15 July 2020
less than a minute read
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.
About a decade ago, Marko Rodriguez wrote a blog post on Loopy Lattices. It became infamous amongst graph database practitioners as it taught them a very important lesson: Never give in to the temptation to ask for all potential paths between two nodes.Read More
Most master data projects fail because we’ve relied on monolithic MDM platforms, and therefore centralized, highly federated approaches to creating master data. The MDM concept is strong. The benefits and uses of master data can provide value to those corporations who successfully implement it. However, we need to fundamentally change our approach to implementation.Read More
Despite early predictions that the deep learning hype would be ephemeral, we are happy to see the field still growing while delivering maturity in algorithms and architectures. ICLR 2022 was full of exciting papers. Here at RelationalAI we spent ~100 hours going through the content as we believe it will drive the commercialization of AI in the following years. We present what we found to be the most noteworthy ideas.Read More