Boolean and Missing: What Are They Good For?
RelationalAI is built on knowledge graphs, which rarely use null and boolean values. And yet, Rel, RelationAI’s declarative modeling language, has a Missing data type to represent null values, and a Bool type to represent true and false boolean values. Let's explore the role null and boolean values play in a dataset and learn when to use Missing and Bool types in Rel.
Recursive Computations in Rel
We are excited to announce the latest enhancements to our Relational Knowledge Graph System (RKGS) that substantially improve the performance of certain recursive computations.
Declarative Quantum Computing
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.
Trends in Machine Learning: ICML 2022
The 38th International Conference on Machine Learning (ICML) took place in mid-July in hybrid mode, two months after the International Conference on Learning Representations (ICLR) 2022. In our analysis of the ICLR, we pointed out the spread of language models (LMs) and self-supervision. Things are not that different at ICML. Read our analysis.
Stop Wasting Good Data: Reclaim Predictive Information With Knowledge Graphs
As we move to data-centric AI, we demand higher quality data. Yet, the first step in creating predictive models odten involves tossing out predictive information. In this post, we'll look at how data scientists and ML engineers can use a relational knowledge graph to make domain expertise and contextual information available to ML models. Learn how to stop wasting the predictive data you already have.
Machine Learning in Consumer Credit: A Knowledge-Driven Approach to Mitigating Bias
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.