Supervised causal learning has shown promise in causal discovery, yet it often struggles with generalization across diverse interventional settings, particularly when intervention targets are unknown.

The standard guidelines for building large language models (LLMs) optimize only for training costs and ignore inference costs. This poses a challenge for real-world applications that use ...

As artificial intelligence (AI) becomes widely used and new workflows scale token demand, questions about how much energy large language models (LLMs) consume are increasingly important for grid ...

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 ...

Your browser does not support the audio element. In the fast-paced world of B2C experimentation, speed is currency. Product managers and executives constantly monitor ...

ABSTRACT: Treatment-Resistant Depression (TRD) remains one of the most challenging subtypes of major depressive disorder, affecting approximately one-third of patients and leading to significant ...

Many nonprofits in low- and middle-income countries face a critical mismatch: urgent social problems demand rapid program iteration, yet organizations often wait years for externally-produced ...

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 ...

A/B testing is the gold standard of experimentation. It is meant to help companies make faster, better, data-driven decisions. But too often, it does the opposite. The meeting starts with optimism: a ...

Earlier this week, we learned that the first test screenings for Amazon MGM's live-action Masters of the Universe had taken place. Early word on the movie was positive, with the reboot described as ...