Presented By O'Reilly and Cloudera
Make Data Work
22–23 May 2017: Training
23–25 May 2017: Tutorials & Conference
London, UK

Enterprise artificial intelligence

Laura Froelich (Think Big Analytics, a Teradata Company)
16:3517:15 Wednesday, 24 May 2017
Data science and advanced analytics
Location: Capital Suite 7
Secondary topics:  AI, Deep learning
Level: Beginner
Average rating: ***..
(3.00, 3 ratings)

Who is this presentation for?

  • Data scientists and business leaders

What you'll learn

  • Understand how deep learning can (and will) be used in businesses

Description

Artificial intelligence has entered a renaissance thanks to rapid progress in domains as diverse as assisted driving systems in cars, intelligent virtual assistants, and game play. Underlying this progress is deep learning, driven by substantial improvements in GPUs and computational models inspired by the human brain that excel at capturing structures hidden in massive datasets. These techniques have been pioneered at research universities and digital giants, but, as open source tools and improved hardware become more widely available, mainstream enterprises are starting to apply them as well.

Laura Frolich explores applications of deep learning in companies, such as fraud detection, mobile personalization, predicting failures for the IoT, and text analysis to improve call center interactions—looking at practical examples of assessing the opportunity for AI, phased adoption, and lessons going from research to prototype to scaled production deployment—and discusses the future of enterprise AI.

Photo of Laura Froelich

Laura Froelich

Think Big Analytics, a Teradata Company

Laura Froelich is a data scientist at Think Big Analytics, a Teradata Company, where she is dedicated to utilizing data to discover patterns and underlying structure to enable optimization of businesses and processes, particularly through deep learning methods. Previously, she was part of a research group investigating nonspecific effects of vaccines using survival analysis methods. Laura holds a PhD from the Technical University of Denmark. For her dissertation, Decomposition and Classification of Electroencephalography Data, Laura used unsupervised decomposition and supervised classification methods to research brain activity and developed rigorous, interpretable approaches to classifying tensor data.

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