The Connected Data Podcast

Industry leaders from Accenture, Johnson & Johnson, and the Enterprise Knowledge Graph Foundation dive deep into the transformative potential of knowledge graphs, exploring how these semantic technologies are revolutionizing enterprise data management.  Featuring Mike Atkin, Laurent Alquier and Teresa Tung. The conversation reveals a critical shift from traditional data processing to a more nuanced, context-rich approach that prioritizes data meaning and reusability. Participants discuss how organizations are moving beyond experimental pilots to enterprise-wide implementations, driven by a growing recognition that data incongruence is a significant liability in today's data-driven business landscape. The discussion unveils the key challenges of knowledge graph adoption: * Overcoming organizational inertia * Bridging technological gaps, and  * Fundamentally changing mindsets about data representation.  Experts share insights into the importance of telling compelling stories about knowledge graphs, focusing on business value rather than technical complexity. They emphasize the need for incremental implementation, collaborative approaches, and the crucial role of knowledge engineers who can translate between technical capabilities and business needs. We've arrived at a pivotal moment for enterprise knowledge graphs: the technology has matured, business leaders are increasingly receptive, and there's a growing understanding that these semantic technologies offer more than just another IT solution.  Knowledge graphs represent a fundamental reimagining of how organizations can capture, understand, and leverage their data—moving away from the myth of a single version of truth towards a more flexible, context-rich approach that allows multiple perspectives to coexist. For businesses looking to remain competitive in a data-driven world, the message is clear: the time to start building knowledge graphs is now. -- Michael Atkin has over 30 years of experience as a strategic analyst to financial institutions, regulators and market authorities on the principles, practices and operational realities of data management. Dr Laurent Alquier's current role is to shape the architecture, design and development of J&J’s Knowledge Sharing ecosystem to further enable Emerging Technologies and Innovation management, Enterprise Architecture, and other IT strategic capabilities. Teresa Tung is a Managing Director at Accenture Labs responsible for taking the best-of-breed next-generation architecture solutions from industry, start-ups, and academia, and for evaluating their impact on Accenture's clients through building experimental prototypes and delivering pioneering pilot engagements. -- For more insightful content be sure to visit Connected Data London 2024 and purchase tickets Connected Data London 2024

Gary Marcus argues for a shift in research priorities, towards four cognitive prerequisites for building robust artificial intelligence: Hybrid architectures that combine large-scale learning with the representational and computational powers of symbol-manipulationLarge-scale knowledge bases—likely leveraging innate frameworks—that incorporate symbolic knowledge along with other forms of knowledgeReasoning mechanisms capable of leveraging those knowledge bases in tractable waysAnd rich cognitive models that work together with those mechanisms and knowledge bases. Although there are real problems to be solved here, and a great deal of effort must go into constraining symbolic search well enough to work in real time for complex problems, Google Knowledge Graph seems to be at least a partial counterexample to this objection, as do large scale recent successes in software and hardware verification. -- Gary Marcus is a scientist, best-selling author, and entrepreneur. He is Founder and CEO of Robust.AI, and was Founder and CEO of Geometric Intelligence, a machine learning company acquired by Uber in 2016. He is the author of five books, including The Algebraic Mind, Kluge, The Birth of the Mind, and The New York Times best seller Guitar Zero, as well as editor of The Future of the Brain and The Norton Psychology Reader. Gary has published extensively in fields ranging from human and animal behavior to neuroscience, genetics, linguistics, evolutionary psychology and artificial intelligence, often in leading journals such as Science and Nature, and is perhaps the youngest Professor Emeritus at NYU. His newest book, co-authored with Ernest Davis, Rebooting AI: Building Machines We Can Trust aims to shake up the field of artificial intelligence. -- For more insightful content be sure to visit Connected Data London 2024 and purchase tickets Connected Data London 2024

Join Omar Khan and David Newman as they canvas the Enterprise Knowledge Graph, and how you can apply it using its cornerstones of: Foundational building blocksInformation model expressivityMachine understandable representationsTranscending the relational modelHow an EKG expands on a graph and a knowledge graphProvides an infrastructure for Machine LearningContrasting an unlinked with linked data environmentQuestion and answering model emergenceSemantic similarity & embeddingFocused UI --- David Newman provides leadership and expertise for the advancement of knowledge graph solutions at Wells Fargo. His team develops innovations that employ key knowledge graph capabilities, including ontology models, semantic and property graph databases, graph analytics, knowledge graph embeddings and graph visualization techniques. David’s core mission is to actualize the potential of knowledge graph at Wells Fargo by creating a collaborative knowledge graph modeling community, developing enterprise standards and best practices, and creating operational pipelines for the ingestion, transformation and consumption of data using knowledge graphs. David’s initiatives include leveraging knowledge graph technology to fulfill business use cases by creating expressive enterprise and line of business ontologies, knowledge driven data asset catalogs, linked operational knowledge graphs and applying machine learning algorithms that train on knowledge graphs. David also chairs the Financial Industry Business Ontology (FIBO) initiative, a collaborative effort of global banks, financial regulators and vendors, under the auspices of the Enterprise Data Management Council (EDMC). Their goal is to semantically define a common language standard for finance using ontologies. Omar Khan is presently a member of Data Management & Insights, fostering Wells Fargo efforts and building applications as Technical Lead in Knowledge Graph & Semantic Technologies. Prior to his current role, Omar built novel solutions for the business during an 11-year tenure as a consultant and full-time employee within Brokerage Technology. While with Brokerage Technology, Omar helped to develop many key applications, and led efforts contributing to a majority of the IT portfolio in Wealth and Investment Management. A few years ago he became known for contributing to proof of concepts in areas unexplored, but necessary for future changes in direction for various lines of businesses. Omar successfully implemented game-changing software development ideas, and this helped form a foundation to allow me to join Innovation Group's R&D, and subsequently Data Management & Insights, specializing in Enterprise Knowledge Graph technologies. Emerging technology was and still is his specialty and passion. -- 👉 For more on Knowledge Graphs, Graph Data Science and AI, Graph Databases and Semantic Technology, join Connected Data London this December - Book Your Ticket Now

Graph representation learning has recently become one of the hottest topics in machine learning. One particular instance, graph neural networks, is being used in a broad spectrum of applications ranging from 3D computer vision and graphics to high energy physics and drug design. Despite the promise and a series of success stories of graph deep learning methods, we have not witnessed so far anything close to the smashing success convolutional networks have had in computer vision. In this Michael Bronstein outlines his views on the possible reasons and how the field could progress in the next few years. -- Michael Bronstein is a professor at Imperial College London, where he holds the Chair in Machine Learning and Pattern Recognition, and Head of Graph Learning Research at Twitter. He also heads ML research in Project CETI, a TED Audacious Prize-winning collaboration aimed at understanding the communication of sperm whales. -- 👉 For more Deep Learning on Knowledge Graphs, Graph Data Science and AI, Graph Databases and Semantic Technology, join Connected Data London this December - Book Your Ticket Now