Aether AI, founded by UCSD professor Biwei Huang, closed a $20 million seed round on June 18, 2026 to build causal world models that understand cause-and-effect relationships rather than statistical ...

We dedicate this short chapter to the DoWhy framework, which provides the structure for the causal methods explored later in Chapter 5–8 in this book. DoWhy includes a wide range of estimators — such ...

What if every decision you made left behind an echo – an imprint of your past actions, repeating endlessly? In Causal Loop, players don’t just solve puzzles—they navigate a fractured reality that is ...

What is this book about? Causal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that ...

Over a decade ago, when I was first starting to pretend I could write about quantum mechanics, I covered a truly bizarre experiment. One half of a pair of entangled photons was sent through a device ...

Celcomen leverages a mathematical causality framework to disentangle intra- and inter-cellular gene regulation programs in spatial transcriptomics data through a generative graph neural network. It is ...

Decades of research have established a significant link between physical activity and health, influencing agenda setting, policy making and community awareness.1–4 However, the field continues to ...

Abstract: Deep neural networks (DNNs) often struggle with out-of-distribution data, limiting their reliability in real-world visual applications. To address this issue, domain generalization methods ...

Animals survive in changing and unpredictable environments by not merely responding to new circumstances, but also, like humans, by forming inferences about their surroundings—for instance, squirrels ...

Forbes contributors publish independent expert analyses and insights. I write about the economics of AI. When OpenAI’s ChatGPT first exploded onto the scene in late 2022, it sparked a global obsession ...

As frontier models move into production, they're running up against major barriers like power caps, inference latency, and rising token-level costs, exposing the limits of traditional scale-first ...

This article is part of an ongoing series that explores how Causal Inference enhances Decision Intelligence by integrating concepts from multiple disciplines to improve decision-making efficiency.