10 Things You Need for AI-Enabled Knowledge Discovery
Uncovering insights and deep connections across your unstructured data using AI is challenging. You need to design for scalability and an appropriate level of sophistication at various stages in the data ingestion pipeline, as well as post-ingestion interactions with the corpora. This session will discuss the top 10 things, including the techniques that you would need to account for when designing AI-enabled discovery and exploration systems, to augment & assist knowledge workers to make good decisions. These include document cleansing/conversion, pre-processing, machine-learned entity extraction and resolution, efficient methods for indexing, knowledge graph construction, natural language queries, passage retrieval, relevancy training, relationship graphs and anomaly detection.
哲人Chandrasekaran
哲人Chandrasekaran
Managing Director & Head of Solution Architecture, Digital Solutions at KPMG
哲人Chandrasekaran (@swamichandra) is a managing director at KPMG’s AI Innovation & Enterprise Solutions. He leads the architecture, technology, creation of AI and emerging tech offerings as well as innovation efforts. He has led the creation of AI-based products and solutions that have solved a wide range of problems in areas such as tax and audit, industrial automation, aviation safety, contact centers, insurance claims, field service, multimedia enrichment, social care, digital marketing, M&A, and KYC. He is currently also driving explainable and trusted AI efforts.
Previously, he spent 12 years at IBM and was appointed as one of their most elite IBM Distinguished Engineers. Prior to IBM, he worked at Webify Solutions (acquired by IBM), BearingPoint, and Ericsson Research. He holds a master’s degree in electrical engineering from UT Arlington, has filed about 20 patents, and is an IBM Master Inventor. He is an avid video gamer, and when he finds the time, he writes onhttp://nirvacana.com.
