The conceptual foundation of OpenML dates all the way back to 2013 when a college boy in Physics major was reading and implementing the Matrix Numerov Method for Solving Schrödinger Equation

Galileo and Descartes are, no double of guess, monumental giants for their obvious reasons. For OpenML, it means the two just born the modern Determinism of AI.

In an agentic era where software engineering is mostly done automatically, OpenML realizes that coding by human does not create competitive values anymore. OpenML decides to up-grow by transitioning itself to business creator and down-introspect by deeply studying LLM internals - its math and its implementations, etc. This blog post focuses on the latter: down-introspect, specifically, Recurrent Neural Networks

I study Social Psychology in order to get insights about myself. A book on Social Psychology sparked my interests on this field so I picked up a text book about it. The notion of "Fundamental Attribution Error" and its application on World War II German society ignited my examination on my past 30 years of life as a Chinese.

In an agentic era where software engineering is mostly done automatically, OpenML realizes that coding by human does not create competitive values anymore. OpenML decides to up-grow by transitioning itself to business creator and down-introspect by deeply studying LLM internals - its math and its implementations, etc. This blog post focuses on the former: up-grow