Leveraging AI on Data to Get Smarter Faster

The majority of industrial data is generated from thousands of diverse sensors. That data is little more than noise unless it is understood, which makes it “smart” data. The domain knowledge needed to understand data is found in many places: subject matter experts, database schemas, language, control systems and configuration databases.

Industry needs to deploy new ways – and at new scales – to create and collect metadata so that it can be better understood and then smartly integrated with other enterprise data generated by customers, sourcing, the supply chain and workers. With the consumer Internet, machine learning and search have developed systems to use metadata to better understand data. These approaches have lagged within the enterprise and especially within industrial companies.

We will present several unique technologies that we have developed to bring structured domain knowledge to data management in industry. We will also present a novel approach to organizing information and retrieval challenges in industrial applications so that, like search on the Internet, platforms can support the creation of value-adding applications.

Dr. Gustafson leads the Knowledge Discovery Lab at the General Electric Global Research Center in Niskayuna, New York. The Knowledge Discovery Lab is focused on large-scale data, semantics, ontologies and text mining, and pattern search and discovery. As a former member of the Machine Learning Lab and Computational Intelligence Lab, he develops and applies advanced AI and machine learning algorithms for complex problem solving. He received his PhD in computer science from the University of Nottingham, UK, where he was a research fellow in the Automated Scheduling, Optimisation and Planning Research Group. He received his BS and MS in computer science from Kansas State University, where he was a research assistant in the Knowledge Discovery in Databases Laboratory. Dr. Gustafson is a member of several program committees, several journal editorial boards, and a Technical Editor-in-Chief of the journal Memetic Computing. In 2006, he received the IEEE Intelligent System’s “AI’s 10 to Watch” award.

A hands-on technical Architect, Engineering Director, and CTO with more than 25 years of designing and delivering high-profile, large-scale software and services. Donald founded the Knowledge and Reasoning group within Bing, where he designed and delivered an internet-scale ingestion and analysis pipeline and benchmark-shattering "realtime," distributed graph (tuple) store with self-optimizing query engine. He co-founded a semantic computing incubation funded directly by Bill Gates and delivered into the SQL organization for productization. He created Microsoft's first contextual ad delivery system spanning multiple international data centers delivering well over 14 billion targeted ads per day and responsible for $700M in annual revenue. Donald served as Architect and Director of Engineering for numerous cutting-edge efforts while in Microsoft Research: new embedded operating system based on .NET, robotics, wearable computing, and privacy-preserving personal data services, analytics, and inference. Prior to Microsoft, Donald built an automated loan decisioning system, cellular billing system, and wrote AI and physics engines for several games.