AI Use Cases in Enterprises + Anomaly Detection using Deep Auto-Encoders

Agenda:

18:30: Doors opening

19:00 Tech talks

Enterprise Artificial Intelligence” by Laura Froelich, Think Big, A Teradata Company

Abstract: 

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 structured hidden in massive datasets. These techniques have been pioneered as 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.

This talk 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.

Bio: 
Laura Froelich is a data scientist at Think Big, where she is dedicated to utilizing data to discover patterns and underlying structure to enable optimization of businesses and processes. Previously, Laura 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 thesis, “Decomposition and Classification of Electroencephalography Data,” Laura used existing unsupervised methods and supervised classification techniques to understand brain activity through recordings of EEG and developed rigorous, interpretable classification methods for multidimensional (tensor) data.

“Anomaly detection using Deep Auto-Encoders”

Abstract:
One of the determinants for a good anomaly detector is finding smart data representations that can easily evince deviations from the normal distribution. Traditional supervised approaches would require a strong assumption about what is normal and what not plus a non negligible effort in labeling the training dataset. Deep auto-encoders work very well in learning high-level abstractions and non-linear relationships of the data without requiring data labels. In this talk we will review a few popular techniques used in shallow machine learning and propose two semi-supervised approaches for novelty detection: one based on reconstruction error and another based on lower-dimensional feature compression.

Bio:
Gianmario is a Senior Data Scientist at Pirelli Tyre, processing sensors telemetry data for IoT and connected vehicles applications. He works closely with tyre mechanics engineers and business units to analyse and formulate hybrid, physics-driven and data-driven, automotive models. His main expertise is on building machine learning systems and end-to-end solutions for data products. Co-author of Python Deep Learning book (Packt edition) and co-author of the Professional Manifesto for Data Science (datasciencemanifesto.org). He holds a Master’s Degree in Telematics (Polytechnic of Turin) and Software Engineering of Distributed Systems (KTH of Stockholm). Prior to Pirelli, he worked in Retail and Business Banking (Barclays), Cyber Security (Cisco), Predictive Marketing (AgilOne) and some occasional freelance work.

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About the host:

Think Big provides expert advisory and implementation services for open source big data solutions. As the first and only pure-play big data services firm, our data scientists and engineers are trusted advisors to the world’s most innovative companies. Our experienced teams combine a distinctive methodology and a proven Think Big Velocity framework that includes tested design patterns and pre-built components to help clients build applications faster.

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