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.
Laura Froelich is a data scientist at DHI, where she’s dedicated to using data to discover patterns and underlying structures to enable optimization of businesses and processes, particularly through deep learning methods. Previously, she worked on a large variety of projects covering industries spanning life sciences to the energy industry at Teradata and was part of a research group investigating nonspecific effects of vaccines using survival analysis methods. Laura earned her 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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