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William Jarrold is a senior scientist at Nuance Communications. His research in intelligent conversational assistants
draws upon expertise in ontology, knowledge representation and reasoning, natural language understanding, and
statistical natural language processing (NLP). Throughout
his career he has developed computational models to augment and understand human cognition. In prior work at
the University of California, Davis and the SRI Artificial
Intelligence Lab he has applied statistical NLP to the differential diagnosis of neuropsychiatric conditions. At SRI and
the University of Texas he developed ontologies for intelligent tutoring (HALO) and cognitive assistants (CALO). Early in his career he worked at MCC and Cycorp developing
ontologies to support commonsense reasoning in Cyc — a
large general-purpose knowledge-based system. His Ph.D. is
from the University of Texas at Austin and his BS is from the
Massachusetts Institute of Technology.
Peter Z. Yeh is a senior principal research scientist at
Nuance Communications. His research interests lie at the
intersection of semantic technologies, data and web mining, and natural language understanding. Prior to joining
Nuance, Yeh was a research lead at Accenture Technology
Labs where he was responsible for investigating and applying AI technologies to various enterprise problems ranging
from data management to advanced analytics. Yeh is currently working on enhancing interpretation intelligence
within intelligent virtual assistants and automatically constructing large-scale knowledge repositories necessary to
support such interpretations. He received his Ph.D. in computer science from The University of Texas at Austin.
AI in Industry Columnists Wanted!
AI Magazine is soliciting contributions for a column on AI in industry. Contributions should inform AI Magazine’s readers about the
kind of AI technology that has been created or used in the company, what kinds of problems are addressed by the technology,
and what lessons have been learned from its deployment (
including successes and failures). Prospective columns should allow
readers to understand what the current AI technology is and is not
able to do for the commercial sector and what the industry cares
about. We are looking for honest assessments (ideally tied carefully to the current state of the art in AI research) — not product
ads. Articles simply describing commercially available products are
not suitable for the column, although descriptions of interesting,
innovative, or high impact uses of commercial products may be.
Questions should be discussed with the column editors.
Columns should contain a title, names of authors, affiliations
and email addresses (and a designation of one author as contact
author), a 2–3 sentence abstract, and a brief bibliography (if
appropriate). The main text should be brief (600– 1,000 words)
and provide the reader with high-level information about how AI
is used in their companies (we understand the need to protect
proprietary information), trends in AI use there, as well as an
assessment of the contribution. Larger companies might want to
focus on one or two suitable projects so that the description of
their development or use of AI technology can be made sufficiently detailed. The column should be written for a knowledgeable audience of Al researchers and practitioners.
Reports go through an internal review process (acceptance is
not guaranteed). The column editors and the AI Magazine
editor-in-chief are the sole reviewers of summaries. All articles will be
copyedited, and authors will be required to transfer copyright of
their columns to AAAI.
If you are interested in submitting an article to the AI in Industry column, please contact column editors Sven Koenig
( skoenig@usc.edu) and Sandip Sen ( sandip-sen@utulsa.edu)
before submission.
