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.
Raw Strings and String Interpolation
Our declarative modeling language Rel has been expanded to include new string functionalities to better handle and manipulate string data.
Understanding Our World
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.
Is Database Administrator Still a Good Job?
The job of a database adminstrator is evolving, just as almost all careers in IT. DBAs are often the ones closest to the task of capturing the real world. This is arguably the base of the entire application stack. If you model is wrong, your data will be wrong, your predictions will be wrong, your decisions will be wrong. It all falls apart without the right model. It falls apart without those people who know what to capture and in what detail, what to connect and in what ways. That's the DBA.