The Connected Data Podcast Connected Data World

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


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 reasoningReasoning in industryGetting started with reasoningThe future of reasoning

Target Audience


DevelopersData EngineersTechnical LeadersManagementCxOsInvestors

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 & axiomsThe 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-deviceWhat performance can you expect from a system with reasoning?

Where do I start?


What do I need before I start reasoning?Who has the skills I need?My company is resistant, why should they change?

Reasoning a future


What does the future of reasoning look like?What are the challenges?How will we get there?

Format


A series of short presentations by reasoning experts, each followed by a discussion from the panel, coordinated by moderator.Audience interaction and questions are encouraged.2 hours running time.

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Panel By Haonan Qiu , Ian Horrocks , Ora Lassila , Marcus Nölke , Peter Crocker And Tara Raafat


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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


To keep up with updates in Knowledge Graphs, Graph Data Science and AI, Graph Databases and Semantic Technology subscribe for Connected Data London (CDL) blogs, newletters.


Meet over 1,000 industry professionals by registering to attend Connected Data London (CDL) held from 11-13 of December in London. The 2024 edition will be our finest and biggest event to date, featuring our tried and true recipe of bringing together leaders and innovators in Masterclasses, Keynotes, Presentations, Workshops and Panels, plus lots of new and exciting features such as Networking and Unconference sessions, a Gala Dinner and Speaker Lounge.

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.


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 just take this battle-hardened web tech and use it behind their firewall to connect their internal data.


In other words, the tools for this job already exist but enterprises are not yet using them internally. In this talk, we’ll share the hard won experience of how this was done for the world’s largest financial organizations.


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Tony Seale is a Knowledge Graph Architect at UBS. An experienced software architect and polyglot programmer with a proven track record of successfully delivering Knowledge Graphs into production for Tier 1 investment banks.


He has been exclusively focused on building decentralized Knowledge Graphs for the last ten years and has given talks, produced videos and written articles to promote the technology.


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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-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.


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 LearningDeep LearningGraph Data ManagementKnowledge GraphsGraph ML in Production

Target Audience


Machine Learning PractitionersData ScientistsData ModelersCxOsInvestors

Goals


Explore the interplay between machine learning and knowledge based technologiesHow 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?NLPBiomedIndustry (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:


IntroductionMeet and GreetSetting the stageKnowledge Graphs, meet Graph Machine LearningIt’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: natural language or structured query languages?How can machine learning help create and populate graphs (including KGs)?Cover some of the current state of the artAn edge - does it appear naturally or derived from node similarities?What kind of problems can we solve by using it?NLP, Biomed, Industry (say, adtech)How academic datasets align with real-world tasksWhere is this used in production?Success stories and business casesWhat are the major roadblocks / goals, how could we address them, and what would that enable?How to bridge the gap between business goals and Graph ML models?How do we measure the impact of applying ML models in real-world tasks? Metrics, A/B testing, generally about setting things in productionWho are some key players to keep an eye on?Both from industry and research

Panelists:


Mikhail Galkin. Researcher, Mila | McGill University


Dr. Tiffany Callahan. Researcher, University of Colorado, Anschutz Medical Campus


Andreea Deac. Researcher, Mila | Université de Montréal


Dr. Charles Hoyt. Researcher, Harvard Medical School, Laboratory of Systems Pharmacology


Sergei Ivanov. Research Scientist, Criteo AI Lab


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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

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.


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

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