William Jarrold

William Jarrold
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Main area of research
Natural language & AI

William Jarrold is a Senior Scientist in the Conversational Prototype group. His research interest is in the area of Natural Language & AI.

At Nuance, Dr. Jarrold's research in conversational systems focuses on (1) ontology for reasoning and data integration (2) named entity extraction and (3) relation extraction. He approaches the development of intelligent conversational agents by working at the intersection of artificial intelligence and human psychology. He received his BS in Brain and Cognitive Sciences from MIT. Subsequently, he developed general-purpose common-sense reasoning ontologies at MCC and Cycorp for 10 years. His PhD was obtained from the University of Texas at Austin. Next, he worked as an ontologist on the DARPA-funded CALO project - a precursor to Apple's SIRI - first at UT's Computer Science Department and later at the SRI Artificial Intelligence Lab in Menlo Park, CA. At SRI he also led efforts in applying machine learning, natural language, and speech processing to assist with the diagnosis of neurological/psychiatric disorders. This worked continued at University of California at Davis where he was co-PI on two projects. At UC Davis he also researched social cognition by developing a virtual reality environment to support studies of speech and interpersonal gaze in Autism/ADHD. He is excited to be applying his background to infuse commercial products with conversational intelligence.

Selected articles

The Social-Emotional Turing Challenge

Social-emotional intelligence is an essential part of being a competent human and is thus required for human-level AI. When considering alternatives to the Turing Test

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An end-to-end dialog system for tv program discovery

In this paper, we present an end-to-end dialog system for TV program discovery that uniquely combines several technologies such as trainable relation extraction, belief tracking

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User initiated learning for adaptive interfaces

Intelligent user interfaces employ machine-learning to learn and adapt according to user peculiarities. In all these cases, the learning tasks are predefined and a machine-learning

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A speech-driven second screen application for TV program discovery

In this paper, we present a speech-driven second screen application for TV program discovery. We give an overview of the application and its architecture. We

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Depression-related impairments in prospective memory

Time-based prospective memory, the ability to carry out a future intention at a specified time, was found to be impaired in a community sample of

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