RelationalAI: Building a Knowledge Graph Database with Julia | Nathan Daly and Molham Aref@POd of Asclepius

from Data & Science with Glen Wright Colopy · host podofasclepius

Molham Aref and Nathan Daly describe their experience using Julia to build a next-generation knowledge graph database that combines reasoning and learning to solve problems that have historically been intractable. They explain how Julia's unique features enabled them to build a high-performance database with less time and effort. Both Nathan and Molham with be speaking at JuliaCon 2020 at the end of July. It's free and online, so there's no reason not to attend. You can register for JuliaCon 2020 here: https://juliacon.org/2020/ 0:00 Intro 1:25 RelationalAI 3:25 Advantages of Julia as a foundation 4:21 "Full stack" data science 5:38 Advantages of Julia in the tech stack 6:30 Technical requirements of RelationalAI 7:45 Advantages of Julia (cont.) 10:00 Data munging, preprocessing, and transparency 14:30 Advantages of Julia (cont.) 18:35 RelationalAI's Innovation 22:00 Data Analysis and taking computational efficiency for granted 23:38 Who are the users of RelationalAI? 25:45 What are "knowledge graphs"? 28:30 Knowledge graphs for AI and Software 2.0 32:43 Julia as "executable math" 34:10 "Multiple dispatch" in a nutshell 36:20 Julia in the scientific community 38:53 See Nathan and Molham again at JuliaCon 2020

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This episode was published on July 21, 2020.

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