies on algorithms that have been trained with example data.