The Connected Data Podcast

What do graphs have to do with novel hardware architectures for AI workloads?

Graph processing is the key to unlocking new architectures, as much as new architectures can boost execution of graph-oriented workloads.

As machine learning-powered applications are proliferating, the workloads that are created in order to serve their requirements are taking up an ever increasing piece of the compute pie.

An IDC study found that Data Management, Application Development & Testing, and Data Analytics workloads represented more than half of all IaaS and PaaS spending already in 2018. IDC notes that this was driven in part by initial adoption of artificial intelligence and machine learning capabilities.

The rise of generative AI means that as adoption grows, data and AI workloads will dominate. This is why we see NVIDIA earnings skyrocket, as well as a renaissance of novel hardware architectures designed from the ground up to serve the needs of data and AI workloads.

More specifically for data analytics, understanding relationships among data points is a challenging but essential capability. Graph analytics has emerged as an approach by which analysts can efficiently examine the structure of the large networks and draw conclusions from the observed patterns. This is why DARPA set out to develop a graph analytics processor with the HIVE Project.

Furthermore, all machine learning models are best expressed as graphs. This is how machine learning libraries such as TensorFlow work. Efficient processing of graph-based networks involves large sparse data structures that consist of mostly zero values, and next generation architectures should avoid unnecessary processing.

This panel explores the interrelationship between graph processing and novel AI hardware architectures. Hosted by ZDNet's Tiernan Ray with panelists from some of the most groundbreaking AI hardware companies: Blaize, Determined AI / HPE, Graphcore, and SambaNova.

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Tiernan Ray. Contributing Writer, ZDNet

Tiernan Ray has been covering technology & business for 27 years. He was most recently technology editor for Barron's where he wrote daily market coverage for the Tech Trader blog and wrote the weekly print column of that name. He has also worked for Bloomberg, SmartMoney, and for the prestigious ComputerLetter newsletter covering venture capital investments in tech

Val G. Cook. Chief Software Architect, Blaize

Val G. Cook is Chief Software Architect at Blaize. An AI visionary and authority on the design of graphics and visual computing architectures, Val possesses two decades of experience in graphics and multimedia algorithms and software architecture. He is responsible for the Blaize Graph Streaming Processor software programming environment.

Carlo Luschi. Director of Research, Graphcore

Carlo is responsible for the study and development of algorithms for machine intelligence. Prior to Graphcore, Carlo was a Member of Technical Staff at Bell Labs Research, Lucent Technologies, and more recently Director of Algorithms and Standards at Icera Inc., which was acquired by NVIDIA in 2011.

Raghu Prabhakar. Software Engineer, SambaNova

Raghu Prabhakar is a senior principal engineer and one of the founding engineers at AI innovation platform SambaNova Systems. His research interests are in the areas of programming models, compilers, and hardware architecture for reconfigurable dataflow architectures.

Evan Sparks. Founder, Determined AI, an HPE Company

Evan Sparks, Vice President of Artificial Intelligence and High Performance Computing at HPE, co-founded Determined AI (now an HPE company). His group helps businesses get better AI-powered solutions to market faster and delivers the open source Determined Training Platform for large scale AI model development.

While mathematicians have used graph theory since the 18th century to solve problems, the software patterns for graph data are new to most developers. To enable "mass adoption" of graph technology, we need to establish the right abstractions, access APIs, and data models.

RDF triples, while of paramount importance in establishing RDF graph semantics, are a low-level abstraction, much like using assembly language. For practical and productive “graph programming” we need something different.

Similarly, existing declarative graph query languages (such as SPARQL and Cypher) are not always the best way to access graph data, and sometimes you need a simpler interface (e.g., GraphQL), or even a different approach altogether (e.g., imperative traversals such as with Gremlin).

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Ora Lassila is a Principal Graph Technologist in the Amazon Neptune graph database group. He has a long experience with graphs, graph databases, ontologies, and knowledge representation. He was a co-author of the original RDF specification as well as a co-author of the seminal article on the Semantic Web.

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Presentation slides available at https://www.slideshare.net/slideshows/graph-abstractions-matter-by-ora-lassila/266140641

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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

Taxonomies are the duct tape of connected data. They seem simple, flexible, and familiar. They are widely used. And they seem to work across many use cases and many domains. 

But when looked at in more detail, taxonomies turn out to be crude tools for knowledge organization that are very difficult to create, to scale, to adapt, to align, and to build on.

They don't work well for larger or more complex domains and use cases. Experienced talent and flexible tools for creating them are hard to find and to develop. Often taxonomies are built then abandoned for other, more robust approaches to knowledge organization.

It is essential to re-evaluate your connected data strategies in the context of alternative approaches to knowledge organization.

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Mike Dillinger. Technical Lead for Taxonomies and Ontologies, AI Division, LinkedIn

Mike Dillinger, PhD, focuses on teaching machine learning algorithms about the world of work at LinkedIn. Before that, he was Technical Lead for LinkedIn’s and eBay’s first machine translation systems, and an independent consultant specialized in deploying translation technologies for Fortune 500 companies.

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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

What is Connected Data, and how is it interesting from a market point of view?

Knowledge Graphs have reached peak Gartner hype. Graph data science and graph AI are the fastest growing areas in AI. Graph databases are the fastest growing category in enterprise software.

Add to this the historical foundations of graph algorithms and analytics and semantic technology, which have been invigorated and are seeing widespread adoption, and you get the burgeoning Connected Data landscape.

While there is ongoing technical innovation happening in the domain, how does this translate to market value and opportunities for investment?

How is this market defined, and what is driving its growth?

Join us as we define and explore this landscape, discuss technology and use cases, challenges and opportunities for growth and investment, and where the future may take us.

Join George Anadiotis, Panos Papadopoulos, Bob van Luijt and Konstantin Vinogradov from our Connected Data World 2021 panel discussion as they address the following:

Key Topics

• Defining the Connected Data technology and market landscape
• Exploring the Connected Data market
• Providing an outlook for the future

Target Audience

• Entrepreneurs
• Technical people with entrepreneurial spirit
• CxOs
• Decision makers
• Investors

Goals

• Define and explore the Connected Data landscape for people who are interested in it from a market perspective
• Answer questions that matter
• How is this market defined?
• What are some key drivers for growth?
• Where are the opportunities for investment?
• What is the outlook for the future?

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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

JSON is the de facto data format for developers today because it’s easy to use, but it’s not without its issues. JSON-LD builds on top of JSON, and has also been called "the gateway drug" for Linked Data.

Our panel of experts explores the many facets of JSON-LD and how it can facilitate enterprise data integration. Featuring Kurt Cagle, Freelance Technology Analyst, Brian Platz, co-founder and CEO of Fluree, Benjamin Young, Principal Architect at John Wiley and Sons and co-chair of the W3C JSON-LD Working Group. Moderated by George Anadiotis, Connected Data World Managing Director.

Article published on the Connected Data World blog.

Sponsored by Fluree. Fluree’s platform enables trusted, linked, and composable data, combining the ease of JSON documents with the power of linked data.

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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

Graph Analytics has long demonstrated that it solves real-world problems including Fraud, Ranking, Recommendation, text summarization and other NLP tasks.

More recently, Graph Machine Learning applied directly on graphs using graph algorithms and machine learning, has been demonstrating significant advantages in solving the same problems as graph analytics as well as problems that are impractical to solve using graph analytics. Graph Machine Learning does this by training statistical models on the graph resulting in Graph Embeddings and Graph Neural Networks that are used to complex problems in a different way.

Jörg Schad, ArangoDB CTO, compares and contrasts these two approaches (spoiler: often complexity vs precision) in real-world scenarios. What factors should you consider when choosing one over the other and when do you even have a choice? Learn about exciting new developments in Graph ML and the graph techniques on which they are based.

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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

Are your personal data, documents, files and messages all over the place?

Do you find yourself switching between applications, devices and files, unable to remember or find what you were looking for?

Would it make you feel better to know that it's not entirely your fault, and maybe there is a way out?

You know the stories about how the volume of data the world generates every day has gone through the roof. You know how most platforms want to lock you and your data in.

The volume and complexity of data each person has to manage today is comparable to what business owners and knowledge management professionals had to manage a few years ago.

What if each one of us could use the tools and practices professionals use to manage their data and build knowledge, while avoiding vendor lock-in?

A new generation of tools aiming to democratize access to knowledge management best practices and technology previously reserved for professional use is on the rise.

These tools, geared towards personal use, come in many shapes and forms. But they have one thing in common: they treat connections and context as first-class citizens, leveraging the graph paradigm.

Join us as we explore the rise of the Personal Knowledge Graph, and discuss use cases, tools, features and functionality, challenges and opportunities, and how to get started.

This panel will help define and explore the interplay between the most advanced technology for managing data and knowledge and user-oriented tools.

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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

The AI industry is now facing its next big challenge.

What are the necessary properties of representational structures that could allow vast amounts of data become meaningful in the human sense of the word?

How can knowledge architectures be constructed in a way that allows for both the efficiency and effectiveness of models they support?

In his Connected Data World 2021 keynote, Gadi Singer, VP & Director of Emergent AI at Intel Labs, discusses anthropomorphic conceptual structures and their benefits for enhancing Cognitive AI capabilities.

A visionary concept and a keynote which is even more timely today than it was then, foreseeing many current and, dare we say, future developments.

Singer introduces his model of the three levels of knowledge – Thrill-K – which can serve as a blueprint for building AI systems that are both efficient and scalable.

He begins with an Introduction to the Next Wave of AI. He addresses Language Models such as GPT-3, their shortcomings as Knowledge Models, and how they can be used in combination with Knowledge Graphs.

He then lists Five Essential Capabilities of Great Knowledge Models and describes the Thrill-K Architecture. Singer concludes by referring to The Future of AI, Cognitive AI and Deep Knowledge.

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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

Most Major Companies are Exploring or Using Knowledge Graphs.

Knowledge Graphs are at the top of the Garter AI Hype Cycle.

But Knowledge Graphs are much more than hype!

Knowledge graphs are a mature technology used in large scale deployments.

Anyone heard of Google, Facebook, Alibaba, or Uber?

Knowledge graphs address major weaknesses in traditional relational technology.

These weaknesses are major drivers for silos, the bane of every enterprise.

Knowledge Graphs are being deployed in a large variety of industries, including financial services, information technology, health care & life sciences, manufacturing and media.

Common use cases include data harmonization, search, recommendation, question answering, entity resolution, provenance, & security.

Join Ashleigh Faith, Katariina Kari, Michael Uschold and Mike Atkin from our Connected Data World 2021 panel discussion as they address the following:

Key Topics

• What are Knowledge Graphs good for?
• Supporting technologies
• How can I get started?
• What roadblocks should I watch out for?

Target Audience

• Chief Data Officers
• Data Scientists
• Data Modelers
• Technical Managers

Goals

• Understand where and how knowledge graphs can add value to your enterprise
• Know what supporting technologies are required for a typical knowledge graph deployment
• Know how to get started on a knowledge graph application in your enterprise.
• Understand what the current state of the art is and what is on the horizon.
• Be aware of possible roadblocks to avoid.

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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

Join us as we have a sneak peek through the Connected Data World 2021 program, and discuss the Connected Data landscape.

Our Program Committee members go through the 50+ sessions and 70+ speakers, and talk about:

• The Connected Data landscape
• Knowledge Graphs
• Graph Databases
• Graph Analytics
• Graph Data Science &
• Semantic Technology
• Topics, speakers and talks that piqued our interest
• Our own work in the domain and how it cross-cuts #CDW21
• Community chat and Ask Me Anything
• More in-depth topics as time permits:
• Hiring a team for building knowledge graphs: required roles and skills, what can be taught? 
• I want a knowledge graph! What next? The process of starting to build a knowledge graph for an organisation: assessment of need, use cases, support needed from management etc.
• Triple Store vs Labelled Property Graphs: It's not either-or, it's both and more!

With an all-star Program Committee and lineup, this will be a tour de force in Connected Data.

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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