DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical ...

A Snakemake workflow to run and benchmark structure learning (a.k.a. causal discovery) algorithms for probabilistic graphical models.

Well, thanks to a sharp-eyed Redditor, we’ve uncovered an Easter egg that links Gilmore Girls and The Sopranos in a way ...

LGBTIQA+ people need the same protections and freedoms as other Victorians.

Did Apple really fit a capacitor backward on the Mac LC III? A multimeter-wielding retro fan has confirmed that, yes – ...

We propose new evaluation metrics for world model recovery inspired by the classic Myhill-Nerode theorem from language theory. We illustrate their utility in three domains: game playing, logic puzzles ...

It works well for nearly all the major generative AI and large language model designs. First, here’s my prompt that invokes LoT. My entered prompt: “I want you to solve the following logic ...

they are revolutionizing our understanding of logic and comprise a major advancement in our understanding of human and machine reasoning and discourse. Large language models (LLMs), for example ...