How RelationalAI’s inbound TMS, built entirely within the AI coprocessor, tackles problems faced by retailers when shipping freight.
RelationalAI's declarative modeling language Rel can be a powerful tool for machine learning data preprocessing. It is concise, readable, and facilitates testing and debugging in development. Rel can significantly simplify your machine learning data pipeline.
In this series of blog posts, we show how to implement neighborhood-based, graph-based, content-based, and hybrid recommender systems using RelationalAI’s declarative modeling language, Rel.
The implementations of these algorithms demonstrate the efficiency of our Relational Knowledge Graph System (RKGS) for aggregating over paths on large and sparse graphs, computing similarities using our graph analytics library, and building compact, easy to read models, without needing to transfer data outside of our system.
In our previous blog post, we explained how to model traditional neighborhood-based recommender systems in Rel. In what follows, we focus on modeling graph-based recommender systems.
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.
Rel and the Relational Knowledge Graph 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.
Here at RelationalAI, we're passing out knowledge graphs to trick-or-treaters this year. 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!
All 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.
The 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.
Rel 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.
Rel 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.
Do 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.
Applications 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.
We 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.
We are excited to introduce the RelationalAI SDK for Julia with APIs for our Relational Knowledge Graph System (RKGS).
A Fortune 50 retailer drove $1 billion in incremental revenues over the last three years after deploying AI models developed by RelationalAl.
Demonstrating the details of weaving metadata into a knowledge graph to automate checking and maintenance of complex policy requirements.
Showing 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.
Business 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.
Many 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.
RelationalAI is the first and only Relational Knowledge Graph System that has the modern table stakes of a cloud-native architecture and has an expressive and complete declarative relational language.
The 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.
At JuliaCon 2021 Huda Nassar gives an overview of what we do at RelationalAI, and highlights how we"ve used Julia to develop our RKGS and modeling language Rel.
Modern databases can choose between two approaches to evaluating queries with high performance: Query Compilation compiles each query to optimized machine code, while Vectorization interprets queries using BLAS-style primitives.
RelationalAI is a cloud-based relational knowledge graph system, with state of the art probabilistic processing and declarative reasoning at scale to make developing Data Applications a superpower for your business.
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.
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