Deep learning. The term seems to be ubiquitous these days. Everywhere from self-driving cars and speech transcription to victories in the game “Go” and cancer diagnosis. If we measure things by press coverage, deep learning seems poised to make every other form of machine learning obsolete.
However, just because deep learning is having a lot of success with image recognition and self-driving cars does not necessarily mean that it translates into every domain with equal disruptive potential. Rather, that much depends on the nature of the task, the amount of data available, and the nature of that data. Dr. Pickens encourages everyone to think past the current hype and buzz, and understand that, like any tool, is not appropriate for every data problem.
About the Speaker:
Dr. Jeremy Pickens is one of the world’s leading information retrieval scientists and a pioneer in the field of collaborative exploratory search, a form of information seeking in which a group of people who share a common information need actively collaborate to achieve it. Dr. Pickens has seven patents and patents pending in the field of search and information retrieval. Dr. Pickens earned his doctoral degree at the University of Massachusetts, Amherst, Center for Intelligent Information Retrieval. He conducted his post-doctoral work at King’s College, London, and spent five years as a research scientist at FX Palo Alto Lab, Inc. His ongoing research and development at Catalyst focuses on methods for continuous learning, and the variety of real world technology assisted review workflows that are only possible with this approach. He continues to organize research workshops and speak at scientific conferences around the world.
Mike Benner, Darren McKellin, Co-Chairs
William Dallyn, Alisa Dicaprio, Tracy Greenwood, John Kirch, Vice Chairs
Eriko Asai, Board Liaison
ACCJ Information, Communications and Technology Committee
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