Abstract: Near perfect language translation, better than human image labeling, Natural Language Understanding, dominating humans in strategy games, self-driving cars - What do all these achievements by machines have in common? Deep Learning – driven by significant improvements in Graphic Processing Units and complex 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 Internet behemoths but are now finding their way into the mainstream enterprise through open source tools and hardware offerings, benefiting from a steady decline in cost of building large, parallel models at scale, inspired by unmatched predictive accuracy in many application areas. In this session, we will discuss how Deep Learning technology can be integrated into mainstream enterprises to unlock significant business value and transform industries. In this talk we look at how Deep Learning is affecting the enterprise including use cases like smart navigation, fraud detection, mobile personalization based on individual behavior, face recognition for authentication and data center optimization. We dive deep into real-world experiences from a project at a large automotive conglomerate that collects and analyzes video data from dash mounted video cameras for the purpose of assisting drivers with navigation and safety. The data is analyzed in real-time and derived inference is immediately used as input into the in-car navigation system. The raw data is collected and sent to a central processing facility where training and testing of Deep Learning models is conducted. Analyzed in-car data is also made available to navigation data collectors as a paid service. The system detects cars, trucks, geographic features, pedestrians, shrubs, animals and debris to give insight and advanced warning to drivers. We discuss data collection, labeling, the use of a variety of state of the art models for training and deployment options, based on real world experience.
Bio: Ron is responsible for leading the global emerging technology team focusing on Artificial Intelligence, GPU and Blockchain. Responsible for leading global consulting teams for enterprise analytics architectures combining Hadoop and Spark, public cloud and traditional data warehousing, driving strategic pillar for Teradata. Previously, Ron was the founding CEO of Think Big Analytics. Think Big provides end to end support for enterprise Big Data including data science, data engineering, advisory and managed services and frameworks such as Kylo for enterprise data lakes. Think Big was acquired by Teradata in 2014 and was the leading global pure play big data services firm. Previously, Ron was VP Engineering at Quantcast where he led the data science and engineer teams that pioneered the use of Hadoop and NoSQL for batch and real-time decision making. Prior to that, Ron was Founder of New Aspects, which provided enterprise consulting for Aspect-oriented programming. Ron was also Co-Founder and CTO of B2B applications provider C-Bridge, which he led to team of 900 people and a successful IPO. Ron graduated with honors from McGill University with a B.S. in Math and Computer Science. Ron also earned his Master’s Degree in Computer Science from MIT, leaving the PhD program after presenting the idea for C-bridge and placing in the finals of the 50k Entrepreneurship Contest.