Recommender systems are one of the most successful and widely used applications of machine learning. Their use cases span a range of industry sectors such as e-commerce, entertainment, and social media. In this post, we focus on a fundamental and effective classical approach to recommender systems, which is neighborhood-based.
Read MoreRel and the Relational Knowledge Graph Management System provide an excellent tool for investigating and analyzing seismic data. This project illustrates an example of working with data that is distributed geographically and temporally.
Read MoreHere at RelationalAI, we're passing out knowledge graphs to trick-or-treaters. Come and get spooked with us as we solve a Halloween logic puzzle in Rel, our relational modeling language. You'll see how to model a problem, store facts, and infer new knowledge from those facts. So grab your flashlight, put on your favorite costume, and let's go trick-or-treating with Rel!
Read MoreAll businesses regularly confront difficult decisions. Even when these decisions are constrained (perhaps by budgets) and the cost implications of the decisions are complex, optimization algorithms may nevertheless provide decisions that minimize cost and maximize benefit. Unfortunately, optimization can become very difficult as the number of decisions increases and this makes it an excellent use-case for quantum computing. Declarative languages like RelationalAI’s Rel make the integration of quantum optimizers simple.
Read MoreThe financial services sector was one of the first industries to widely adopt predictive modeling, starting with Bayesian statistics in the 1960s and evolving after the advent of neural networks to deep learning and beyond. Machine learning applications in this industry are endless, whether in auditing, fraud detection, credit scoring, or others.
Read MoreRel is not a procedural language. It is a relational declarative language with roots in logic programming. Rel goes far beyond a simple type system by allowing you to define the rules your creations have to obey. Definitions are not methods that turn input into output, but rather rules that can be evaluated in both directions.
Read MoreRel supports relations as arguments, recursion, variable arguments and inlined definitions that reference other inlined definitions. These features give you tremendous power so you can express your business logic clearly and concisely. Power that was typically reserved for procedural languages is now available in your models and queries.
Read MoreDo you remember playing with legos as a kid? This is what our query language Rel feels like. You don’t have to write 300 lines of SQL code and go absolutely insane trying to debug or handle it all in your head. You make progress with every little definition. You can divide and conquer the problem by building small pieces, combining them together and creating something that is greater than the sum of its parts.
Read MoreApplications implemented in imperative programming languages encode vast amounts of human knowledge at their core, but the essence of the application logic is often obscured by the imperative nature of the implementation language.
Read MoreWe show how the relational model can be successfully exploited to model complex analytics scenarios while enjoying the same characteristics of clarity and flexibility as when modeling the data themselves.
Read MoreA Fortune 50 retailer drove $1 billion in incremental revenues over the last three years after deploying AI models developed by RelationalAl.
Read MoreDemonstrating the details of weaving metadata into a knowledge graph to automate checking and maintenance of complex policy requirements.
Read MoreShowing how knowledge graphs support the construction of scalable models that mix discoverable with explicitly asserted metadata to afford reasoning and policy enforcement. In this first article, we show how explicitly asserted metadata in a knowledge graph enables automated reasoning.
Read MoreBusiness applications are traditionally complicated to understand and maintain because business logic tends to be scattered in separate tiers of growing code bases, while schema becomes rigid and brittle. The end result is that business logic dissolves in a soup of disparate application code.
Read MoreMany recognize the potential of artificial intelligence (AI) in healthcare and life sciences. Some early adopters are starting to reap the benefits already, especially in fields such as radiology and digital pathology.
Read MoreThe RAI relational KGMS combines Graph, Reasoning, and Learning in a single platform. Add cloud scalability, workload concurrency, and zero-copy cloning to get unique analytic flexibility.
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