The Connected Data Podcast

Afleveringen

1. Graph-Based Data Science: Hybrid AI meets data science process | Paco Nathan

   6 januari 2025 · 36 min

   Afspelen
   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. Lees meer

   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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2. Enterprise Knowledge Graphs: Breaking Through Organizational Inertia to Reimagine Data Management | Panel Discussion

   2 december 2024 · 39 min

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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. Lees meer

   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 stra…

3. Rebooting AI: Adding Knowledge to Deep Learning | Gary Marcus

   3 november 2024 · 39 min

   Afspelen
   Gary Marcus argues for a shift in research priorities, towards four cognitive prerequisites for building robust artificial intelligence: Lees meer

   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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   For more insightful content be sure to visit Connected Data London 2024 and purchase tickets Connected Data London 2024

4. The Enterprise Knowledge Graph | Omar Khan and David Newman

   7 oktober 2024 · 33 min

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   Join Omar Khan and David Newman as they canvas the Enterprise Knowledge Graph, and how you can apply it using its cornerstones of: Lees meer

   Foundational building blocks

   Information model expressivity

   Machine understandable representations

   Transcending the relational model

   How an EKG expands on a graph and a knowledge graph

   Provides an infrastructure for Machine Learning

   Contrasting an unlinked with linked data environment

   Question and answering model emergence

   Semantic similarity & embedding

   Focused UI

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   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 r…

5. Deep Learning on Graphs: Past, Present, And Future | Michael Bronstein

   2 september 2024 · 30 min

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   Graph representation learning has recently become one of the hottest topics in machine learning. Lees meer

   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.

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   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.

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   👉 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

6. Connected Data London 2024 Call for Submissions Roundtable | Panel Discussion

   5 augustus 2024 · 1 uur 27 min

   Afspelen
   Connected Data is coming back to London in 2024, on December 11-13. Lees meer

   Join us for a tour de force in all things Knowledge Graph, Graph Analytics / Al / Data Science / Databases and Semantic Technology.

   Call for submissions and volunteers, program committee, chairs, and initial lineup have been announced.

   This online roundtable highlights the Connected Data landscape and how it's reflected in our Call for Submissions, while it also goes over the event's format and answers audience questions.

   Key topics:

   How taxonomy, ontology and knowledge graphs can help GenAI and Large Language Models: Graph RAG and beyond

   The Knowledge Graph Development Lifecycle: Building, Consolidating and Managing Knowledge Graphs

   Featuring Connected Data Founders George Anadiotis and James Phare, Program chairs Amy Hodler and Paco Nathan, and Program Committee Members Panos Alexopoulos, Giuseppe Futia, Heather Hedden, Juan Sequeda, Ivo Velitchkov and Andrea Volpini.

   Call for submissions: https://www.connected-data.london/call-for-submissions

7. The Momentum Behind Semantic Reasoning and Why It’s Here to Stay | Panel Discussion

   1 juli 2024 · 1 uur 31 min

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   What does reasoning have to offer? How does it add so much value to data? Who is using it and why should I care? Lees meer

   All questions that we’re delighted to answer.

   Access to data has exploded over the last decade, but it leaves us asking what to make of it all? Often lacking quality, reasoning is required to enrich data by adding context and insights, serving up knowledge, not just numbers.

   This expert panel will explore the who, what, why, and how of reasoning: Its foundations, its advancements over the years, and its bright future.

   Google, Amazon and Facebook are just a few of the giants implementing reasoning today to great effect. With that said, this is not a tool exclusive to the Fortune 500—intelligence is buried in data everywhere, a valuable asset at any scale.

   Key Topics

   A technical introduction to reasoning

   Reasoning in industry

   Getting started with reasoning

   The future of reasoning

   Target Audience

   Developers

   Data Engineers

   Technical Leaders

   Management

   CxOs

   Investors

   Goals

   To introduce the concepts of reasoning & the theoretical foundations that support it.

   To explore the role of reasoning in industry—who, what, why, and how.

   To examine the opinions of sector leaders as to the future of reasoning.What is the current state of the art, how and where is it used in the wild?

   Session outline:

   Meet the panel

   An introduction to reasoning

   What separates a knowledge graph from a simple graph?

   What is semantic reasoning?

   Logical consequences, facts & axioms

   The reasoning standards and beyond

   Where is reasoning used in production?

   What kind of problems are being solved with reasoning?

   Who uses this technology?

   What is the current state of reasoning in industry?

   From data modelling to the technical stack

   What are your options for reasoning today?

   Where can reasoning be deployed? From Cloud to Edge to on-device

   What performance can you expect from a system with reasoning?

   Where do I start?

   What do I need before I start reasoning?

   Who …

8. Data Revolution: The Emergence of the Decentralized Enterprise Knowledge Graph | Tony Seale

   2 juni 2024 · 32 min

   Afspelen
   An AI tsunami is on the rise, and the past few months have only amplified it. To survive it and thrive in tomorrow’s economy, organizations big and small must rethink the way they do business. To do this, a radical shift in the way they work with their data is needed. And no, we don’t mean Big Data. Lees meer

   By now, most organizations have gotten their Big Data. And that is a problem. Not because we can’t accommodate Big Data, but because the more data you have, the harder it becomes to connect it and use it. We need to go beyond Big Data, towards Connected Data.

   We’ll show how enterprises can use decentralized Knowledge Graphs to vastly increase the connectivity of their data, drawing on hard won experience of architecting and successfully delivering innovative technical projects for the world’s largest financial organisations.

   Large enterprises that want to survive the AI tsunami must undergo a profound transformation in the way they think about their data. It starts by accepting that they need to link a large percentage of ALL their data together into a unified whole.

   Achieving this will require a radical rethink of some established ideas about enterprise data integration. The truth is meaningful data doesn't exist in isolation; everything is positioned within the context of everything else.

   That is why the future of data is graph shaped … but what are graphs and what is so great about them?

   Knowledge Graphs are a really powerful tool, but on their own, they are not enough to transform enterprise data integration. We also need to get our heads around the complex idea of decentralisation. In a decentralised data mesh, the responsibility for data integration is pushed down to the individual applications.

   Unbeknownst to most people, a third of all web pages now contain little islands of data that help the search engines build their knowledge graphs. Enterprises do not need to reinvent the wheel to build themselves a Decentralised Enterprise Knowledge Graph. They can …

9. Graph Machine Learning - Research and Industry Applications | Panel Discussion

   5 mei 2024 · 1 uur 36 min

   Afspelen
   Graph-based technologies became first-class citizens in various industries and many practical applications. Still, building performant and reliable machine learning pipelines over graph data, e.g., graph machine learning applications and products, remains a non-trivial task. Lees meer

   This panel discussion brings together academic and industrial experts from fields where Graph ML yields significant gains and greatly improves traditional processes. In addition to highlighting successful business cases, the panel concentrates on questions often dismissed or hidden behind the curtains of modern Graph ML applications.

   In particular, we will talk about the origins of graph data, its modeling, organization, and processing aspects; best communication interfaces; bridging a gap between products and ML algorithms as well as measuring their practical impact.

   On a higher level, the panel will discuss upcoming trends in industrial Graph ML and prospective disruptive applications.

   Key Topics

   Graph Machine Learning

   Deep Learning

   Graph Data Management

   Knowledge Graphs

   Graph ML in Production

   Target Audience

   Machine Learning Practitioners

   Data Scientists

   Data Modelers

   CxOs

   Investors

   Goals

   Explore the interplay between machine learning and knowledge based technologies

   How to get the “actionable” knowledge from the graph data?

   How can those approaches complement one another, and what would that unlock?

   What is the current state of the art, how and where is it used in the wild?

   NLP

   Biomed

   Industry (say, Ad Tech)

   What are the next milestones / roadblocks?

   Where are the opportunities for investment?

   We know drug discovery is on its highs, NLP is being democratized rapidly, what else?

   Session outline:

   Introduction

   Meet and Greet

   Setting the stage

   Knowledge Graphs, meet Graph Machine Learning

   It’s all about the data:

   How do you create, maintain, and process graphs?

   Databases or tabular sources?

   Do you consider data modeling aspects?

   Best communication interface: …

10. Systems that learn and reason | Frank Van Harmelen

    1 april 2024 · 28 min

    Afspelen
    After the amazing breakthroughs of machine learning (deep learning or otherwise) in the past decade, the shortcomings of machine learning are also becoming increasingly clear: unexplainable results, data hunger and limited generalisability are all becoming bottlenecks. Lees meer

    In this talk we will look at how the combination with symbolic AI (in the form of very large knowledge graphs) can give us a way forward, towards machine learning systems that can explain their results, that need less data, and that generalise better outside their training set.

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    Frank van Harmelen leads the Knowledge Representation & Reasoning group in the CS Department of the VU University Amsterdam. He is also Principal investigator of the Hybrid Intelligence Centre, a 20Μ€, 10 year collaboration between researchers at 6 Dutch universities into AI that collaborates with people instead of replacing them.

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    Slides available at: https://www.slideshare.net/slideshow/systems-that-learn-and-reason-frank-van-harmelen/267008886

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    Connected Data London 2024 has been announced!.

    December 11-13, etc Venues St. Paul’s, City of London

    Check #CDL24 for more Presentations, Keynotes, Masterclasses, and Workshops on cutting-edge topics from industry leaders and innovators: https://connected-data.london

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