Python offers excellent libraries for working with graphs: semantic technologies, graph queries, interactive visualizations, graph algorithms, probabilistic graph inference, as well as embedding and other integrations with deep learning.

However, most of these approaches share little common ground, nor do many of them integrate effectively with popular data science tools (pandas, scikit-learn, spacy, pytorch), nor efficiently with popular data engineering infrastructure such as Spark, RAPIDS, Ray, Parquet, fsspect, etc.

In this podcast episode, Paco Nathan reviews kglab – an open source project that integrates most all of the above, and moreover provides ways to leverage disparate techniques in ways that complement each other, to produce Hybrid AI solutions for industry use cases.

Slides available: https://derwen.ai/s/kcgh

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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.

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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.

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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-manipulation
• Large-scale knowledge bases—likely leveraging innate frameworks—that incorporate symbolic knowledge along with other forms of knowledge
• Reasoning mechanisms capable of leveraging those knowledge bases in tractable ways
• And 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.

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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.

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